Random regression models using different functions to model milk flow in dairy cows.

PubMed

Laureano, M M M; Bignardi, A B; El Faro, L; Cardoso, V L; Tonhati, H; Albuquerque, L G

2014-09-12

We analyzed 75,555 test-day milk flow records from 2175 primiparous Holstein cows that calved between 1997 and 2005. Milk flow was obtained by dividing the mean milk yield (kg) of the 3 daily milking by the total milking time (min) and was expressed as kg/min. Milk flow was grouped into 43 weekly classes. The analyses were performed using a single-trait Random Regression Models that included direct additive genetic, permanent environmental, and residual random effects. In addition, the contemporary group and linear and quadratic effects of cow age at calving were included as fixed effects. Fourth-order orthogonal Legendre polynomial of days in milk was used to model the mean trend in milk flow. The additive genetic and permanent environmental covariance functions were estimated using random regression Legendre polynomials and B-spline functions of days in milk. The model using a third-order Legendre polynomial for additive genetic effects and a sixth-order polynomial for permanent environmental effects, which contained 7 residual classes, proved to be the most adequate to describe variations in milk flow, and was also the most parsimonious. The heritability in milk flow estimated by the most parsimonious model was of moderate to high magnitude.

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages.

PubMed

Kim, Yoonsang; Choi, Young-Ku; Emery, Sherry

2013-08-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods' performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages-SAS GLIMMIX Laplace and SuperMix Gaussian quadrature-perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes.

Logistic Regression with Multiple Random Effects: A Simulation Study of Estimation Methods and Statistical Packages

PubMed Central

Kim, Yoonsang; Emery, Sherry

2013-01-01

Several statistical packages are capable of estimating generalized linear mixed models and these packages provide one or more of three estimation methods: penalized quasi-likelihood, Laplace, and Gauss-Hermite. Many studies have investigated these methods’ performance for the mixed-effects logistic regression model. However, the authors focused on models with one or two random effects and assumed a simple covariance structure between them, which may not be realistic. When there are multiple correlated random effects in a model, the computation becomes intensive, and often an algorithm fails to converge. Moreover, in our analysis of smoking status and exposure to anti-tobacco advertisements, we have observed that when a model included multiple random effects, parameter estimates varied considerably from one statistical package to another even when using the same estimation method. This article presents a comprehensive review of the advantages and disadvantages of each estimation method. In addition, we compare the performances of the three methods across statistical packages via simulation, which involves two- and three-level logistic regression models with at least three correlated random effects. We apply our findings to a real dataset. Our results suggest that two packages—SAS GLIMMIX Laplace and SuperMix Gaussian quadrature—perform well in terms of accuracy, precision, convergence rates, and computing speed. We also discuss the strengths and weaknesses of the two packages in regard to sample sizes. PMID:24288415

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models.

PubMed

Bignardi, A B; El Faro, L; Cardoso, V L; Machado, P F; Albuquerque, L G

2009-09-01

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Summer School Effects in a Randomized Field Trial

ERIC Educational Resources Information Center

Zvoch, Keith; Stevens, Joseph J.

2013-01-01

This field-based randomized trial examined the effect of assignment to and participation in summer school for two moderately at-risk samples of struggling readers. Application of multiple regression models to difference scores capturing the change in summer reading fluency revealed that kindergarten students randomly assigned to summer school…

Probability Theory Plus Noise: Descriptive Estimation and Inferential Judgment.

PubMed

Costello, Fintan; Watts, Paul

2018-01-01

We describe a computational model of two central aspects of people's probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people's reasoning follows standard frequentist probability theory, but it is subject to random noise. This random noise has a regressive effect in descriptive probability estimation, moving probability estimates away from normative probabilities and toward the center of the probability scale. This random noise has an anti-regressive effect in inferential judgement, however. These regressive and anti-regressive effects explain various reliable and systematic biases seen in people's descriptive probability estimation and inferential probability judgment. This model predicts that these contrary effects will tend to cancel out in tasks that involve both descriptive estimation and inferential judgement, leading to unbiased responses in those tasks. We test this model by applying it to one such task, described by Gallistel et al. ). Participants' median responses in this task were unbiased, agreeing with normative probability theory over the full range of responses. Our model captures the pattern of unbiased responses in this task, while simultaneously explaining systematic biases away from normatively correct probabilities seen in other tasks. Copyright © 2018 Cognitive Science Society, Inc.

Use of random regression to estimate genetic parameters of temperament across an age continuum in a crossbred cattle population.

PubMed

Littlejohn, B P; Riley, D G; Welsh, T H; Randel, R D; Willard, S T; Vann, R C

2018-05-12

The objective was to estimate genetic parameters of temperament in beef cattle across an age continuum. The population consisted predominantly of Brahman-British crossbred cattle. Temperament was quantified by: 1) pen score (PS), the reaction of a calf to a single experienced evaluator on a scale of 1 to 5 (1 = calm, 5 = excitable); 2) exit velocity (EV), the rate (m/sec) at which a calf traveled 1.83 m upon exiting a squeeze chute; and 3) temperament score (TS), the numerical average of PS and EV. Covariates included days of age and proportion of Bos indicus in the calf and dam. Random regression models included the fixed effects determined from the repeated measures models, except for calf age. Likelihood ratio tests were used to determine the most appropriate random structures. In repeated measures models, the proportion of Bos indicus in the calf was positively related with each calf temperament trait (0.41 ± 0.20, 0.85 ± 0.21, and 0.57 ± 0.18 for PS, EV, and TS, respectively; P < 0.01). There was an effect of contemporary group (combinations of season, year of birth, and management group) and dam age (P < 0.001) in all models. From repeated records analyses, estimates of heritability (h2) were 0.34 ± 0.04, 0.31 ± 0.04, and 0.39 ± 0.04, while estimates of permanent environmental variance as a proportion of the phenotypic variance (c2) were 0.30 ± 0.04, 0.31 ± 0.03, and 0.34 ± 0.04 for PS, EV, and TS, respectively. Quadratic additive genetic random regressions on Legendre polynomials of age were significant for all traits. Quadratic permanent environmental random regressions were significant for PS and TS, but linear permanent environmental random regressions were significant for EV. Random regression results suggested that these components change across the age dimension of these data. There appeared to be an increasing influence of permanent environmental effects and decreasing influence of additive genetic effects corresponding to increasing calf age for EV, and to a lesser extent for TS. Inherited temperament may be overcome by accumulating environmental stimuli with increases in age, especially after weaning.

[How to fit and interpret multilevel models using SPSS].

PubMed

Pardo, Antonio; Ruiz, Miguel A; San Martín, Rafael

2007-05-01

Hierarchic or multilevel models are used to analyse data when cases belong to known groups and sample units are selected both from the individual level and from the group level. In this work, the multilevel models most commonly discussed in the statistic literature are described, explaining how to fit these models using the SPSS program (any version as of the 11 th ) and how to interpret the outcomes of the analysis. Five particular models are described, fitted, and interpreted: (1) one-way analysis of variance with random effects, (2) regression analysis with means-as-outcomes, (3) one-way analysis of covariance with random effects, (4) regression analysis with random coefficients, and (5) regression analysis with means- and slopes-as-outcomes. All models are explained, trying to make them understandable to researchers in health and behaviour sciences.

Random regression analyses using B-spline functions to model growth of Nellore cattle.

PubMed

Boligon, A A; Mercadante, M E Z; Lôbo, R B; Baldi, F; Albuquerque, L G

2012-02-01

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Evaluating the Effectiveness of Developmental Mathematics by Embedding a Randomized Experiment within a Regression Discontinuity Design

ERIC Educational Resources Information Center

Moss, Brian G.; Yeaton, William H.; Lloyd, Jane E.

2014-01-01

Using a novel design approach, a randomized experiment (RE) was embedded within a regression discontinuity (RD) design (R-RE-D) to evaluate the impact of developmental mathematics at a large midwestern college ("n" = 2,122). Within a region of uncertainty near the cut-score, estimates of benefit from a prospective RE were closely…

Comparison of random regression models with Legendre polynomials and linear splines for production traits and somatic cell score of Canadian Holstein cows.

PubMed

Bohmanova, J; Miglior, F; Jamrozik, J; Misztal, I; Sullivan, P G

2008-09-01

A random regression model with both random and fixed regressions fitted by Legendre polynomials of order 4 was compared with 3 alternative models fitting linear splines with 4, 5, or 6 knots. The effects common for all models were a herd-test-date effect, fixed regressions on days in milk (DIM) nested within region-age-season of calving class, and random regressions for additive genetic and permanent environmental effects. Data were test-day milk, fat and protein yields, and SCS recorded from 5 to 365 DIM during the first 3 lactations of Canadian Holstein cows. A random sample of 50 herds consisting of 96,756 test-day records was generated to estimate variance components within a Bayesian framework via Gibbs sampling. Two sets of genetic evaluations were subsequently carried out to investigate performance of the 4 models. Models were compared by graphical inspection of variance functions, goodness of fit, error of prediction of breeding values, and stability of estimated breeding values. Models with splines gave lower estimates of variances at extremes of lactations than the model with Legendre polynomials. Differences among models in goodness of fit measured by percentages of squared bias, correlations between predicted and observed records, and residual variances were small. The deviance information criterion favored the spline model with 6 knots. Smaller error of prediction and higher stability of estimated breeding values were achieved by using spline models with 5 and 6 knots compared with the model with Legendre polynomials. In general, the spline model with 6 knots had the best overall performance based upon the considered model comparison criteria.

Solving large test-day models by iteration on data and preconditioned conjugate gradient.

PubMed

Lidauer, M; Strandén, I; Mäntysaari, E A; Pösö, J; Kettunen, A

1999-12-01

A preconditioned conjugate gradient method was implemented into an iteration on a program for data estimation of breeding values, and its convergence characteristics were studied. An algorithm was used as a reference in which one fixed effect was solved by Gauss-Seidel method, and other effects were solved by a second-order Jacobi method. Implementation of the preconditioned conjugate gradient required storing four vectors (size equal to number of unknowns in the mixed model equations) in random access memory and reading the data at each round of iteration. The preconditioner comprised diagonal blocks of the coefficient matrix. Comparison of algorithms was based on solutions of mixed model equations obtained by a single-trait animal model and a single-trait, random regression test-day model. Data sets for both models used milk yield records of primiparous Finnish dairy cows. Animal model data comprised 665,629 lactation milk yields and random regression test-day model data of 6,732,765 test-day milk yields. Both models included pedigree information of 1,099,622 animals. The animal model ¿random regression test-day model¿ required 122 ¿305¿ rounds of iteration to converge with the reference algorithm, but only 88 ¿149¿ were required with the preconditioned conjugate gradient. To solve the random regression test-day model with the preconditioned conjugate gradient required 237 megabytes of random access memory and took 14% of the computation time needed by the reference algorithm.

Empirical likelihood inference in randomized clinical trials.

PubMed

Zhang, Biao

2017-01-01

In individually randomized controlled trials, in addition to the primary outcome, information is often available on a number of covariates prior to randomization. This information is frequently utilized to undertake adjustment for baseline characteristics in order to increase precision of the estimation of average treatment effects; such adjustment is usually performed via covariate adjustment in outcome regression models. Although the use of covariate adjustment is widely seen as desirable for making treatment effect estimates more precise and the corresponding hypothesis tests more powerful, there are considerable concerns that objective inference in randomized clinical trials can potentially be compromised. In this paper, we study an empirical likelihood approach to covariate adjustment and propose two unbiased estimating functions that automatically decouple evaluation of average treatment effects from regression modeling of covariate-outcome relationships. The resulting empirical likelihood estimator of the average treatment effect is as efficient as the existing efficient adjusted estimators 1 when separate treatment-specific working regression models are correctly specified, yet are at least as efficient as the existing efficient adjusted estimators 1 for any given treatment-specific working regression models whether or not they coincide with the true treatment-specific covariate-outcome relationships. We present a simulation study to compare the finite sample performance of various methods along with some results on analysis of a data set from an HIV clinical trial. The simulation results indicate that the proposed empirical likelihood approach is more efficient and powerful than its competitors when the working covariate-outcome relationships by treatment status are misspecified.

Random regression models using Legendre orthogonal polynomials to evaluate the milk production of Alpine goats.

PubMed

Silva, F G; Torres, R A; Brito, L F; Euclydes, R F; Melo, A L P; Souza, N O; Ribeiro, J I; Rodrigues, M T

2013-12-11

The objective of this study was to identify the best random regression model using Legendre orthogonal polynomials to evaluate Alpine goats genetically and to estimate the parameters for test day milk yield. On the test day, we analyzed 20,710 records of milk yield of 667 goats from the Goat Sector of the Universidade Federal de Viçosa. The evaluated models had combinations of distinct fitting orders for polynomials (2-5), random genetic (1-7), and permanent environmental (1-7) fixed curves and a number of classes for residual variance (2, 4, 5, and 6). WOMBAT software was used for all genetic analyses. A random regression model using the best Legendre orthogonal polynomial for genetic evaluation of milk yield on the test day of Alpine goats considered a fixed curve of order 4, curve of genetic additive effects of order 2, curve of permanent environmental effects of order 7, and a minimum of 5 classes of residual variance because it was the most economical model among those that were equivalent to the complete model by the likelihood ratio test. Phenotypic variance and heritability were higher at the end of the lactation period, indicating that the length of lactation has more genetic components in relation to the production peak and persistence. It is very important that the evaluation utilizes the best combination of fixed, genetic additive and permanent environmental regressions, and number of classes of heterogeneous residual variance for genetic evaluation using random regression models, thereby enhancing the precision and accuracy of the estimates of parameters and prediction of genetic values.

Regression discontinuity was a valid design for dichotomous outcomes in three randomized trials.

PubMed

van Leeuwen, Nikki; Lingsma, Hester F; Mooijaart, Simon P; Nieboer, Daan; Trompet, Stella; Steyerberg, Ewout W

2018-06-01

Regression discontinuity (RD) is a quasi-experimental design that may provide valid estimates of treatment effects in case of continuous outcomes. We aimed to evaluate validity and precision in the RD design for dichotomous outcomes. We performed validation studies in three large randomized controlled trials (RCTs) (Corticosteroid Randomization After Significant Head injury [CRASH], the Global Utilization of Streptokinase and Tissue Plasminogen Activator for Occluded Coronary Arteries [GUSTO], and PROspective Study of Pravastatin in elderly individuals at risk of vascular disease [PROSPER]). To mimic the RD design, we selected patients above and below a cutoff (e.g., age 75 years) randomized to treatment and control, respectively. Adjusted logistic regression models using restricted cubic splines (RCS) and polynomials and local logistic regression models estimated the odds ratio (OR) for treatment, with 95% confidence intervals (CIs) to indicate precision. In CRASH, treatment increased mortality with OR 1.22 [95% CI 1.06-1.40] in the RCT. The RD estimates were 1.42 (0.94-2.16) and 1.13 (0.90-1.40) with RCS adjustment and local regression, respectively. In GUSTO, treatment reduced mortality (OR 0.83 [0.72-0.95]), with more extreme estimates in the RD analysis (OR 0.57 [0.35; 0.92] and 0.67 [0.51; 0.86]). In PROSPER, similar RCT and RD estimates were found, again with less precision in RD designs. We conclude that the RD design provides similar but substantially less precise treatment effect estimates compared with an RCT, with local regression being the preferred method of analysis. Copyright © 2018 Elsevier Inc. All rights reserved.

Genetic analysis of groups of mid-infrared predicted fatty acids in milk.

PubMed

Narayana, S G; Schenkel, F S; Fleming, A; Koeck, A; Malchiodi, F; Jamrozik, J; Johnston, J; Sargolzaei, M; Miglior, F

2017-06-01

The objective of this study was to investigate genetic variability of mid-infrared predicted fatty acid groups in Canadian Holstein cattle. Genetic parameters were estimated for 5 groups of fatty acids: short-chain (4 to 10 carbons), medium-chain (11 to 16 carbons), long-chain (17 to 22 carbons), saturated, and unsaturated fatty acids. The data set included 49,127 test-day records from 10,029 first-lactation Holstein cows in 810 herds. The random regression animal test-day model included days in milk, herd-test date, and age-season of calving (polynomial regression) as fixed effects, herd-year of calving, animal additive genetic effect, and permanent environment effects as random polynomial regressions, and random residual effect. Legendre polynomials of the third degree were selected for the fixed regression for age-season of calving effect and Legendre polynomials of the fourth degree were selected for the random regression for animal additive genetic, permanent environment, and herd-year effect. The average daily heritability over the lactation for the medium-chain fatty acid group (0.32) was higher than for the short-chain (0.24) and long-chain (0.23) fatty acid groups. The average daily heritability for the saturated fatty acid group (0.33) was greater than for the unsaturated fatty acid group (0.21). Estimated average daily genetic correlations were positive among all fatty acid groups and ranged from moderate to high (0.63-0.96). The genetic correlations illustrated similarities and differences in their origin and the makeup of the groupings based on chain length and saturation. These results provide evidence for the existence of genetic variation in mid-infrared predicted fatty acid groups, and the possibility of improving milk fatty acid profile through genetic selection in Canadian dairy cattle. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Learning accurate and interpretable models based on regularized random forests regression

PubMed Central

2014-01-01

Background Many biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. Methods In this study, we focus on regression problems for biological data where target outcomes are continuous. In general, models constructed from linear regression approaches are relatively easy to interpret. However, many practical biological applications are nonlinear in essence where we can hardly find a direct linear relationship between input and output. Nonlinear regression techniques can reveal nonlinear relationship of data, but are generally hard for human to interpret. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features. Results We tested the approach on some biological data sets. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression. Conclusion It demonstrates high potential in aiding prediction and interpretation of nonlinear relationships of the subject being studied. PMID:25350120

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

On the Bias-Amplifying Effect of Near Instruments in Observational Studies

ERIC Educational Resources Information Center

Steiner, Peter M.; Kim, Yongnam

2014-01-01

In contrast to randomized experiments, the estimation of unbiased treatment effects from observational data requires an analysis that conditions on all confounding covariates. Conditioning on covariates can be done via standard parametric regression techniques or nonparametric matching like propensity score (PS) matching. The regression or…

Technology diffusion in hospitals: a log odds random effects regression model.

PubMed

Blank, Jos L T; Valdmanis, Vivian G

2015-01-01

This study identifies the factors that affect the diffusion of hospital innovations. We apply a log odds random effects regression model on hospital micro data. We introduce the concept of clustering innovations and the application of a log odds random effects regression model to describe the diffusion of technologies. We distinguish a number of determinants, such as service, physician, and environmental, financial and organizational characteristics of the 60 Dutch hospitals in our sample. On the basis of this data set on Dutch general hospitals over the period 1995-2002, we conclude that there is a relation between a number of determinants and the diffusion of innovations underlining conclusions from earlier research. Positive effects were found on the basis of the size of the hospitals, competition and a hospital's commitment to innovation. It appears that if a policy is developed to further diffuse innovations, the external effects of demand and market competition need to be examined, which would de facto lead to an efficient use of technology. For the individual hospital, instituting an innovations office appears to be the most prudent course of action. © 2013 The Authors. International Journal of Health Planning and Management published by John Wiley & Sons, Ltd.

Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression.

PubMed

Chen, Carla Chia-Ming; Schwender, Holger; Keith, Jonathan; Nunkesser, Robin; Mengersen, Kerrie; Macrossan, Paula

2011-01-01

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

Using mixed treatment comparisons and meta-regression to perform indirect comparisons to estimate the efficacy of biologic treatments in rheumatoid arthritis.

PubMed

Nixon, R M; Bansback, N; Brennan, A

2007-03-15

Mixed treatment comparison (MTC) is a generalization of meta-analysis. Instead of the same treatment for a disease being tested in a number of studies, a number of different interventions are considered. Meta-regression is also a generalization of meta-analysis where an attempt is made to explain the heterogeneity between the treatment effects in the studies by regressing on study-level covariables. Our focus is where there are several different treatments considered in a number of randomized controlled trials in a specific disease, the same treatment can be applied in several arms within a study, and where differences in efficacy can be explained by differences in the study settings. We develop methods for simultaneously comparing several treatments and adjusting for study-level covariables by combining ideas from MTC and meta-regression. We use a case study from rheumatoid arthritis. We identified relevant trials of biologic verses standard therapy or placebo and extracted the doses, comparators and patient baseline characteristics. Efficacy is measured using the log odds ratio of achieving six-month ACR50 responder status. A random-effects meta-regression model is fitted which adjusts the log odds ratio for study-level prognostic factors. A different random-effect distribution on the log odds ratios is allowed for each different treatment. The odds ratio is found as a function of the prognostic factors for each treatment. The apparent differences in the randomized trials between tumour necrosis factor alpha (TNF- alpha) antagonists are explained by differences in prognostic factors and the analysis suggests that these drugs as a class are not different from each other. Copyright (c) 2006 John Wiley & Sons, Ltd.

Estimation of genetic parameters related to eggshell strength using random regression models.

PubMed

Guo, J; Ma, M; Qu, L; Shen, M; Dou, T; Wang, K

2015-01-01

This study examined the changes in eggshell strength and the genetic parameters related to this trait throughout a hen's laying life using random regression. The data were collected from a crossbred population between 2011 and 2014, where the eggshell strength was determined repeatedly for 2260 hens. Using random regression models (RRMs), several Legendre polynomials were employed to estimate the fixed, direct genetic and permanent environment effects. The residual effects were treated as independently distributed with heterogeneous variance for each test week. The direct genetic variance was included with second-order Legendre polynomials and the permanent environment with third-order Legendre polynomials. The heritability of eggshell strength ranged from 0.26 to 0.43, the repeatability ranged between 0.47 and 0.69, and the estimated genetic correlations between test weeks was high at > 0.67. The first eigenvalue of the genetic covariance matrix accounted for about 97% of the sum of all the eigenvalues. The flexibility and statistical power of RRM suggest that this model could be an effective method to improve eggshell quality and to reduce losses due to cracked eggs in a breeding plan.

Predicting longitudinal trajectories of health probabilities with random-effects multinomial logit regression.

PubMed

Liu, Xian; Engel, Charles C

2012-12-20

Researchers often encounter longitudinal health data characterized with three or more ordinal or nominal categories. Random-effects multinomial logit models are generally applied to account for potential lack of independence inherent in such clustered data. When parameter estimates are used to describe longitudinal processes, however, random effects, both between and within individuals, need to be retransformed for correctly predicting outcome probabilities. This study attempts to go beyond existing work by developing a retransformation method that derives longitudinal growth trajectories of unbiased health probabilities. We estimated variances of the predicted probabilities by using the delta method. Additionally, we transformed the covariates' regression coefficients on the multinomial logit function, not substantively meaningful, to the conditional effects on the predicted probabilities. The empirical illustration uses the longitudinal data from the Asset and Health Dynamics among the Oldest Old. Our analysis compared three sets of the predicted probabilities of three health states at six time points, obtained from, respectively, the retransformation method, the best linear unbiased prediction, and the fixed-effects approach. The results demonstrate that neglect of retransforming random errors in the random-effects multinomial logit model results in severely biased longitudinal trajectories of health probabilities as well as overestimated effects of covariates on the probabilities. Copyright © 2012 John Wiley & Sons, Ltd.

Accounting for center in the Early External Cephalic Version trials: an empirical comparison of statistical methods to adjust for center in a multicenter trial with binary outcomes.

PubMed

Reitsma, Angela; Chu, Rong; Thorpe, Julia; McDonald, Sarah; Thabane, Lehana; Hutton, Eileen

2014-09-26

Clustering of outcomes at centers involved in multicenter trials is a type of center effect. The Consolidated Standards of Reporting Trials Statement recommends that multicenter randomized controlled trials (RCTs) should account for center effects in their analysis, however most do not. The Early External Cephalic Version (EECV) trials published in 2003 and 2011 stratified by center at randomization, but did not account for center in the analyses, and due to the nature of the intervention and number of centers, may have been prone to center effects. Using data from the EECV trials, we undertook an empirical study to compare various statistical approaches to account for center effect while estimating the impact of external cephalic version timing (early or delayed) on the outcomes of cesarean section, preterm birth, and non-cephalic presentation at the time of birth. The data from the EECV pilot trial and the EECV2 trial were merged into one dataset. Fisher's exact method was used to test the overall effect of external cephalic version timing unadjusted for center effects. Seven statistical models that accounted for center effects were applied to the data. The models included: i) the Mantel-Haenszel test, ii) logistic regression with fixed center effect and fixed treatment effect, iii) center-size weighted and iv) un-weighted logistic regression with fixed center effect and fixed treatment-by-center interaction, iv) logistic regression with random center effect and fixed treatment effect, v) logistic regression with random center effect and random treatment-by-center interaction, and vi) generalized estimating equations. For each of the three outcomes of interest approaches to account for center effect did not alter the overall findings of the trial. The results were similar for the majority of the methods used to adjust for center, illustrating the robustness of the findings. Despite literature that suggests center effect can change the estimate of effect in multicenter trials, this empirical study does not show a difference in the outcomes of the EECV trials when accounting for center effect. The EECV2 trial was registered on 30 July 30 2005 with Current Controlled Trials: ISRCTN 56498577.

Estimating Unbiased Treatment Effects in Education Using a Regression Discontinuity Design

ERIC Educational Resources Information Center

Smith, William C.

2014-01-01

The ability of regression discontinuity (RD) designs to provide an unbiased treatment effect while overcoming the ethical concerns plagued by Random Control Trials (RCTs) make it a valuable and useful approach in education evaluation. RD is the only explicitly recognized quasi-experimental approach identified by the Institute of Education…

Regression Discontinuity Designs in Epidemiology

PubMed Central

Moscoe, Ellen; Mutevedzi, Portia; Newell, Marie-Louise; Bärnighausen, Till

2014-01-01

When patients receive an intervention based on whether they score below or above some threshold value on a continuously measured random variable, the intervention will be randomly assigned for patients close to the threshold. The regression discontinuity design exploits this fact to estimate causal treatment effects. In spite of its recent proliferation in economics, the regression discontinuity design has not been widely adopted in epidemiology. We describe regression discontinuity, its implementation, and the assumptions required for causal inference. We show that regression discontinuity is generalizable to the survival and nonlinear models that are mainstays of epidemiologic analysis. We then present an application of regression discontinuity to the much-debated epidemiologic question of when to start HIV patients on antiretroviral therapy. Using data from a large South African cohort (2007–2011), we estimate the causal effect of early versus deferred treatment eligibility on mortality. Patients whose first CD4 count was just below the 200 cells/μL CD4 count threshold had a 35% lower hazard of death (hazard ratio = 0.65 [95% confidence interval = 0.45–0.94]) than patients presenting with CD4 counts just above the threshold. We close by discussing the strengths and limitations of regression discontinuity designs for epidemiology. PMID:25061922

The Bayesian group lasso for confounded spatial data

USGS Publications Warehouse

Hefley, Trevor J.; Hooten, Mevin B.; Hanks, Ephraim M.; Russell, Robin E.; Walsh, Daniel P.

2017-01-01

Generalized linear mixed models for spatial processes are widely used in applied statistics. In many applications of the spatial generalized linear mixed model (SGLMM), the goal is to obtain inference about regression coefficients while achieving optimal predictive ability. When implementing the SGLMM, multicollinearity among covariates and the spatial random effects can make computation challenging and influence inference. We present a Bayesian group lasso prior with a single tuning parameter that can be chosen to optimize predictive ability of the SGLMM and jointly regularize the regression coefficients and spatial random effect. We implement the group lasso SGLMM using efficient Markov chain Monte Carlo (MCMC) algorithms and demonstrate how multicollinearity among covariates and the spatial random effect can be monitored as a derived quantity. To test our method, we compared several parameterizations of the SGLMM using simulated data and two examples from plant ecology and disease ecology. In all examples, problematic levels multicollinearity occurred and influenced sampling efficiency and inference. We found that the group lasso prior resulted in roughly twice the effective sample size for MCMC samples of regression coefficients and can have higher and less variable predictive accuracy based on out-of-sample data when compared to the standard SGLMM.

Comparison of random regression test-day models for Polish Black and White cattle.

PubMed

Strabel, T; Szyda, J; Ptak, E; Jamrozik, J

2005-10-01

Test-day milk yields of first-lactation Black and White cows were used to select the model for routine genetic evaluation of dairy cattle in Poland. The population of Polish Black and White cows is characterized by small herd size, low level of production, and relatively early peak of lactation. Several random regression models for first-lactation milk yield were initially compared using the "percentage of squared bias" criterion and the correlations between true and predicted breeding values. Models with random herd-test-date effects, fixed age-season and herd-year curves, and random additive genetic and permanent environmental curves (Legendre polynomials of different orders were used for all regressions) were chosen for further studies. Additional comparisons included analyses of the residuals and shapes of variance curves in days in milk. The low production level and early peak of lactation of the breed required the use of Legendre polynomials of order 5 to describe age-season lactation curves. For the other curves, Legendre polynomials of order 3 satisfactorily described daily milk yield variation. Fitting third-order polynomials for the permanent environmental effect made it possible to adequately account for heterogeneous residual variance at different stages of lactation.

Random regression models on Legendre polynomials to estimate genetic parameters for weights from birth to adult age in Canchim cattle.

PubMed

Baldi, F; Albuquerque, L G; Alencar, M M

2010-08-01

The objective of this work was to estimate covariance functions for direct and maternal genetic effects, animal and maternal permanent environmental effects, and subsequently, to derive relevant genetic parameters for growth traits in Canchim cattle. Data comprised 49,011 weight records on 2435 females from birth to adult age. The model of analysis included fixed effects of contemporary groups (year and month of birth and at weighing) and age of dam as quadratic covariable. Mean trends were taken into account by a cubic regression on orthogonal polynomials of animal age. Residual variances were allowed to vary and were modelled by a step function with 1, 4 or 11 classes based on animal's age. The model fitting four classes of residual variances was the best. A total of 12 random regression models from second to seventh order were used to model direct and maternal genetic effects, animal and maternal permanent environmental effects. The model with direct and maternal genetic effects, animal and maternal permanent environmental effects fitted by quadric, cubic, quintic and linear Legendre polynomials, respectively, was the most adequate to describe the covariance structure of the data. Estimates of direct and maternal heritability obtained by multi-trait (seven traits) and random regression models were very similar. Selection for higher weight at any age, especially after weaning, will produce an increase in mature cow weight. The possibility to modify the growth curve in Canchim cattle to obtain animals with rapid growth at early ages and moderate to low mature cow weight is limited.

Does higher education protect against obesity? Evidence using Mendelian randomization.

PubMed

Böckerman, Petri; Viinikainen, Jutta; Pulkki-Råback, Laura; Hakulinen, Christian; Pitkänen, Niina; Lehtimäki, Terho; Pehkonen, Jaakko; Raitakari, Olli T

2017-08-01

The aim of this explorative study was to examine the effect of education on obesity using Mendelian randomization. Participants (N=2011) were from the on-going nationally representative Young Finns Study (YFS) that began in 1980 when six cohorts (aged 30, 33, 36, 39, 42 and 45 in 2007) were recruited. The average value of BMI (kg/m 2 ) measurements in 2007 and 2011 and genetic information were linked to comprehensive register-based information on the years of education in 2007. We first used a linear regression (Ordinary Least Squares, OLS) to estimate the relationship between education and BMI. To identify a causal relationship, we exploited Mendelian randomization and used a genetic score as an instrument for education. The genetic score was based on 74 genetic variants that genome-wide association studies (GWASs) have found to be associated with the years of education. Because the genotypes are randomly assigned at conception, the instrument causes exogenous variation in the years of education and thus enables identification of causal effects. The years of education in 2007 were associated with lower BMI in 2007/2011 (regression coefficient (b)=-0.22; 95% Confidence Intervals [CI]=-0.29, -0.14) according to the linear regression results. The results based on Mendelian randomization suggests that there may be a negative causal effect of education on BMI (b=-0.84; 95% CI=-1.77, 0.09). The findings indicate that education could be a protective factor against obesity in advanced countries. Copyright © 2017 Elsevier Inc. All rights reserved.

Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes.

PubMed

Borquis, Rusbel Raul Aspilcueta; Neto, Francisco Ribeiro de Araujo; Baldi, Fernando; Hurtado-Lugo, Naudin; de Camargo, Gregório M F; Muñoz-Berrocal, Milthon; Tonhati, Humberto

2013-09-01

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The regression discontinuity design showed to be a valid alternative to a randomized controlled trial for estimating treatment effects.

PubMed

Maas, Iris L; Nolte, Sandra; Walter, Otto B; Berger, Thomas; Hautzinger, Martin; Hohagen, Fritz; Lutz, Wolfgang; Meyer, Björn; Schröder, Johanna; Späth, Christina; Klein, Jan Philipp; Moritz, Steffen; Rose, Matthias

2017-02-01

To compare treatment effect estimates obtained from a regression discontinuity (RD) design with results from an actual randomized controlled trial (RCT). Data from an RCT (EVIDENT), which studied the effect of an Internet intervention on depressive symptoms measured with the Patient Health Questionnaire (PHQ-9), were used to perform an RD analysis, in which treatment allocation was determined by a cutoff value at baseline (PHQ-9 = 10). A linear regression model was fitted to the data, selecting participants above the cutoff who had received the intervention (n = 317) and control participants below the cutoff (n = 187). Outcome was PHQ-9 sum score 12 weeks after baseline. Robustness of the effect estimate was studied; the estimate was compared with the RCT treatment effect. The final regression model showed a regression coefficient of -2.29 [95% confidence interval (CI): -3.72 to -.85] compared with a treatment effect found in the RCT of -1.57 (95% CI: -2.07 to -1.07). Although the estimates obtained from two designs are not equal, their confidence intervals overlap, suggesting that an RD design can be a valid alternative for RCTs. This finding is particularly important for situations where an RCT may not be feasible or ethical as is often the case in clinical research settings. Copyright © 2016 Elsevier Inc. All rights reserved.

A Bayesian ridge regression analysis of congestion's impact on urban expressway safety.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Lee, Jaeyoung

2016-03-01

With the rapid growth of traffic in urban areas, concerns about congestion and traffic safety have been heightened. This study leveraged both Automatic Vehicle Identification (AVI) system and Microwave Vehicle Detection System (MVDS) installed on an expressway in Central Florida to explore how congestion impacts the crash occurrence in urban areas. Multiple congestion measures from the two systems were developed. To ensure more precise estimates of the congestion's effects, the traffic data were aggregated into peak and non-peak hours. Multicollinearity among traffic parameters was examined. The results showed the presence of multicollinearity especially during peak hours. As a response, ridge regression was introduced to cope with this issue. Poisson models with uncorrelated random effects, correlated random effects, and both correlated random effects and random parameters were constructed within the Bayesian framework. It was proven that correlated random effects could significantly enhance model performance. The random parameters model has similar goodness-of-fit compared with the model with only correlated random effects. However, by accounting for the unobserved heterogeneity, more variables were found to be significantly related to crash frequency. The models indicated that congestion increased crash frequency during peak hours while during non-peak hours it was not a major crash contributing factor. Using the random parameter model, the three congestion measures were compared. It was found that all congestion indicators had similar effects while Congestion Index (CI) derived from MVDS data was a better congestion indicator for safety analysis. Also, analyses showed that the segments with higher congestion intensity could not only increase property damage only (PDO) crashes, but also more severe crashes. In addition, the issues regarding the necessity to incorporate specific congestion indicator for congestion's effects on safety and to take care of the multicollinearity between explanatory variables were also discussed. By including a specific congestion indicator, the model performance significantly improved. When comparing models with and without ridge regression, the magnitude of the coefficients was altered in the existence of multicollinearity. These conclusions suggest that the use of appropriate congestion measure and consideration of multicolilnearity among the variables would improve the models and our understanding about the effects of congestion on traffic safety. Copyright © 2015 Elsevier Ltd. All rights reserved.

Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes.

PubMed

Li, Baoyue; Lingsma, Hester F; Steyerberg, Ewout W; Lesaffre, Emmanuel

2011-05-23

Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models. We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC.Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted. The packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient. On relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen "non-informative" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain.

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

PubMed

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Statistical methods for efficient design of community surveys of response to noise: Random coefficients regression models

NASA Technical Reports Server (NTRS)

Tomberlin, T. J.

1985-01-01

Research studies of residents' responses to noise consist of interviews with samples of individuals who are drawn from a number of different compact study areas. The statistical techniques developed provide a basis for those sample design decisions. These techniques are suitable for a wide range of sample survey applications. A sample may consist of a random sample of residents selected from a sample of compact study areas, or in a more complex design, of a sample of residents selected from a sample of larger areas (e.g., cities). The techniques may be applied to estimates of the effects on annoyance of noise level, numbers of noise events, the time-of-day of the events, ambient noise levels, or other factors. Methods are provided for determining, in advance, how accurately these effects can be estimated for different sample sizes and study designs. Using a simple cost function, they also provide for optimum allocation of the sample across the stages of the design for estimating these effects. These techniques are developed via a regression model in which the regression coefficients are assumed to be random, with components of variance associated with the various stages of a multi-stage sample design.

Logistic regression of family data from retrospective study designs.

PubMed

Whittemore, Alice S; Halpern, Jerry

2003-11-01

We wish to study the effects of genetic and environmental factors on disease risk, using data from families ascertained because they contain multiple cases of the disease. To do so, we must account for the way participants were ascertained, and for within-family correlations in both disease occurrences and covariates. We model the joint probability distribution of the covariates of ascertained family members, given family disease occurrence and pedigree structure. We describe two such covariate models: the random effects model and the marginal model. Both models assume a logistic form for the distribution of one person's covariates that involves a vector beta of regression parameters. The components of beta in the two models have different interpretations, and they differ in magnitude when the covariates are correlated within families. We describe ascertainment assumptions needed to estimate consistently the parameters beta(RE) in the random effects model and the parameters beta(M) in the marginal model. Under the ascertainment assumptions for the random effects model, we show that conditional logistic regression (CLR) of matched family data gives a consistent estimate beta(RE) for beta(RE) and a consistent estimate for the covariance matrix of beta(RE). Under the ascertainment assumptions for the marginal model, we show that unconditional logistic regression (ULR) gives a consistent estimate for beta(M), and we give a consistent estimator for its covariance matrix. The random effects/CLR approach is simple to use and to interpret, but it can use data only from families containing both affected and unaffected members. The marginal/ULR approach uses data from all individuals, but its variance estimates require special computations. A C program to compute these variance estimates is available at http://www.stanford.edu/dept/HRP/epidemiology. We illustrate these pros and cons by application to data on the effects of parity on ovarian cancer risk in mother/daughter pairs, and use simulations to study the performance of the estimates. Copyright 2003 Wiley-Liss, Inc.

Effectiveness of preventive home visits in reducing the risk of falls in old age: a randomized controlled trial

PubMed Central

Luck, Tobias; Motzek, Tom; Luppa, Melanie; Matschinger, Herbert; Fleischer, Steffen; Sesselmann, Yves; Roling, Gudrun; Beutner, Katrin; König, Hans-Helmut; Behrens, Johann; Riedel-Heller, Steffi G

2013-01-01

Background Falls in older people are a major public health issue, but the underlying causes are complex. We sought to evaluate the effectiveness of preventive home visits as a multifactorial, individualized strategy to reduce falls in community-dwelling older people. Methods Data were derived from a prospective randomized controlled trial with follow-up examination after 18 months. Two hundred and thirty participants (≥80 years of age) with functional impairment were randomized to intervention and control groups. The intervention group received up to three preventive home visits including risk assessment, home counseling intervention, and a booster session. The control group received no preventive home visits. Structured interviews at baseline and follow-up provided information concerning falls in both study groups. Random-effects Poisson regression evaluated the effect of preventive home visits on the number of falls controlling for covariates. Results Random-effects Poisson regression showed a significant increase in the number of falls between baseline and follow-up in the control group (incidence rate ratio 1.96) and a significant decrease in the intervention group (incidence rate ratio 0.63) controlling for age, sex, family status, level of care, and impairment in activities of daily living. Conclusion Our results indicate that a preventive home visiting program can be effective in reducing falls in community-dwelling older people. PMID:23788832

Robust, Adaptive Functional Regression in Functional Mixed Model Framework.

PubMed

Zhu, Hongxiao; Brown, Philip J; Morris, Jeffrey S

2011-09-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets.

Robust, Adaptive Functional Regression in Functional Mixed Model Framework

PubMed Central

Zhu, Hongxiao; Brown, Philip J.; Morris, Jeffrey S.

2012-01-01

Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying curves and outlying regions of curves, so is not robust. In this paper, we introduce a new Bayesian method, robust functional mixed models (R-FMM), for performing robust functional regression within the general functional mixed model framework, which includes multiple continuous or categorical predictors and random effect functions accommodating potential between-function correlation induced by the experimental design. The underlying model involves a hierarchical scale mixture model for the fixed effects, random effect and residual error functions. These modeling assumptions across curves result in robust nonparametric estimators of the fixed and random effect functions which down-weight outlying curves and regions of curves, and produce statistics that can be used to flag global and local outliers. These assumptions also lead to distributions across wavelet coefficients that have outstanding sparsity and adaptive shrinkage properties, with great flexibility for the data to determine the sparsity and the heaviness of the tails. Together with the down-weighting of outliers, these within-curve properties lead to fixed and random effect function estimates that appear in our simulations to be remarkably adaptive in their ability to remove spurious features yet retain true features of the functions. We have developed general code to implement this fully Bayesian method that is automatic, requiring the user to only provide the functional data and design matrices. It is efficient enough to handle large data sets, and yields posterior samples of all model parameters that can be used to perform desired Bayesian estimation and inference. Although we present details for a specific implementation of the R-FMM using specific distributional choices in the hierarchical model, 1D functions, and wavelet transforms, the method can be applied more generally using other heavy-tailed distributions, higher dimensional functions (e.g. images), and using other invertible transformations as alternatives to wavelets. PMID:22308015

Impact of Contextual Factors on Prostate Cancer Risk and Outcomes

DTIC Science & Technology

2013-07-01

framework with random effects (“frailty models”) while the case-control analyses (Aim 4) will use multilevel unconditional logistic regression models...contextual-level SES on prostate cancer risk within racial/ethnic groups. The survival analyses (Aims 1-3) will utilize a proportional hazards regression

Longitudinal analysis of the strengths and difficulties questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression.

PubMed

Tzavidis, Nikos; Salvati, Nicola; Schmid, Timo; Flouri, Eirini; Midouhas, Emily

2016-02-01

Multilevel modelling is a popular approach for longitudinal data analysis. Statistical models conventionally target a parameter at the centre of a distribution. However, when the distribution of the data is asymmetric, modelling other location parameters, e.g. percentiles, may be more informative. We present a new approach, M -quantile random-effects regression, for modelling multilevel data. The proposed method is used for modelling location parameters of the distribution of the strengths and difficulties questionnaire scores of children in England who participate in the Millennium Cohort Study. Quantile mixed models are also considered. The analyses offer insights to child psychologists about the differential effects of risk factors on children's outcomes.

Zinc supplementation for the prevention of acute lower respiratory infection in children in developing countries: meta-analysis and meta-regression of randomized trials.

PubMed

Roth, Daniel E; Richard, Stephanie A; Black, Robert E

2010-06-01

Routine zinc supplementation is a potential intervention for the prevention of acute lower respiratory infection (ALRI) in developing countries. However, discrepant findings from recent randomized trials remain unexplained. Randomized trials of zinc supplementation in young children in developing countries were identified by a systematic literature review. Trials included in the meta-analysis met specific criteria, including participants <5 years of age, daily/weekly zinc and control supplementation for greater than 3 months, active household surveillance for respiratory morbidity and use of a case definition that included at least one sign of lower respiratory tract illness. ALRI case definitions were classified on the basis of specificity/severity. Incidence rate ratios (IRRs) were pooled by random-effects models. Meta-regression and sub-group analysis were performed to assess potential sources of between-study heterogeneity. Ten trials were eligible for inclusion (n = 49 450 children randomized). Zinc reduced the incidence of ALRI defined by specific clinical criteria [IRR 0.65, 95% confidence interval (CI) 0.52-0.82], but had no effect on lower-specificity ALRI case definitions based on caregiver report (IRR 1.01, 95% CI 0.91-1.12) or World Health Organization 'non-severe pneumonia' (0.96, 95% CI 0.86-1.08). By meta-regression, the effect of zinc was associated with ALRI case definition, but not with mean baseline age, geographic location, nutritional status or zinc dose. Routine zinc supplementation reduced the incidence of childhood ALRI defined by relatively specific clinical criteria, but the effect was null if lower specificity case definitions were applied. The choice of ALRI case definition may substantially influence inferences from community trials regarding the efficacy of preventive interventions.

Estimating Individual Influences of Behavioral Intentions: An Application of Random-Effects Modeling to the Theory of Reasoned Action.

ERIC Educational Resources Information Center

Hedeker, Donald; And Others

1996-01-01

Methods are proposed and described for estimating the degree to which relations among variables vary at the individual level. As an example, M. Fishbein and I. Ajzen's theory of reasoned action is examined. This article illustrates the use of empirical Bayes methods based on a random-effects regression model to estimate individual influences…

Revisiting Fixed- and Random-Effects Models: Some Considerations for Policy-Relevant Education Research

ERIC Educational Resources Information Center

Clarke, Paul; Crawford, Claire; Steele, Fiona; Vignoles, Anna

2015-01-01

The use of fixed (FE) and random effects (RE) in two-level hierarchical linear regression is discussed in the context of education research. We compare the robustness of FE models with the modelling flexibility and potential efficiency of those from RE models. We argue that the two should be seen as complementary approaches. We then compare both…

A general framework for the use of logistic regression models in meta-analysis.

PubMed

Simmonds, Mark C; Higgins, Julian Pt

2016-12-01

Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.

Randomization at the level of primary care practice: use of pre-intervention data and random effects models.

PubMed

Nixon, R M; Duffy, S W; Fender, G R; Day, N E; Prevost, T C

2001-06-30

The Anglia menorrhagia education study tests the effectiveness of an education package for the treatment of menorrhagia given to doctors at a primary care level. General practices were randomized to receive or not receive the package. It is hoped that this intervention will reduce the proportion of women suffering from menorrhagia that are referred to hospital. Data are available on the treatment and referral of women in the practices in the education and control groups, both pre- and post-intervention. We define and demonstrate a random effects logistic regression model that includes pre-intervention data for calculating the effectiveness of the intervention. Copyright 2001 John Wiley & Sons, Ltd.

Logistic random effects regression models: a comparison of statistical packages for binary and ordinal outcomes

PubMed Central

2011-01-01

Background Logistic random effects models are a popular tool to analyze multilevel also called hierarchical data with a binary or ordinal outcome. Here, we aim to compare different statistical software implementations of these models. Methods We used individual patient data from 8509 patients in 231 centers with moderate and severe Traumatic Brain Injury (TBI) enrolled in eight Randomized Controlled Trials (RCTs) and three observational studies. We fitted logistic random effects regression models with the 5-point Glasgow Outcome Scale (GOS) as outcome, both dichotomized as well as ordinal, with center and/or trial as random effects, and as covariates age, motor score, pupil reactivity or trial. We then compared the implementations of frequentist and Bayesian methods to estimate the fixed and random effects. Frequentist approaches included R (lme4), Stata (GLLAMM), SAS (GLIMMIX and NLMIXED), MLwiN ([R]IGLS) and MIXOR, Bayesian approaches included WinBUGS, MLwiN (MCMC), R package MCMCglmm and SAS experimental procedure MCMC. Three data sets (the full data set and two sub-datasets) were analysed using basically two logistic random effects models with either one random effect for the center or two random effects for center and trial. For the ordinal outcome in the full data set also a proportional odds model with a random center effect was fitted. Results The packages gave similar parameter estimates for both the fixed and random effects and for the binary (and ordinal) models for the main study and when based on a relatively large number of level-1 (patient level) data compared to the number of level-2 (hospital level) data. However, when based on relatively sparse data set, i.e. when the numbers of level-1 and level-2 data units were about the same, the frequentist and Bayesian approaches showed somewhat different results. The software implementations differ considerably in flexibility, computation time, and usability. There are also differences in the availability of additional tools for model evaluation, such as diagnostic plots. The experimental SAS (version 9.2) procedure MCMC appeared to be inefficient. Conclusions On relatively large data sets, the different software implementations of logistic random effects regression models produced similar results. Thus, for a large data set there seems to be no explicit preference (of course if there is no preference from a philosophical point of view) for either a frequentist or Bayesian approach (if based on vague priors). The choice for a particular implementation may largely depend on the desired flexibility, and the usability of the package. For small data sets the random effects variances are difficult to estimate. In the frequentist approaches the MLE of this variance was often estimated zero with a standard error that is either zero or could not be determined, while for Bayesian methods the estimates could depend on the chosen "non-informative" prior of the variance parameter. The starting value for the variance parameter may be also critical for the convergence of the Markov chain. PMID:21605357

Heritability estimations for inner muscular fat in Hereford cattle using random regressions

USDA-ARS?s Scientific Manuscript database

Random regressions make possible to make genetic predictions and parameters estimation across a gradient of environments, allowing a more accurate and beneficial use of animals as breeders in specific environments. The objective of this study was to use random regression models to estimate heritabil...

Random regression models for the prediction of days to weight, ultrasound rib eye area, and ultrasound back fat depth in beef cattle.

PubMed

Speidel, S E; Peel, R K; Crews, D H; Enns, R M

2016-02-01

Genetic evaluation research designed to reduce the required days to a specified end point has received very little attention in pertinent scientific literature, given that its economic importance was first discussed in 1957. There are many production scenarios in today's beef industry, making a prediction for the required number of days to a single end point a suboptimal option. Random regression is an attractive alternative to calculate days to weight (DTW), days to ultrasound back fat (DTUBF), and days to ultrasound rib eye area (DTUREA) genetic predictions that could overcome weaknesses of a single end point prediction. The objective of this study was to develop random regression approaches for the prediction of the DTW, DTUREA, and DTUBF. Data were obtained from the Agriculture and Agri-Food Canada Research Centre, Lethbridge, AB, Canada. Data consisted of records on 1,324 feedlot cattle spanning 1999 to 2007. Individual animals averaged 5.77 observations with weights, ultrasound rib eye area (UREA), ultrasound back fat depth (UBF), and ages ranging from 293 to 863 kg, 73.39 to 129.54 cm, 1.53 to 30.47 mm, and 276 to 519 d, respectively. Random regression models using Legendre polynomials were used to regress age of the individual on weight, UREA, and UBF. Fixed effects in the model included an overall fixed regression of age on end point (weight, UREA, and UBF) nested within breed to account for the mean relationship between age and weight as well as a contemporary group effect consisting of breed of the animal (Angus, Charolais, and Charolais sired), feedlot pen, and year of measure. Likelihood ratio tests were used to determine the appropriate random polynomial order. Use of the quadratic polynomial did not account for any additional genetic variation in days for DTW ( > 0.11), for DTUREA ( > 0.18), and for DTUBF ( > 0.20) when compared with the linear random polynomial. Heritability estimates from the linear random regression for DTW ranged from 0.54 to 0.74, corresponding to end points of 293 and 863 kg, respectively. Heritability for DTUREA ranged from 0.51 to 0.34 and for DTUBF ranged from 0.55 to 0.37. These estimates correspond to UREA end points of 35 and 125 cm and UBF end points of 1.53 and 30 mm, respectively. This range of heritability shows DTW, DTUREA, and DTUBF to be highly heritable and indicates that selection pressure aimed at reducing the number of days to reach a finish weight end point can result in genetic change given sufficient data.

LiDAR based prediction of forest biomass using hierarchical models with spatially varying coefficients

USGS Publications Warehouse

Babcock, Chad; Finley, Andrew O.; Bradford, John B.; Kolka, Randall K.; Birdsey, Richard A.; Ryan, Michael G.

2015-01-01

Many studies and production inventory systems have shown the utility of coupling covariates derived from Light Detection and Ranging (LiDAR) data with forest variables measured on georeferenced inventory plots through regression models. The objective of this study was to propose and assess the use of a Bayesian hierarchical modeling framework that accommodates both residual spatial dependence and non-stationarity of model covariates through the introduction of spatial random effects. We explored this objective using four forest inventory datasets that are part of the North American Carbon Program, each comprising point-referenced measures of above-ground forest biomass and discrete LiDAR. For each dataset, we considered at least five regression model specifications of varying complexity. Models were assessed based on goodness of fit criteria and predictive performance using a 10-fold cross-validation procedure. Results showed that the addition of spatial random effects to the regression model intercept improved fit and predictive performance in the presence of substantial residual spatial dependence. Additionally, in some cases, allowing either some or all regression slope parameters to vary spatially, via the addition of spatial random effects, further improved model fit and predictive performance. In other instances, models showed improved fit but decreased predictive performance—indicating over-fitting and underscoring the need for cross-validation to assess predictive ability. The proposed Bayesian modeling framework provided access to pixel-level posterior predictive distributions that were useful for uncertainty mapping, diagnosing spatial extrapolation issues, revealing missing model covariates, and discovering locally significant parameters.

Neither fixed nor random: weighted least squares meta-analysis.

PubMed

Stanley, T D; Doucouliagos, Hristos

2015-06-15

This study challenges two core conventional meta-analysis methods: fixed effect and random effects. We show how and explain why an unrestricted weighted least squares estimator is superior to conventional random-effects meta-analysis when there is publication (or small-sample) bias and better than a fixed-effect weighted average if there is heterogeneity. Statistical theory and simulations of effect sizes, log odds ratios and regression coefficients demonstrate that this unrestricted weighted least squares estimator provides satisfactory estimates and confidence intervals that are comparable to random effects when there is no publication (or small-sample) bias and identical to fixed-effect meta-analysis when there is no heterogeneity. When there is publication selection bias, the unrestricted weighted least squares approach dominates random effects; when there is excess heterogeneity, it is clearly superior to fixed-effect meta-analysis. In practical applications, an unrestricted weighted least squares weighted average will often provide superior estimates to both conventional fixed and random effects. Copyright © 2015 John Wiley & Sons, Ltd.

Pennies for Milk: Using a First-Hand Descent into Randomness to Illustrate the Risks of Regression to the Mean for Marketing Researchers and Managers

ERIC Educational Resources Information Center

Posavac, Steven S.; Posavac, Emil J.

2017-01-01

The authors describe the Pennies for Milk exercise, a participative classroom experience in which students generate a regression to the mean effect within the context of simulated household milk purchases. Regression to the mean is a ubiquitous threat for marketing researchers and managers but is often hard for students to understand. The Pennies…

Simple, Efficient Estimators of Treatment Effects in Randomized Trials Using Generalized Linear Models to Leverage Baseline Variables

PubMed Central

Rosenblum, Michael; van der Laan, Mark J.

2010-01-01

Models, such as logistic regression and Poisson regression models, are often used to estimate treatment effects in randomized trials. These models leverage information in variables collected before randomization, in order to obtain more precise estimates of treatment effects. However, there is the danger that model misspecification will lead to bias. We show that certain easy to compute, model-based estimators are asymptotically unbiased even when the working model used is arbitrarily misspecified. Furthermore, these estimators are locally efficient. As a special case of our main result, we consider a simple Poisson working model containing only main terms; in this case, we prove the maximum likelihood estimate of the coefficient corresponding to the treatment variable is an asymptotically unbiased estimator of the marginal log rate ratio, even when the working model is arbitrarily misspecified. This is the log-linear analog of ANCOVA for linear models. Our results demonstrate one application of targeted maximum likelihood estimation. PMID:20628636

Bayesian random-effect model for predicting outcome fraught with heterogeneity--an illustration with episodes of 44 patients with intractable epilepsy.

PubMed

Yen, A M-F; Liou, H-H; Lin, H-L; Chen, T H-H

2006-01-01

The study aimed to develop a predictive model to deal with data fraught with heterogeneity that cannot be explained by sampling variation or measured covariates. The random-effect Poisson regression model was first proposed to deal with over-dispersion for data fraught with heterogeneity after making allowance for measured covariates. Bayesian acyclic graphic model in conjunction with Markov Chain Monte Carlo (MCMC) technique was then applied to estimate the parameters of both relevant covariates and random effect. Predictive distribution was then generated to compare the predicted with the observed for the Bayesian model with and without random effect. Data from repeated measurement of episodes among 44 patients with intractable epilepsy were used as an illustration. The application of Poisson regression without taking heterogeneity into account to epilepsy data yielded a large value of heterogeneity (heterogeneity factor = 17.90, deviance = 1485, degree of freedom (df) = 83). After taking the random effect into account, the value of heterogeneity factor was greatly reduced (heterogeneity factor = 0.52, deviance = 42.5, df = 81). The Pearson chi2 for the comparison between the expected seizure frequencies and the observed ones at two and three months of the model with and without random effect were 34.27 (p = 1.00) and 1799.90 (p < 0.0001), respectively. The Bayesian acyclic model using the MCMC method was demonstrated to have great potential for disease prediction while data show over-dispersion attributed either to correlated property or to subject-to-subject variability.

Randomized Controlled Studies and Alternative Designs in Outcome Studies: Challenges and Opportunities

ERIC Educational Resources Information Center

Shadish, William R.

2011-01-01

This article reviews several decades of the author's meta-analytic and experimental research on the conditions under which nonrandomized experiments can approximate the results from randomized experiments (REs). Several studies make clear that we can expect accurate effect estimates from the regression discontinuity design, though its statistical…

Effect of motivational interviewing on rates of early childhood caries: a randomized trial.

PubMed

Harrison, Rosamund; Benton, Tonya; Everson-Stewart, Siobhan; Weinstein, Phil

2007-01-01

The purposes of this randomized controlled trial were to: (1) test motivational interviewing (MI) to prevent early childhood caries; and (2) use Poisson regression for data analysis. A total of 240 South Asian children 6 to 18 months old were enrolled and randomly assigned to either the MI or control condition. Children had a dental exam, and their mothers completed pretested instruments at baseline and 1 and 2 years postintervention. Other covariates that might explain outcomes over and above treatment differences were modeled using Poisson regression. Hazard ratios were produced. Analyses included all participants whenever possible. Poisson regression supported a protective effect of MI (hazard ratio [HR]=0.54 (95%CI=035-0.84)-that is, the M/ group had about a 46% lower rate of dmfs at 2 years than did control children. Similar treatment effect estimates were obtained from models that included, as alternative outcomes, ds, dms, and dmfs, including "white spot lesions." Exploratory analyses revealed that rates of dmfs were higher in children whose mothers had: (1) prechewed their food; (2) been raised in a rural environment; and (3) a higher family income (P<.05). A motivational interviewing-style intervention shows promise to promote preventive behaviors in mothers of young children at high risk for caries.

Genetic analyses of partial egg production in Japanese quail using multi-trait random regression models.

PubMed

Karami, K; Zerehdaran, S; Barzanooni, B; Lotfi, E

2017-12-01

1. The aim of the present study was to estimate genetic parameters for average egg weight (EW) and egg number (EN) at different ages in Japanese quail using multi-trait random regression (MTRR) models. 2. A total of 8534 records from 900 quail, hatched between 2014 and 2015, were used in the study. Average weekly egg weights and egg numbers were measured from second until sixth week of egg production. 3. Nine random regression models were compared to identify the best order of the Legendre polynomials (LP). The most optimal model was identified by the Bayesian Information Criterion. A model with second order of LP for fixed effects, second order of LP for additive genetic effects and third order of LP for permanent environmental effects (MTRR23) was found to be the best. 4. According to the MTRR23 model, direct heritability for EW increased from 0.26 in the second week to 0.53 in the sixth week of egg production, whereas the ratio of permanent environment to phenotypic variance decreased from 0.48 to 0.1. Direct heritability for EN was low, whereas the ratio of permanent environment to phenotypic variance decreased from 0.57 to 0.15 during the production period. 5. For each trait, estimated genetic correlations among weeks of egg production were high (from 0.85 to 0.98). Genetic correlations between EW and EN were low and negative for the first two weeks, but they were low and positive for the rest of the egg production period. 6. In conclusion, random regression models can be used effectively for analysing egg production traits in Japanese quail. Response to selection for increased egg weight would be higher at older ages because of its higher heritability and such a breeding program would have no negative genetic impact on egg production.

Regression Discontinuity Design in Gifted and Talented Education Research

ERIC Educational Resources Information Center

Matthews, Michael S.; Peters, Scott J.; Housand, Angela M.

2012-01-01

This Methodological Brief introduces the reader to the regression discontinuity design (RDD), which is a method that when used correctly can yield estimates of research treatment effects that are equivalent to those obtained through randomized control trials and can therefore be used to infer causality. However, RDD does not require the random…

An INAR(1) Negative Multinomial Regression Model for Longitudinal Count Data.

ERIC Educational Resources Information Center

Bockenholt, Ulf

1999-01-01

Discusses a regression model for the analysis of longitudinal count data in a panel study by adapting an integer-valued first-order autoregressive (INAR(1)) Poisson process to represent time-dependent correlation between counts. Derives a new negative multinomial distribution by combining INAR(1) representation with a random effects approach.…

A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs

ERIC Educational Resources Information Center

Karabatsos, George; Walker, Stephen G.

2013-01-01

The regression discontinuity (RD) design (Thistlewaite & Campbell, 1960; Cook, 2008) provides a framework to identify and estimate causal effects from a non-randomized design. Each subject of a RD design is assigned to the treatment (versus assignment to a non-treatment) whenever her/his observed value of the assignment variable equals or…

Bayesian hierarchical models for cost-effectiveness analyses that use data from cluster randomized trials.

PubMed

Grieve, Richard; Nixon, Richard; Thompson, Simon G

2010-01-01

Cost-effectiveness analyses (CEA) may be undertaken alongside cluster randomized trials (CRTs) where randomization is at the level of the cluster (for example, the hospital or primary care provider) rather than the individual. Costs (and outcomes) within clusters may be correlated so that the assumption made by standard bivariate regression models, that observations are independent, is incorrect. This study develops a flexible modeling framework to acknowledge the clustering in CEA that use CRTs. The authors extend previous Bayesian bivariate models for CEA of multicenter trials to recognize the specific form of clustering in CRTs. They develop new Bayesian hierarchical models (BHMs) that allow mean costs and outcomes, and also variances, to differ across clusters. They illustrate how each model can be applied using data from a large (1732 cases, 70 primary care providers) CRT evaluating alternative interventions for reducing postnatal depression. The analyses compare cost-effectiveness estimates from BHMs with standard bivariate regression models that ignore the data hierarchy. The BHMs show high levels of cost heterogeneity across clusters (intracluster correlation coefficient, 0.17). Compared with standard regression models, the BHMs yield substantially increased uncertainty surrounding the cost-effectiveness estimates, and altered point estimates. The authors conclude that ignoring clustering can lead to incorrect inferences. The BHMs that they present offer a flexible modeling framework that can be applied more generally to CEA that use CRTs.

Teacher consultation and coaching within mental health practice: classroom and child effects in urban elementary schools.

PubMed

Cappella, Elise; Hamre, Bridget K; Kim, Ha Yeon; Henry, David B; Frazier, Stacy L; Atkins, Marc S; Schoenwald, Sonja K

2012-08-01

To examine effects of a teacher consultation and coaching program delivered by school and community mental health professionals on change in observed classroom interactions and child functioning across one school year. Thirty-six classrooms within 5 urban elementary schools (87% Latino, 11% Black) were randomly assigned to intervention (training + consultation/coaching) and control (training only) conditions. Classroom and child outcomes (n = 364; 43% girls) were assessed in the fall and spring. Random effects regression models showed main effects of intervention on teacher-student relationship closeness, academic self-concept, and peer victimization. Results of multiple regression models showed levels of observed teacher emotional support in the fall moderated intervention impact on emotional support at the end of the school year. Results suggest teacher consultation and coaching can be integrated within existing mental health activities in urban schools and impact classroom effectiveness and child adaptation across multiple domains. © 2012 American Psychological Association

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle.

PubMed

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-12-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran.

Random Regression Models Using Legendre Polynomials to Estimate Genetic Parameters for Test-day Milk Protein Yields in Iranian Holstein Dairy Cattle

PubMed Central

Naserkheil, Masoumeh; Miraie-Ashtiani, Seyed Reza; Nejati-Javaremi, Ardeshir; Son, Jihyun; Lee, Deukhwan

2016-01-01

The objective of this study was to estimate the genetic parameters of milk protein yields in Iranian Holstein dairy cattle. A total of 1,112,082 test-day milk protein yield records of 167,269 first lactation Holstein cows, calved from 1990 to 2010, were analyzed. Estimates of the variance components, heritability, and genetic correlations for milk protein yields were obtained using a random regression test-day model. Milking times, herd, age of recording, year, and month of recording were included as fixed effects in the model. Additive genetic and permanent environmental random effects for the lactation curve were taken into account by applying orthogonal Legendre polynomials of the fourth order in the model. The lowest and highest additive genetic variances were estimated at the beginning and end of lactation, respectively. Permanent environmental variance was higher at both extremes. Residual variance was lowest at the middle of the lactation and contrarily, heritability increased during this period. Maximum heritability was found during the 12th lactation stage (0.213±0.007). Genetic, permanent, and phenotypic correlations among test-days decreased as the interval between consecutive test-days increased. A relatively large data set was used in this study; therefore, the estimated (co)variance components for random regression coefficients could be used for national genetic evaluation of dairy cattle in Iran. PMID:26954192

Visible and near infrared spectroscopy coupled to random forest to quantify some soil quality parameters

NASA Astrophysics Data System (ADS)

de Santana, Felipe Bachion; de Souza, André Marcelo; Poppi, Ronei Jesus

2018-02-01

This study evaluates the use of visible and near infrared spectroscopy (Vis-NIRS) combined with multivariate regression based on random forest to quantify some quality soil parameters. The parameters analyzed were soil cation exchange capacity (CEC), sum of exchange bases (SB), organic matter (OM), clay and sand present in the soils of several regions of Brazil. Current methods for evaluating these parameters are laborious, timely and require various wet analytical methods that are not adequate for use in precision agriculture, where faster and automatic responses are required. The random forest regression models were statistically better than PLS regression models for CEC, OM, clay and sand, demonstrating resistance to overfitting, attenuating the effect of outlier samples and indicating the most important variables for the model. The methodology demonstrates the potential of the Vis-NIR as an alternative for determination of CEC, SB, OM, sand and clay, making possible to develop a fast and automatic analytical procedure.

Effect of folic acid on appetite in children: ordinal logistic and fuzzy logistic regressions.

PubMed

Namdari, Mahshid; Abadi, Alireza; Taheri, S Mahmoud; Rezaei, Mansour; Kalantari, Naser; Omidvar, Nasrin

2014-03-01

Reduced appetite and low food intake are often a concern in preschool children, since it can lead to malnutrition, a leading cause of impaired growth and mortality in childhood. It is occasionally considered that folic acid has a positive effect on appetite enhancement and consequently growth in children. The aim of this study was to assess the effect of folic acid on the appetite of preschool children 3 to 6 y old. The study sample included 127 children ages 3 to 6 who were randomly selected from 20 preschools in the city of Tehran in 2011. Since appetite was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. The obtained results were compared with a statistical ordinal logistic model. After controlling for the potential confounders, in a statistical ordinal logistic model, serum folate showed a significantly positive effect on appetite. A small but positive effect of folate was detected by fuzzy logistic regression. Based on fuzzy regression, the risk for poor appetite in preschool children was related to the employment status of their mothers. In this study, a positive association was detected between the levels of serum folate and improved appetite. For further investigation, a randomized controlled, double-blind clinical trial could be helpful to address causality. Copyright © 2014 Elsevier Inc. All rights reserved.

Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks.

PubMed

Hsieh, Chung-Ho; Lu, Ruey-Hwa; Lee, Nai-Hsin; Chiu, Wen-Ta; Hsu, Min-Huei; Li, Yu-Chuan Jack

2011-01-01

Diagnosing acute appendicitis clinically is still difficult. We developed random forests, support vector machines, and artificial neural network models to diagnose acute appendicitis. Between January 2006 and December 2008, patients who had a consultation session with surgeons for suspected acute appendicitis were enrolled. Seventy-five percent of the data set was used to construct models including random forest, support vector machines, artificial neural networks, and logistic regression. Twenty-five percent of the data set was withheld to evaluate model performance. The area under the receiver operating characteristic curve (AUC) was used to evaluate performance, which was compared with that of the Alvarado score. Data from a total of 180 patients were collected, 135 used for training and 45 for testing. The mean age of patients was 39.4 years (range, 16-85). Final diagnosis revealed 115 patients with and 65 without appendicitis. The AUC of random forest, support vector machines, artificial neural networks, logistic regression, and Alvarado was 0.98, 0.96, 0.91, 0.87, and 0.77, respectively. The sensitivity, specificity, positive, and negative predictive values of random forest were 94%, 100%, 100%, and 87%, respectively. Random forest performed better than artificial neural networks, logistic regression, and Alvarado. We demonstrated that random forest can predict acute appendicitis with good accuracy and, deployed appropriately, can be an effective tool in clinical decision making. Copyright © 2011 Mosby, Inc. All rights reserved.

Strengthening the Regression Discontinuity Design Using Additional Design Elements: A Within-Study Comparison

ERIC Educational Resources Information Center

Wing, Coady; Cook, Thomas D.

2013-01-01

The sharp regression discontinuity design (RDD) has three key weaknesses compared to the randomized clinical trial (RCT). It has lower statistical power, it is more dependent on statistical modeling assumptions, and its treatment effect estimates are limited to the narrow subpopulation of cases immediately around the cutoff, which is rarely of…

Genetic improvement in mastitis resistance: comparison of selection criteria from cross-sectional and random regression sire models for somatic cell score.

PubMed

Odegård, J; Klemetsdal, G; Heringstad, B

2005-04-01

Several selection criteria for reducing incidence of mastitis were developed from a random regression sire model for test-day somatic cell score (SCS). For comparison, sire transmitting abilities were also predicted based on a cross-sectional model for lactation mean SCS. Only first-crop daughters were used in genetic evaluation of SCS, and the different selection criteria were compared based on their correlation with incidence of clinical mastitis in second-crop daughters (measured as mean daughter deviations). Selection criteria were predicted based on both complete and reduced first-crop daughter groups (261 or 65 daughters per sire, respectively). For complete daughter groups, predicted transmitting abilities at around 30 d in milk showed the best predictive ability for incidence of clinical mastitis, closely followed by average predicted transmitting abilities over the entire lactation. Both of these criteria were derived from the random regression model. These selection criteria improved accuracy of selection by approximately 2% relative to a cross-sectional model. However, for reduced daughter groups, the cross-sectional model yielded increased predictive ability compared with the selection criteria based on the random regression model. This result may be explained by the cross-sectional model being more robust, i.e., less sensitive to precision of (co)variance components estimates and effects of data structure.

The arcsine is asinine: the analysis of proportions in ecology.

PubMed

Warton, David I; Hui, Francis K C

2011-01-01

The arcsine square root transformation has long been standard procedure when analyzing proportional data in ecology, with applications in data sets containing binomial and non-binomial response variables. Here, we argue that the arcsine transform should not be used in either circumstance. For binomial data, logistic regression has greater interpretability and higher power than analyses of transformed data. However, it is important to check the data for additional unexplained variation, i.e., overdispersion, and to account for it via the inclusion of random effects in the model if found. For non-binomial data, the arcsine transform is undesirable on the grounds of interpretability, and because it can produce nonsensical predictions. The logit transformation is proposed as an alternative approach to address these issues. Examples are presented in both cases to illustrate these advantages, comparing various methods of analyzing proportions including untransformed, arcsine- and logit-transformed linear models and logistic regression (with or without random effects). Simulations demonstrate that logistic regression usually provides a gain in power over other methods.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

Influence of therapist competence and quantity of cognitive behavioural therapy on suicidal behaviour and inpatient hospitalisation in a randomised controlled trial in borderline personality disorder: further analyses of treatment effects in the BOSCOT study.

PubMed

Norrie, John; Davidson, Kate; Tata, Philip; Gumley, Andrew

2013-09-01

We investigated the treatment effects reported from a high-quality randomized controlled trial of cognitive behavioural therapy (CBT) for 106 people with borderline personality disorder attending community-based clinics in the UK National Health Service - the BOSCOT trial. Specifically, we examined whether the amount of therapy and therapist competence had an impact on our primary outcome, the number of suicidal acts, using instrumental variables regression modelling. Randomized controlled trial. Participants from across three sites (London, Glasgow, and Ayrshire/Arran) were randomized equally to CBT for personality disorders (CBTpd) plus Treatment as Usual or to Treatment as Usual. Treatment as Usual varied between sites and individuals, but was consistent with routine treatment in the UK National Health Service at the time. CBTpd comprised an average 16 sessions (range 0-35) over 12 months. We used instrumental variable regression modelling to estimate the impact of quantity and quality of therapy received (recording activities and behaviours that took place after randomization) on number of suicidal acts and inpatient psychiatric hospitalization. A total of 101 participants provided full outcome data at 2 years post randomization. The previously reported intention-to-treat (ITT) results showed on average a reduction of 0.91 (95% confidence interval 0.15-1.67) suicidal acts over 2 years for those randomized to CBT. By incorporating the influence of quantity of therapy and therapist competence, we show that this estimate of the effect of CBTpd could be approximately two to three times greater for those receiving the right amount of therapy from a competent therapist. Trials should routinely control for and collect data on both quantity of therapy and therapist competence, which can be used, via instrumental variable regression modelling, to estimate treatment effects for optimal delivery of therapy. Such estimates complement rather than replace the ITT results, which are properly the principal analysis results from such trials. © 2013 The British Psychological Society.

Genetic parameters for stayability to consecutive calvings in Zebu cattle.

PubMed

Silva, D O; Santana, M L; Ayres, D R; Menezes, G R O; Silva, L O C; Nobre, P R C; Pereira, R J

2017-12-22

Longer-lived cows tend to be more profitable and the stayability trait is a selection criterion correlated to longevity. An alternative to the traditional approach to evaluate stayability is its definition based on consecutive calvings, whose main advantage is the more accurate evaluation of young bulls. However, no study using this alternative approach has been conducted for Zebu breeds. Therefore, the objective of this study was to compare linear random regression models to fit stayability to consecutive calvings of Guzerá, Nelore and Tabapuã cows and to estimate genetic parameters for this trait in the respective breeds. Data up to the eighth calving were used. The models included the fixed effects of age at first calving and year-season of birth of the cow and the random effects of contemporary group, additive genetic, permanent environmental and residual. Random regressions were modeled by orthogonal Legendre polynomials of order 1 to 4 (2 to 5 coefficients) for contemporary group, additive genetic and permanent environmental effects. Using Deviance Information Criterion as the selection criterion, the model with 4 regression coefficients for each effect was the most adequate for the Nelore and Tabapuã breeds and the model with 5 coefficients is recommended for the Guzerá breed. For Guzerá, heritabilities ranged from 0.05 to 0.08, showing a quadratic trend with a peak between the fourth and sixth calving. For the Nelore and Tabapuã breeds, the estimates ranged from 0.03 to 0.07 and from 0.03 to 0.08, respectively, and increased with increasing calving number. The additive genetic correlations exhibited a similar trend among breeds and were higher for stayability between closer calvings. Even between more distant calvings (second v. eighth), stayability showed a moderate to high genetic correlation, which was 0.77, 0.57 and 0.79 for the Guzerá, Nelore and Tabapuã breeds, respectively. For Guzerá, when the models with 4 or 5 regression coefficients were compared, the rank correlations between predicted breeding values for the intercept were always higher than 0.99, indicating the possibility of practical application of the least parameterized model. In conclusion, the model with 4 random regression coefficients is recommended for the genetic evaluation of stayability to consecutive calvings in Zebu cattle.

Genetic analyses of stillbirth in relation to litter size using random regression models.

PubMed

Chen, C Y; Misztal, I; Tsuruta, S; Herring, W O; Holl, J; Culbertson, M

2010-12-01

Estimates of genetic parameters for number of stillborns (NSB) in relation to litter size (LS) were obtained with random regression models (RRM). Data were collected from 4 purebred Duroc nucleus farms between 2004 and 2008. Two data sets with 6,575 litters for the first parity (P1) and 6,259 litters for the second to fifth parity (P2-5) with a total of 8,217 and 5,066 animals in the pedigree were analyzed separately. Number of stillborns was studied as a trait on sow level. Fixed effects were contemporary groups (farm-year-season) and fixed cubic regression coefficients on LS with Legendre polynomials. Models for P2-5 included the fixed effect of parity. Random effects were additive genetic effects for both data sets with permanent environmental effects included for P2-5. Random effects modeled with Legendre polynomials (RRM-L), linear splines (RRM-S), and degree 0 B-splines (RRM-BS) with regressions on LS were used. For P1, the order of polynomial, the number of knots, and the number of intervals used for respective models were quadratic, 3, and 3, respectively. For P2-5, the same parameters were linear, 2, and 2, respectively. Heterogeneous residual variances were considered in the models. For P1, estimates of heritability were 12 to 15%, 5 to 6%, and 6 to 7% in LS 5, 9, and 13, respectively. For P2-5, estimates were 15 to 17%, 4 to 5%, and 4 to 6% in LS 6, 9, and 12, respectively. For P1, average estimates of genetic correlations between LS 5 to 9, 5 to 13, and 9 to 13 were 0.53, -0.29, and 0.65, respectively. For P2-5, same estimates averaged for RRM-L and RRM-S were 0.75, -0.21, and 0.50, respectively. For RRM-BS with 2 intervals, the correlation was 0.66 between LS 5 to 7 and 8 to 13. Parameters obtained by 3 RRM revealed the nonlinear relationship between additive genetic effect of NSB and the environmental deviation of LS. The negative correlations between the 2 extreme LS might possibly indicate different genetic bases on incidence of stillbirth.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Teacher Consultation and Coaching within Mental Health Practice: Classroom and Child Effects in Urban Elementary Schools

PubMed Central

Cappella, Elise; Hamre, Bridget K.; Kim, Ha Yeon; Henry, David B.; Frazier, Stacy L.; Atkins, Marc S.; Schoenwald, Sonja K.

2012-01-01

Objective To examine effects of a teacher consultation and coaching program delivered by school and community mental health professionals on change in observed classroom interactions and child functioning across one school year. Method Thirty-six classrooms within five urban elementary schools (87% Latino, 11% Black) were randomly assigned to intervention (training + consultation/coaching) and control (training only) conditions. Classroom and child outcomes (n = 364; 43% girls) were assessed in the fall and spring. Results Random effects regression models showed main effects of intervention on teacher-student relationship closeness, academic self-concept, and peer victimization. Results of multiple regression models showed levels of observed teacher emotional support in the fall moderated intervention impact on emotional support at the end of the school year. Conclusions Results suggest teacher consultation and coaching can be integrated within existing mental health activities in urban schools and impact classroom effectiveness and child adaptation across multiple domains. PMID:22428941

Random forests of interaction trees for estimating individualized treatment effects in randomized trials.

PubMed

Su, Xiaogang; Peña, Annette T; Liu, Lei; Levine, Richard A

2018-04-29

Assessing heterogeneous treatment effects is a growing interest in advancing precision medicine. Individualized treatment effects (ITEs) play a critical role in such an endeavor. Concerning experimental data collected from randomized trials, we put forward a method, termed random forests of interaction trees (RFIT), for estimating ITE on the basis of interaction trees. To this end, we propose a smooth sigmoid surrogate method, as an alternative to greedy search, to speed up tree construction. The RFIT outperforms the "separate regression" approach in estimating ITE. Furthermore, standard errors for the estimated ITE via RFIT are obtained with the infinitesimal jackknife method. We assess and illustrate the use of RFIT via both simulation and the analysis of data from an acupuncture headache trial. Copyright © 2018 John Wiley & Sons, Ltd.

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials

PubMed Central

2018-01-01

Objective The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. Methods A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. Results All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. Conclusion These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins. PMID:28823122

Genetic analysis of milk production traits of Tunisian Holsteins using random regression test-day model with Legendre polynomials.

PubMed

Ben Zaabza, Hafedh; Ben Gara, Abderrahmen; Rekik, Boulbaba

2018-05-01

The objective of this study was to estimate genetic parameters of milk, fat, and protein yields within and across lactations in Tunisian Holsteins using a random regression test-day (TD) model. A random regression multiple trait multiple lactation TD model was used to estimate genetic parameters in the Tunisian dairy cattle population. Data were TD yields of milk, fat, and protein from the first three lactations. Random regressions were modeled with third-order Legendre polynomials for the additive genetic, and permanent environment effects. Heritabilities, and genetic correlations were estimated by Bayesian techniques using the Gibbs sampler. All variance components tended to be high in the beginning and the end of lactations. Additive genetic variances for milk, fat, and protein yields were the lowest and were the least variable compared to permanent variances. Heritability values tended to increase with parity. Estimates of heritabilities for 305-d yield-traits were low to moderate, 0.14 to 0.2, 0.12 to 0.17, and 0.13 to 0.18 for milk, fat, and protein yields, respectively. Within-parity, genetic correlations among traits were up to 0.74. Genetic correlations among lactations for the yield traits were relatively high and ranged from 0.78±0.01 to 0.82±0.03, between the first and second parities, from 0.73±0.03 to 0.8±0.04 between the first and third parities, and from 0.82±0.02 to 0.84±0.04 between the second and third parities. These results are comparable to previously reported estimates on the same population, indicating that the adoption of a random regression TD model as the official genetic evaluation for production traits in Tunisia, as developed by most Interbull countries, is possible in the Tunisian Holsteins.

Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine.

PubMed

Howard, Jeremy T; Ashwell, Melissa S; Baynes, Ronald E; Brooks, James D; Yeatts, James L; Maltecca, Christian

2018-01-01

In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs ( n = 198) that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK) parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope). The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite) was 0.15 (0.18) and 0.31 (0.40), respectively. For the parent drug (metabolite), the mean heritability across time was 0.27 (0.60) and 0.14 (0.44) for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug concentration across time resulted in estimates with a smaller standard error compared to models that utilized PK parameters. The current study found a low to moderate proportion of the phenotypic variation in metabolizing fenbendazole and flunixin meglumine that was explained by genetics in the current study.

Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine

PubMed Central

Howard, Jeremy T.; Ashwell, Melissa S.; Baynes, Ronald E.; Brooks, James D.; Yeatts, James L.; Maltecca, Christian

2018-01-01

In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198) that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK) parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope). The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite) was 0.15 (0.18) and 0.31 (0.40), respectively. For the parent drug (metabolite), the mean heritability across time was 0.27 (0.60) and 0.14 (0.44) for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug concentration across time resulted in estimates with a smaller standard error compared to models that utilized PK parameters. The current study found a low to moderate proportion of the phenotypic variation in metabolizing fenbendazole and flunixin meglumine that was explained by genetics in the current study. PMID:29487615

Random effects coefficient of determination for mixed and meta-analysis models

PubMed Central

Demidenko, Eugene; Sargent, James; Onega, Tracy

2011-01-01

The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, Rr2, that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If Rr2 is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of Rr2 apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects—the model can be estimated using the dummy variable approach. We derive explicit formulas for Rr2 in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine. PMID:23750070

Small-Sample Adjustments for Tests of Moderators and Model Fit in Robust Variance Estimation in Meta-Regression

ERIC Educational Resources Information Center

Tipton, Elizabeth; Pustejovsky, James E.

2015-01-01

Randomized experiments are commonly used to evaluate the effectiveness of educational interventions. The goal of the present investigation is to develop small-sample corrections for multiple contrast hypothesis tests (i.e., F-tests) such as the omnibus test of meta-regression fit or a test for equality of three or more levels of a categorical…

An optimal sample data usage strategy to minimize overfitting and underfitting effects in regression tree models based on remotely-sensed data

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.; Boyte, Stephen; Picotte, Joshua J.; Howard, Danny; Smith, Kelcy; Nelson, Kurtis

2016-01-01

Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.

Breastfeeding and intelligence: a systematic review and meta-analysis.

PubMed

Horta, Bernardo L; Loret de Mola, Christian; Victora, Cesar G

2015-12-01

This study was aimed at systematically reviewing evidence of the association between breastfeeding and performance in intelligence tests. Two independent searches were carried out using Medline, LILACS, SCIELO and Web of Science. Studies restricted to infants and those where estimates were not adjusted for stimulation or interaction at home were excluded. Fixed- and random-effects models were used to pool the effect estimates, and a random-effects regression was used to assess potential sources of heterogeneity. We included 17 studies with 18 estimates of the relationship between breastfeeding and performance in intelligence tests. In a random-effects model, breastfed subjects achieved a higher IQ [mean difference: 3.44 points (95% confidence interval: 2.30; 4.58)]. We found no evidence of publication bias. Studies that controlled for maternal IQ showed a smaller benefit from breastfeeding [mean difference 2.62 points (95% confidence interval: 1.25; 3.98)]. In the meta-regression, none of the study characteristics explained the heterogeneity among the studies. Breastfeeding is related to improved performance in intelligence tests. A positive effect of breastfeeding on cognition was also observed in a randomised trial. This suggests that the association is causal. ©2015 The Authors. Acta Paediatrica published by John Wiley & Sons Ltd on behalf of Foundation Acta Paediatrica.

Random regression models using Legendre polynomials or linear splines for test-day milk yield of dairy Gyr (Bos indicus) cattle.

PubMed

Pereira, R J; Bignardi, A B; El Faro, L; Verneque, R S; Vercesi Filho, A E; Albuquerque, L G

2013-01-01

Studies investigating the use of random regression models for genetic evaluation of milk production in Zebu cattle are scarce. In this study, 59,744 test-day milk yield records from 7,810 first lactations of purebred dairy Gyr (Bos indicus) and crossbred (dairy Gyr × Holstein) cows were used to compare random regression models in which additive genetic and permanent environmental effects were modeled using orthogonal Legendre polynomials or linear spline functions. Residual variances were modeled considering 1, 5, or 10 classes of days in milk. Five classes fitted the changes in residual variances over the lactation adequately and were used for model comparison. The model that fitted linear spline functions with 6 knots provided the lowest sum of residual variances across lactation. On the other hand, according to the deviance information criterion (DIC) and bayesian information criterion (BIC), a model using third-order and fourth-order Legendre polynomials for additive genetic and permanent environmental effects, respectively, provided the best fit. However, the high rank correlation (0.998) between this model and that applying third-order Legendre polynomials for additive genetic and permanent environmental effects, indicates that, in practice, the same bulls would be selected by both models. The last model, which is less parameterized, is a parsimonious option for fitting dairy Gyr breed test-day milk yield records. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

[On the effectiveness of the homeopathic remedy Arnica montana].

PubMed

Lüdtke, Rainer; Hacke, Daniela

2005-11-01

Arnica montana is a homeopathic remedy often prescribed after traumata and injuries. To assess whether Arnica is effective beyond placebo and to identify factors which support or contradict this effectiveness. All prospective, controlled trials on the effectiveness of homeopathic Arnica were included. Overall effectiveness was assessed by meta-analysis and meta-regression techniques. 68 comparisons from 49 clinical trials show a significant effectiveness of Arnica in traumatic injuries in random effects meta-analysis (odds ratio [OR], 0.36; 95% confidence interval [CI], 0.24-0.55), but not in meta-regression models (OR, 0.37; CI, 0.11-1.24). We found no evidence for publication bias. Studies from Medline-listed journals and high-quality studies are less likely to report positive results (p = 0.0006 and p = 0.0167). The hypothesis that homeopathic Arnica is effective could neither be proved nor rejected. All trials were highly heterogeneous, meta-regression does not help to explain this heterogeneity substantially.

Linear regression analysis of survival data with missing censoring indicators.

PubMed

Wang, Qihua; Dinse, Gregg E

2011-04-01

Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial.

Random Effects: Variance Is the Spice of Life.

PubMed

Jupiter, Daniel C

Covariates in regression analyses allow us to understand how independent variables of interest impact our dependent outcome variable. Often, we consider fixed effects covariates (e.g., gender or diabetes status) for which we examine subjects at each value of the covariate. We examine both men and women and, within each gender, examine both diabetic and nondiabetic patients. Occasionally, however, we consider random effects covariates for which we do not examine subjects at every value. For example, we examine patients from only a sample of hospitals and, within each hospital, examine both diabetic and nondiabetic patients. The random sampling of hospitals is in contrast to the complete coverage of all genders. In this column I explore the differences in meaning and analysis when thinking about fixed and random effects variables. Copyright © 2016 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Random effects coefficient of determination for mixed and meta-analysis models.

PubMed

Demidenko, Eugene; Sargent, James; Onega, Tracy

2012-01-01

The key feature of a mixed model is the presence of random effects. We have developed a coefficient, called the random effects coefficient of determination, [Formula: see text], that estimates the proportion of the conditional variance of the dependent variable explained by random effects. This coefficient takes values from 0 to 1 and indicates how strong the random effects are. The difference from the earlier suggested fixed effects coefficient of determination is emphasized. If [Formula: see text] is close to 0, there is weak support for random effects in the model because the reduction of the variance of the dependent variable due to random effects is small; consequently, random effects may be ignored and the model simplifies to standard linear regression. The value of [Formula: see text] apart from 0 indicates the evidence of the variance reduction in support of the mixed model. If random effects coefficient of determination is close to 1 the variance of random effects is very large and random effects turn into free fixed effects-the model can be estimated using the dummy variable approach. We derive explicit formulas for [Formula: see text] in three special cases: the random intercept model, the growth curve model, and meta-analysis model. Theoretical results are illustrated with three mixed model examples: (1) travel time to the nearest cancer center for women with breast cancer in the U.S., (2) cumulative time watching alcohol related scenes in movies among young U.S. teens, as a risk factor for early drinking onset, and (3) the classic example of the meta-analysis model for combination of 13 studies on tuberculosis vaccine.

Dose-Dependent Effects of Statins for Patients with Aneurysmal Subarachnoid Hemorrhage: Meta-Regression Analysis.

PubMed

To, Minh-Son; Prakash, Shivesh; Poonnoose, Santosh I; Bihari, Shailesh

2018-05-01

The study uses meta-regression analysis to quantify the dose-dependent effects of statin pharmacotherapy on vasospasm, delayed ischemic neurologic deficits (DIND), and mortality in aneurysmal subarachnoid hemorrhage. Prospective, retrospective observational studies, and randomized controlled trials (RCTs) were retrieved by a systematic database search. Summary estimates were expressed as absolute risk (AR) for a given statin dose or control (placebo). Meta-regression using inverse variance weighting and robust variance estimation was performed to assess the effect of statin dose on transformed AR in a random effects model. Dose-dependence of predicted AR with 95% confidence interval (CI) was recovered by using Miller's Freeman-Tukey inverse. The database search and study selection criteria yielded 18 studies (2594 patients) for analysis. These included 12 RCTs, 4 retrospective observational studies, and 2 prospective observational studies. Twelve studies investigated simvastatin, whereas the remaining studies investigated atorvastatin, pravastatin, or pitavastatin, with simvastatin-equivalent doses ranging from 20 to 80 mg. Meta-regression revealed dose-dependent reductions in Freeman-Tukey-transformed AR of vasospasm (slope coefficient -0.00404, 95% CI -0.00720 to -0.00087; P = 0.0321), DIND (slope coefficient -0.00316, 95% CI -0.00586 to -0.00047; P = 0.0392), and mortality (slope coefficient -0.00345, 95% CI -0.00623 to -0.00067; P = 0.0352). The present meta-regression provides weak evidence for dose-dependent reductions in vasospasm, DIND and mortality associated with acute statin use after aneurysmal subarachnoid hemorrhage. However, the analysis was limited by substantial heterogeneity among individual studies. Greater dosing strategies are a potential consideration for future RCTs. Copyright © 2018 Elsevier Inc. All rights reserved.

Heat and moisture exchangers (HMEs) and heated humidifiers (HHs) in adult critically ill patients: a systematic review, meta-analysis and meta-regression of randomized controlled trials.

PubMed

Vargas, Maria; Chiumello, Davide; Sutherasan, Yuda; Ball, Lorenzo; Esquinas, Antonio M; Pelosi, Paolo; Servillo, Giuseppe

2017-05-29

The aims of this systematic review and meta-analysis of randomized controlled trials are to evaluate the effects of active heated humidifiers (HHs) and moisture exchangers (HMEs) in preventing artificial airway occlusion and pneumonia, and on mortality in adult critically ill patients. In addition, we planned to perform a meta-regression analysis to evaluate the relationship between the incidence of artificial airway occlusion, pneumonia and mortality and clinical features of adult critically ill patients. Computerized databases were searched for randomized controlled trials (RCTs) comparing HHs and HMEs and reporting artificial airway occlusion, pneumonia and mortality as predefined outcomes. Relative risk (RR), 95% confidence interval for each outcome and I 2 were estimated for each outcome. Furthermore, weighted random-effect meta-regression analysis was performed to test the relationship between the effect size on each considered outcome and covariates. Eighteen RCTs and 2442 adult critically ill patients were included in the analysis. The incidence of artificial airway occlusion (RR = 1.853; 95% CI 0.792-4.338), pneumonia (RR = 932; 95% CI 0.730-1.190) and mortality (RR = 1.023; 95% CI 0.878-1.192) were not different in patients treated with HMEs and HHs. However, in the subgroup analyses the incidence of airway occlusion was higher in HMEs compared with HHs with non-heated wire (RR = 3.776; 95% CI 1.560-9.143). According to the meta-regression, the effect size in the treatment group on artificial airway occlusion was influenced by the percentage of patients with pneumonia (β = -0.058; p = 0.027; favors HMEs in studies with high prevalence of pneumonia), and a trend was observed for an effect of the duration of mechanical ventilation (MV) (β = -0.108; p = 0.054; favors HMEs in studies with longer MV time). In this meta-analysis we found no superiority of HMEs and HHs, in terms of artificial airway occlusion, pneumonia and mortality. A trend favoring HMEs was observed in studies including a high percentage of patients with pneumonia diagnosis at admission and those with prolonged MV. However, the choice of humidifiers should be made according to the clinical context, trying to avoid possible complications and reaching the appropriate performance at lower costs.

Sample size estimation for alternating logistic regressions analysis of multilevel randomized community trials of under-age drinking.

PubMed

Reboussin, Beth A; Preisser, John S; Song, Eun-Young; Wolfson, Mark

2012-07-01

Under-age drinking is an enormous public health issue in the USA. Evidence that community level structures may impact on under-age drinking has led to a proliferation of efforts to change the environment surrounding the use of alcohol. Although the focus of these efforts is to reduce drinking by individual youths, environmental interventions are typically implemented at the community level with entire communities randomized to the same intervention condition. A distinct feature of these trials is the tendency of the behaviours of individuals residing in the same community to be more alike than that of others residing in different communities, which is herein called 'clustering'. Statistical analyses and sample size calculations must account for this clustering to avoid type I errors and to ensure an appropriately powered trial. Clustering itself may also be of scientific interest. We consider the alternating logistic regressions procedure within the population-averaged modelling framework to estimate the effect of a law enforcement intervention on the prevalence of under-age drinking behaviours while modelling the clustering at multiple levels, e.g. within communities and within neighbourhoods nested within communities, by using pairwise odds ratios. We then derive sample size formulae for estimating intervention effects when planning a post-test-only or repeated cross-sectional community-randomized trial using the alternating logistic regressions procedure.

pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome-wide association studies.

PubMed

Zhang, J; Feng, J-Y; Ni, Y-L; Wen, Y-J; Niu, Y; Tamba, C L; Yue, C; Song, Q; Zhang, Y-M

2017-06-01

Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.

Principal regression analysis and the index leverage effect

NASA Astrophysics Data System (ADS)

Reigneron, Pierre-Alain; Allez, Romain; Bouchaud, Jean-Philippe

2011-09-01

We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call ‘Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode away from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications

PubMed Central

Austin, Peter C.

2017-01-01

Summary Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata). PMID:29307954

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications.

PubMed

Austin, Peter C

2017-08-01

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log-log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).

The role of gender in a smoking cessation intervention: a cluster randomized clinical trial.

PubMed

Puente, Diana; Cabezas, Carmen; Rodriguez-Blanco, Teresa; Fernández-Alonso, Carmen; Cebrian, Tránsito; Torrecilla, Miguel; Clemente, Lourdes; Martín, Carlos

2011-05-23

The prevalence of smoking in Spain is high in both men and women. The aim of our study was to evaluate the role of gender in the effectiveness of a specific smoking cessation intervention conducted in Spain. This study was a secondary analysis of a cluster randomized clinical trial in which the randomization unit was the Basic Care Unit (family physician and nurse who care for the same group of patients). The intervention consisted of a six-month period of implementing the recommendations of a Clinical Practice Guideline. A total of 2,937 current smokers at 82 Primary Care Centers in 13 different regions of Spain were included (2003-2005). The success rate was measured by a six-month continued abstinence rate at the one-year follow-up. A logistic mixed-effects regression model, taking Basic Care Units as random-effect parameter, was performed in order to analyze gender as a predictor of smoking cessation. At the one-year follow-up, the six-month continuous abstinence quit rate was 9.4% in men and 8.5% in women (p = 0.400). The logistic mixed-effects regression model showed that women did not have a higher odds of being an ex-smoker than men after the analysis was adjusted for confounders (OR adjusted = 0.9, 95% CI = 0.7-1.2). Gender does not appear to be a predictor of smoking cessation at the one-year follow-up in individuals presenting at Primary Care Centers. CLINICALTRIALS.GOV IDENTIFIER: NCT00125905.

Estimation of genetic effects in the presence of multicollinearity in multibreed beef cattle evaluation.

PubMed

Roso, V M; Schenkel, F S; Miller, S P; Schaeffer, L R

2005-08-01

Breed additive, dominance, and epistatic loss effects are of concern in the genetic evaluation of a multibreed population. Multiple regression equations used for fitting these effects may show a high degree of multicollinearity among predictor variables. Typically, when strong linear relationships exist, the regression coefficients have large SE and are sensitive to changes in the data file and to the addition or deletion of variables in the model. Generalized ridge regression methods were applied to obtain stable estimates of direct and maternal breed additive, dominance, and epistatic loss effects in the presence of multicollinearity among predictor variables. Preweaning weight gains of beef calves in Ontario, Canada, from 1986 to 1999 were analyzed. The genetic model included fixed direct and maternal breed additive, dominance, and epistatic loss effects, fixed environmental effects of age of the calf, contemporary group, and age of the dam x sex of the calf, random additive direct and maternal genetic effects, and random maternal permanent environment effect. The degree and the nature of the multicollinearity were identified and ridge regression methods were used as an alternative to ordinary least squares (LS). Ridge parameters were obtained using two different objective methods: 1) generalized ridge estimator of Hoerl and Kennard (R1); and 2) bootstrap in combination with cross-validation (R2). Both ridge regression methods outperformed the LS estimator with respect to mean squared error of predictions (MSEP) and variance inflation factors (VIF) computed over 100 bootstrap samples. The MSEP of R1 and R2 were similar, and they were 3% less than the MSEP of LS. The average VIF of LS, R1, and R2 were equal to 26.81, 6.10, and 4.18, respectively. Ridge regression methods were particularly effective in decreasing the multicollinearity involving predictor variables of breed additive effects. Because of a high degree of confounding between estimates of maternal dominance and direct epistatic loss effects, it was not possible to compare the relative importance of these effects with a high level of confidence. The inclusion of epistatic loss effects in the additive-dominance model did not cause noticeable reranking of sires, dams, and calves based on across-breed EBV. More precise estimates of breed effects as a result of this study may result in more stable across-breed estimated breeding values over the years.

An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level

NASA Astrophysics Data System (ADS)

Muller, Sybrand Jacobus; van Niekerk, Adriaan

2016-07-01

Soil salinity often leads to reduced crop yield and quality and can render soils barren. Irrigated areas are particularly at risk due to intensive cultivation and secondary salinization caused by waterlogging. Regular monitoring of salt accumulation in irrigation schemes is needed to keep its negative effects under control. The dynamic spatial and temporal characteristics of remote sensing can provide a cost-effective solution for monitoring salt accumulation at irrigation scheme level. This study evaluated a range of pan-fused SPOT-5 derived features (spectral bands, vegetation indices, image textures and image transformations) for classifying salt-affected areas in two distinctly different irrigation schemes in South Africa, namely Vaalharts and Breede River. The relationship between the input features and electro conductivity measurements were investigated using regression modelling (stepwise linear regression, partial least squares regression, curve fit regression modelling) and supervised classification (maximum likelihood, nearest neighbour, decision tree analysis, support vector machine and random forests). Classification and regression trees and random forest were used to select the most important features for differentiating salt-affected and unaffected areas. The results showed that the regression analyses produced weak models (<0.4 R squared). Better results were achieved using the supervised classifiers, but the algorithms tend to over-estimate salt-affected areas. A key finding was that none of the feature sets or classification algorithms stood out as being superior for monitoring salt accumulation at irrigation scheme level. This was attributed to the large variations in the spectral responses of different crops types at different growing stages, coupled with their individual tolerances to saline conditions.

Influence of therapist competence and quantity of cognitive behavioural therapy on suicidal behaviour and inpatient hospitalisation in a randomised controlled trial in borderline personality disorder: Further analyses of treatment effects in the BOSCOT study

PubMed Central

Norrie, John; Davidson, Kate; Tata, Philip; Gumley, Andrew

2013-01-01

Objectives We investigated the treatment effects reported from a high-quality randomized controlled trial of cognitive behavioural therapy (CBT) for 106 people with borderline personality disorder attending community-based clinics in the UK National Health Service – the BOSCOT trial. Specifically, we examined whether the amount of therapy and therapist competence had an impact on our primary outcome, the number of suicidal acts†, using instrumental variables regression modelling. Design Randomized controlled trial. Participants from across three sites (London, Glasgow, and Ayrshire/Arran) were randomized equally to CBT for personality disorders (CBTpd) plus Treatment as Usual or to Treatment as Usual. Treatment as Usual varied between sites and individuals, but was consistent with routine treatment in the UK National Health Service at the time. CBTpd comprised an average 16 sessions (range 0–35) over 12 months. Method We used instrumental variable regression modelling to estimate the impact of quantity and quality of therapy received (recording activities and behaviours that took place after randomization) on number of suicidal acts and inpatient psychiatric hospitalization. Results A total of 101 participants provided full outcome data at 2 years post randomization. The previously reported intention-to-treat (ITT) results showed on average a reduction of 0.91 (95% confidence interval 0.15–1.67) suicidal acts over 2 years for those randomized to CBT. By incorporating the influence of quantity of therapy and therapist competence, we show that this estimate of the effect of CBTpd could be approximately two to three times greater for those receiving the right amount of therapy from a competent therapist. Conclusions Trials should routinely control for and collect data on both quantity of therapy and therapist competence, which can be used, via instrumental variable regression modelling, to estimate treatment effects for optimal delivery of therapy. Such estimates complement rather than replace the ITT results, which are properly the principal analysis results from such trials. Practitioner points Assessing the impact of the quantity and quality of therapy (competence of therapists) is complex. More competent therapists, trained in CBTpd, may significantly reduce the number of suicidal act in patients with borderline personality disorder. PMID:23420622

Reinforcement of subarachnoid block by epidural volume effect in lower abdominal surgery: A comparison between fentanyl and tramadol for efficacy and block properties

PubMed Central

Mohan, Atiharsh; Singh, Preet Mohinder; Malviya, Deepak; Arya, Sunil Kumar; Singh, Dinesh Kumar

2012-01-01

Background: Epidural volume extension (EVE) is claimed to increase the block height and decrease the dose requirement for intrathecal drug. However, almost all studies have been done in obstetric population and none actually compares the effect of additional drugs added to epidural volume. Materials and Methods: Seventy-five (ASA I and II) patients scheduled for lower abdominal surgery were randomly divided into three groups. All groups received intrathecal 10 mg bupivacaine; two groups received additional 10 ml of normal saline epidurally with 25 mg tramadol or 25 mg of fentanyl. Groups were than compared for maximal block height, rate of sensory block regression to T10, and motor block regression to Bromage scale of 0. Time to first analgesia and adverse effects were also compared among the three groups. Materials and Methods: Seventy-five (ASA I and II) patients scheduled for lower abdominal surgery were randomly divided into three groups. All groups received intrathecal 10 mg bupivacaine; two groups received additional 10 ml of normal saline epidurally with 25 mg tramadol or 25 mg of fentanyl. Groups were than compared for maximal block height, rate of sensory block regression to T10, and motor block regression to Bromage scale of 0. Time to first analgesia and adverse effects were also compared among the three groups. Results: Groups with EVE had statistically significant higher block height, with a significant faster regression that the control group. However, both fentanyl and tramadol groups were inseparable in respect to motor or sensory block regression. Fentanyl group had maximal time to first analgesia, followed by tramadol and control groups. Hemodynamic alterations were also more common in EVE groups. Conclusion: EVE can increase the block height significantly, but it seems to be limited only to the physical property of additional volume in epidural space and fentanyl or tramadol do not seem to differ in their ability to alter block properties. PMID:25885615

Effect of extended-release niacin on plasma lipoprotein(a) levels: A systematic review and meta-analysis of randomized placebo-controlled trials.

PubMed

Sahebkar, Amirhosssein; Reiner, Željko; Simental-Mendía, Luis E; Ferretti, Gianna; Cicero, Arrigo F G

2016-11-01

Lipoprotein(a) (Lp(a)) is a proatherogenic and prothrombotic lipoprotein. Our aim was to quantify the extended-release nicotinic acid Lp(a) reducing effect with a meta-analysis of the available randomized clinical trials. A meta-analysis and random-effects meta-regression were performed on data pooled from 14 randomized placebo-controlled clinical trials published between 1998 and 2015, comprising 17 treatment arms, which included 9013 subjects, with 5362 in the niacin arm. The impact of ER niacin on plasma Lp(a) concentrations was reported in 17 treatment arms. Meta-analysis suggested a significant reduction of Lp(a) levels following ER niacin treatment (weighted mean difference - WMD: -22.90%, 95% CI: -27.32, -18.48, p<0.001). Results also remained similar when the meta-analysis was repeated with standardized mean difference as summary statistic (WMD: -0.66, 95% CI: -0.82, -0.50, p<0.001). When the studies were categorized according to the administered dose, there was a comparable effect between the subsets of studies with administered doses of <2000mg/day (WMD: -21.85%, 95% CI: -30.61, -13.10, p<0.001) and ≥2000mg/day (WMD: -23.21%, 95% CI: -28.41, -18.01, p<0.001). The results of the random-effects meta-regression did not suggest any significant association between the changes in plasma concentrations of Lp(a) with dose (slope: -0.0001; 95% CI: -0.01, 0.01; p=0.983), treatment duration (slope: -0.40; 95% CI: -0.97, 0.17; p=0.166), and percentage change in plasma HDL-C concentrations (slope: 0.44; 95% CI: -0.48, 1.36; p=0.350). In this meta-analysis of randomized placebo-controlled clinical trials, treatment with nicotinic acid was associated with a significant reduction in Lp(a) levels. Copyright © 2016 Elsevier Inc. All rights reserved.

MIXREG: a computer program for mixed-effects regression analysis with autocorrelated errors.

PubMed

Hedeker, D; Gibbons, R D

1996-05-01

MIXREG is a program that provides estimates for a mixed-effects regression model (MRM) for normally-distributed response data including autocorrelated errors. This model can be used for analysis of unbalanced longitudinal data, where individuals may be measured at a different number of timepoints, or even at different timepoints. Autocorrelated errors of a general form or following an AR(1), MA(1), or ARMA(1,1) form are allowable. This model can also be used for analysis of clustered data, where the mixed-effects model assumes data within clusters are dependent. The degree of dependency is estimated jointly with estimates of the usual model parameters, thus adjusting for clustering. MIXREG uses maximum marginal likelihood estimation, utilizing both the EM algorithm and a Fisher-scoring solution. For the scoring solution, the covariance matrix of the random effects is expressed in its Gaussian decomposition, and the diagonal matrix reparameterized using the exponential transformation. Estimation of the individual random effects is accomplished using an empirical Bayes approach. Examples illustrating usage and features of MIXREG are provided.

Encrypted data stream identification using randomness sparse representation and fuzzy Gaussian mixture model

NASA Astrophysics Data System (ADS)

Zhang, Hong; Hou, Rui; Yi, Lei; Meng, Juan; Pan, Zhisong; Zhou, Yuhuan

2016-07-01

The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.

Restricted spatial regression in practice: Geostatistical models, confounding, and robustness under model misspecification

USGS Publications Warehouse

Hanks, Ephraim M.; Schliep, Erin M.; Hooten, Mevin B.; Hoeting, Jennifer A.

2015-01-01

In spatial generalized linear mixed models (SGLMMs), covariates that are spatially smooth are often collinear with spatially smooth random effects. This phenomenon is known as spatial confounding and has been studied primarily in the case where the spatial support of the process being studied is discrete (e.g., areal spatial data). In this case, the most common approach suggested is restricted spatial regression (RSR) in which the spatial random effects are constrained to be orthogonal to the fixed effects. We consider spatial confounding and RSR in the geostatistical (continuous spatial support) setting. We show that RSR provides computational benefits relative to the confounded SGLMM, but that Bayesian credible intervals under RSR can be inappropriately narrow under model misspecification. We propose a posterior predictive approach to alleviating this potential problem and discuss the appropriateness of RSR in a variety of situations. We illustrate RSR and SGLMM approaches through simulation studies and an analysis of malaria frequencies in The Gambia, Africa.

Continuing or Temporarily Stopping Prestroke Antihypertensive Medication in Acute Stroke: An Individual Patient Data Meta-Analysis.

PubMed

Woodhouse, Lisa J; Manning, Lisa; Potter, John F; Berge, Eivind; Sprigg, Nikola; Wardlaw, Joanna; Lees, Kennedy R; Bath, Philip M; Robinson, Thompson G

2017-05-01

Over 50% of patients are already taking blood pressure-lowering therapy on hospital admission for acute stroke. An individual patient data meta-analysis from randomized controlled trials was undertaken to determine the effect of continuation versus temporarily stopping preexisting antihypertensive medication in acute stroke. Key databases were searched for trials against the following inclusion criteria: randomized design; stroke onset ≤48 hours; investigating the effect of continuation versus stopping prestroke antihypertensive medication; and follow-up of ≥2 weeks. Two randomized controlled trials were identified and included in this meta-analysis of individual patient data from 2860 patients with ≤48 hours of acute stroke. Risk of bias in each study was low. In adjusted logistic regression and multiple regression analyses (using random effects), we found no significant association between continuation of prestroke antihypertensive therapy (versus stopping) and risk of death or dependency at final follow-up: odds ratio 0.96 (95% confidence interval, 0.80-1.14). No significant associations were found between continuation (versus stopping) of therapy and secondary outcomes at final follow-up. Analyses for death and dependency in prespecified subgroups revealed no significant associations with continuation versus temporarily stopping therapy, with the exception of patients randomized ≤12 hours, in whom a difference favoring stopping treatment met statistical significance. We found no significant benefit with continuation of antihypertensive treatment in the acute stroke period. Therefore, there is no urgency to administer preexisting antihypertensive therapy in the first few hours or days after stroke, unless indicated for other comorbid conditions. © 2017 American Heart Association, Inc.

Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data

PubMed Central

Austin, Peter C.; Stryhn, Henrik; Leckie, George; Merlo, Juan

2017-01-01

Multilevel data occur frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models. These models incorporate cluster‐specific random effects that allow one to partition the total variation in the outcome into between‐cluster variation and between‐individual variation. The magnitude of the effect of clustering provides a measure of the general contextual effect. When outcomes are binary or time‐to‐event in nature, the general contextual effect can be quantified by measures of heterogeneity like the median odds ratio or the median hazard ratio, respectively, which can be calculated from a multilevel regression model. Outcomes that are integer counts denoting the number of times that an event occurred are common in epidemiological and medical research. The median (incidence) rate ratio in multilevel Poisson regression for counts that corresponds to the median odds ratio or median hazard ratio for binary or time‐to‐event outcomes respectively is relatively unknown and is rarely used. The median rate ratio is the median relative change in the rate of the occurrence of the event when comparing identical subjects from 2 randomly selected different clusters that are ordered by rate. We also describe how the variance partition coefficient, which denotes the proportion of the variation in the outcome that is attributable to between‐cluster differences, can be computed with count outcomes. We illustrate the application and interpretation of these measures in a case study analyzing the rate of hospital readmission in patients discharged from hospital with a diagnosis of heart failure. PMID:29114926

Approximating prediction uncertainty for random forest regression models

Treesearch

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature x humidity-dependent covariates.

PubMed

Brügemann, K; Gernand, E; von Borstel, U U; König, S

2011-08-01

Data used in the present study included 1,095,980 first-lactation test-day records for protein yield of 154,880 Holstein cows housed on 196 large-scale dairy farms in Germany. Data were recorded between 2002 and 2009 and merged with meteorological data from public weather stations. The maximum distance between each farm and its corresponding weather station was 50 km. Hourly temperature-humidity indexes (THI) were calculated using the mean of hourly measurements of dry bulb temperature and relative humidity. On the phenotypic scale, an increase in THI was generally associated with a decrease in daily protein yield. For genetic analyses, a random regression model was applied using time-dependent (d in milk, DIM) and THI-dependent covariates. Additive genetic and permanent environmental effects were fitted with this random regression model and Legendre polynomials of order 3 for DIM and THI. In addition, the fixed curve was modeled with Legendre polynomials of order 3. Heterogeneous residuals were fitted by dividing DIM into 5 classes, and by dividing THI into 4 classes, resulting in 20 different classes. Additive genetic variances for daily protein yield decreased with increasing degrees of heat stress and were lowest at the beginning of lactation and at extreme THI. Due to higher additive genetic variances, slightly higher permanent environment variances, and similar residual variances, heritabilities were highest for low THI in combination with DIM at the end of lactation. Genetic correlations among individual values for THI were generally >0.90. These trends from the complex random regression model were verified by applying relatively simple bivariate animal models for protein yield measured in 2 THI environments; that is, defining a THI value of 60 as a threshold. These high correlations indicate the absence of any substantial genotype × environment interaction for protein yield. However, heritabilities and additive genetic variances from the random regression model tended to be slightly higher in the THI range corresponding to cows' comfort zone. Selecting such superior environments for progeny testing can contribute to an accurate genetic differentiation among selection candidates. Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Household water treatment in developing countries: comparing different intervention types using meta-regression.

PubMed

Hunter, Paul R

2009-12-01

Household water treatment (HWT) is being widely promoted as an appropriate intervention for reducing the burden of waterborne disease in poor communities in developing countries. A recent study has raised concerns about the effectiveness of HWT, in part because of concerns over the lack of blinding and in part because of considerable heterogeneity in the reported effectiveness of randomized controlled trials. This study set out to attempt to investigate the causes of this heterogeneity and so identify factors associated with good health gains. Studies identified in an earlier systematic review and meta-analysis were supplemented with more recently published randomized controlled trials. A total of 28 separate studies of randomized controlled trials of HWT with 39 intervention arms were included in the analysis. Heterogeneity was studied using the "metareg" command in Stata. Initial analyses with single candidate predictors were undertaken and all variables significant at the P < 0.2 level were included in a final regression model. Further analyses were done to estimate the effect of the interventions over time by MonteCarlo modeling using @Risk and the parameter estimates from the final regression model. The overall effect size of all unblinded studies was relative risk = 0.56 (95% confidence intervals 0.51-0.63), but after adjusting for bias due to lack of blinding the effect size was much lower (RR = 0.85, 95% CI = 0.76-0.97). Four main variables were significant predictors of effectiveness of intervention in a multipredictor meta regression model: Log duration of study follow-up (regression coefficient of log effect size = 0.186, standard error (SE) = 0.072), whether or not the study was blinded (coefficient 0.251, SE 0.066) and being conducted in an emergency setting (coefficient -0.351, SE 0.076) were all significant predictors of effect size in the final model. Compared to the ceramic filter all other interventions were much less effective (Biosand 0.247, 0.073; chlorine and safe waste storage 0.295, 0.061; combined coagulant-chlorine 0.2349, 0.067; SODIS 0.302, 0.068). A Monte Carlo model predicted that over 12 months ceramic filters were likely to be still effective at reducing disease, whereas SODIS, chlorination, and coagulation-chlorination had little if any benefit. Indeed these three interventions are predicted to have the same or less effect than what may be expected due purely to reporting bias in unblinded studies With the currently available evidence ceramic filters are the most effective form of HWT in the longterm, disinfection-only interventions including SODIS appear to have poor if any longterm public health benefit.

Some Factors Effected Student's Calculus Learning Outcome

ERIC Educational Resources Information Center

Rajagukguk, Wamington

2016-01-01

The purpose of this study is to determine the factors effected calculus learning outcome of the student. This study was conducted with 176 respondents, which were selected randomly. The data were obtained by questionnaire, and then analyzed by using multiple regressions, and correlation, at level of a = 0.05. The findings showed there is the…

Item and Testlet Position Effects in Computer-Based Alternate Assessments for Students with Disabilities

ERIC Educational Resources Information Center

Bulut, Okan; Lei, Ming; Guo, Qi

2018-01-01

Item positions in educational assessments are often randomized across students to prevent cheating. However, if altering item positions results in any significant impact on students' performance, it may threaten the validity of test scores. Two widely used approaches for detecting position effects -- logistic regression and hierarchical…

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis.

PubMed

Austin, Peter C; Wagner, Philippe; Merlo, Juan

2017-03-15

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

The median hazard ratio: a useful measure of variance and general contextual effects in multilevel survival analysis

PubMed Central

Wagner, Philippe; Merlo, Juan

2016-01-01

Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster‐specific random effects which allow one to partition the total individual variance into between‐cluster variation and between‐individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time‐to‐event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., ‘frailty’) Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:27885709

Regression dilution bias: tools for correction methods and sample size calculation.

PubMed

Berglund, Lars

2012-08-01

Random errors in measurement of a risk factor will introduce downward bias of an estimated association to a disease or a disease marker. This phenomenon is called regression dilution bias. A bias correction may be made with data from a validity study or a reliability study. In this article we give a non-technical description of designs of reliability studies with emphasis on selection of individuals for a repeated measurement, assumptions of measurement error models, and correction methods for the slope in a simple linear regression model where the dependent variable is a continuous variable. Also, we describe situations where correction for regression dilution bias is not appropriate. The methods are illustrated with the association between insulin sensitivity measured with the euglycaemic insulin clamp technique and fasting insulin, where measurement of the latter variable carries noticeable random error. We provide software tools for estimation of a corrected slope in a simple linear regression model assuming data for a continuous dependent variable and a continuous risk factor from a main study and an additional measurement of the risk factor in a reliability study. Also, we supply programs for estimation of the number of individuals needed in the reliability study and for choice of its design. Our conclusion is that correction for regression dilution bias is seldom applied in epidemiological studies. This may cause important effects of risk factors with large measurement errors to be neglected.

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Predicting attention-deficit/hyperactivity disorder severity from psychosocial stress and stress-response genes: a random forest regression approach

PubMed Central

van der Meer, D; Hoekstra, P J; van Donkelaar, M; Bralten, J; Oosterlaan, J; Heslenfeld, D; Faraone, S V; Franke, B; Buitelaar, J K; Hartman, C A

2017-01-01

Identifying genetic variants contributing to attention-deficit/hyperactivity disorder (ADHD) is complicated by the involvement of numerous common genetic variants with small effects, interacting with each other as well as with environmental factors, such as stress exposure. Random forest regression is well suited to explore this complexity, as it allows for the analysis of many predictors simultaneously, taking into account any higher-order interactions among them. Using random forest regression, we predicted ADHD severity, measured by Conners’ Parent Rating Scales, from 686 adolescents and young adults (of which 281 were diagnosed with ADHD). The analysis included 17 374 single-nucleotide polymorphisms (SNPs) across 29 genes previously linked to hypothalamic–pituitary–adrenal (HPA) axis activity, together with information on exposure to 24 individual long-term difficulties or stressful life events. The model explained 12.5% of variance in ADHD severity. The most important SNP, which also showed the strongest interaction with stress exposure, was located in a region regulating the expression of telomerase reverse transcriptase (TERT). Other high-ranking SNPs were found in or near NPSR1, ESR1, GABRA6, PER3, NR3C2 and DRD4. Chronic stressors were more influential than single, severe, life events. Top hits were partly shared with conduct problems. We conclude that random forest regression may be used to investigate how multiple genetic and environmental factors jointly contribute to ADHD. It is able to implicate novel SNPs of interest, interacting with stress exposure, and may explain inconsistent findings in ADHD genetics. This exploratory approach may be best combined with more hypothesis-driven research; top predictors and their interactions with one another should be replicated in independent samples. PMID:28585928

Model selection for logistic regression models

NASA Astrophysics Data System (ADS)

Duller, Christine

2012-09-01

Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

Needling versus liquid nitrogen cryotherapy for the treatment of pedal warts a randomized controlled pilot study.

PubMed

Cunningham, Daniel J; Brimage, Jessica T; Naraghi, Reza N; Bower, Virginia M

2014-07-01

We hypothesized that needling of a pedal wart creates local inflammation and a subsequent cell-mediated immune response (CMIR) against human papillomavirus. The primary objective of this study was to investigate whether needling to induce a CMIR against human papillomavirus is an effective treatment for pedal warts compared with liquid nitrogen cryotherapy. A secondary objective was to investigate whether the CMIR induced by needling is effective against satellite pedal warts. Eligible patients with pedal warts were randomly allocated to receive either needling or liquid nitrogen cryotherapy. Only the primary pedal wart was treated during the study. Follow-up was 12 weeks, with outcome assessments made independently under blinded circumstances. Of 37 patients enrolled in the study, 18 were allocated to receive needling and 19 to receive liquid nitrogen cryotherapy. Regression of the primary pedal wart occurred in 64.7% of the needling group (11 of 17) and in 6.2% of the liquid nitrogen cryotherapy group (1 of 16) (P =  .001). No significant relationship was found between needling of the primary pedal wart and regression of satellite pedal warts (P = .615) or complete pedal wart regression (P = .175). There was no significant difference in pain, satisfaction, or cosmesis between the two groups. The regression rate of the primary pedal wart was significantly higher in the needling group compared with the liquid nitrogen cryotherapy group.

Quasi-Experimental Designs for Causal Inference

ERIC Educational Resources Information Center

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Treatment with GLP1 receptor agonists reduce serum CRP concentrations in patients with type 2 diabetes mellitus: A systematic review and meta-analysis of randomized controlled trials.

PubMed

Mazidi, Mohsen; Karimi, Ehsan; Rezaie, Peyman; Ferns, Gordon A

2017-07-01

To undertake a systematic review and meta-analysis of randomized controlled trials of the effect of glucagon-like peptide-1 receptor agonist (GLP-1 RAs) therapy on serum C-reactive protein (CRP) concentrations. PubMed-Medline, SCOPUS, Web of Science and Google Scholar databases were searched for the period up until March 16, 2016. Prospective studies evaluating the impact of GLP-1 RAs on serum CRP were identified. A random effects model (using the DerSimonian-Laird method) and generic inverse variance methods were used for quantitative data synthesis. Sensitivity analysis was conducted using the leave-one-out method. Heterogeneity was quantitatively assessed using the I 2 index. Random effects meta-regression was performed using unrestricted maximum likelihood method to evaluate the impact of potential moderator. International Prospective Register for Systematic Reviews (PROSPERO) number CRD42016036868. Meta-analysis of the data from 7 treatment arms revealed a significant reduction in serum CRP concentrations following treatment with GLP-1 RAs (WMD -2.14 (mg/dL), 95% CI -3.51, -0.78, P=0.002; I 2 96.1%). Removal of one study in the meta-analysis did not change the result in the sensitivity analysis (WMD -2.14 (mg/dL), 95% CI -3.51, -0.78, P=0.002; I 2 96.1%), indicating that our results could not be solely attributed to the effect of a single study. Random effects meta-regression was performed to evaluate the impact of potential moderator on the estimated effect size. Changes in serum CRP concentration were associated with the duration of treatment (slope -0.097, 95% CI -0.158, -0.042, P<0.001). This meta-analysis suggests that GLP-1 RAs therapy causes a significant reduction in CRP. Copyright © 2016 Elsevier Inc. All rights reserved.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows.

PubMed

Bignardi, A B; El Faro, L; Torres Júnior, R A A; Cardoso, V L; Machado, P F; Albuquerque, L G

2011-10-31

We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.

Analysis of Palm Oil Production, Export, and Government Consumption to Gross Domestic Product of Five Districts in West Kalimantan by Panel Regression

NASA Astrophysics Data System (ADS)

Sulistianingsih, E.; Kiftiah, M.; Rosadi, D.; Wahyuni, H.

2017-04-01

Gross Domestic Product (GDP) is an indicator of economic growth in a region. GDP is a panel data, which consists of cross-section and time series data. Meanwhile, panel regression is a tool which can be utilised to analyse panel data. There are three models in panel regression, namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). The models will be chosen based on results of Chow Test, Hausman Test and Lagrange Multiplier Test. This research analyses palm oil about production, export, and government consumption to five district GDP are in West Kalimantan, namely Sanggau, Sintang, Sambas, Ketapang and Bengkayang by panel regression. Based on the results of analyses, it concluded that REM, which adjusted-determination-coefficient is 0,823, is the best model in this case. Also, according to the result, only Export and Government Consumption that influence GDP of the districts.

The role of gender in a smoking cessation intervention: a cluster randomized clinical trial

PubMed Central

2011-01-01

Background The prevalence of smoking in Spain is high in both men and women. The aim of our study was to evaluate the role of gender in the effectiveness of a specific smoking cessation intervention conducted in Spain. Methods This study was a secondary analysis of a cluster randomized clinical trial in which the randomization unit was the Basic Care Unit (family physician and nurse who care for the same group of patients). The intervention consisted of a six-month period of implementing the recommendations of a Clinical Practice Guideline. A total of 2,937 current smokers at 82 Primary Care Centers in 13 different regions of Spain were included (2003-2005). The success rate was measured by a six-month continued abstinence rate at the one-year follow-up. A logistic mixed-effects regression model, taking Basic Care Units as random-effect parameter, was performed in order to analyze gender as a predictor of smoking cessation. Results At the one-year follow-up, the six-month continuous abstinence quit rate was 9.4% in men and 8.5% in women (p = 0.400). The logistic mixed-effects regression model showed that women did not have a higher odds of being an ex-smoker than men after the analysis was adjusted for confounders (OR adjusted = 0.9, 95% CI = 0.7-1.2). Conclusions Gender does not appear to be a predictor of smoking cessation at the one-year follow-up in individuals presenting at Primary Care Centers. ClinicalTrials.gov Identifier NCT00125905. PMID:21605389

Comparative analysis of used car price evaluation models

NASA Astrophysics Data System (ADS)

Chen, Chuancan; Hao, Lulu; Xu, Cong

2017-05-01

An accurate used car price evaluation is a catalyst for the healthy development of used car market. Data mining has been applied to predict used car price in several articles. However, little is studied on the comparison of using different algorithms in used car price estimation. This paper collects more than 100,000 used car dealing records throughout China to do empirical analysis on a thorough comparison of two algorithms: linear regression and random forest. These two algorithms are used to predict used car price in three different models: model for a certain car make, model for a certain car series and universal model. Results show that random forest has a stable but not ideal effect in price evaluation model for a certain car make, but it shows great advantage in the universal model compared with linear regression. This indicates that random forest is an optimal algorithm when handling complex models with a large number of variables and samples, yet it shows no obvious advantage when coping with simple models with less variables.

Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Effect of Risk of Bias on the Effect Size of Meta-Analytic Estimates in Randomized Controlled Trials in Periodontology and Implant Dentistry.

PubMed

Faggion, Clovis Mariano; Wu, Yun-Chun; Scheidgen, Moritz; Tu, Yu-Kang

2015-01-01

Risk of bias (ROB) may threaten the internal validity of a clinical trial by distorting the magnitude of treatment effect estimates, although some conflicting information on this assumption exists. The objective of this study was evaluate the effect of ROB on the magnitude of treatment effect estimates in randomized controlled trials (RCTs) in periodontology and implant dentistry. A search for Cochrane systematic reviews (SRs), including meta-analyses of RCTs published in periodontology and implant dentistry fields, was performed in the Cochrane Library in September 2014. Random-effect meta-analyses were performed by grouping RCTs with different levels of ROBs in three domains (sequence generation, allocation concealment, and blinding of outcome assessment). To increase power and precision, only SRs with meta-analyses including at least 10 RCTs were included. Meta-regression was performed to investigate the association between ROB characteristics and the magnitudes of intervention effects in the meta-analyses. Of the 24 initially screened SRs, 21 SRs were excluded because they did not include at least 10 RCTs in the meta-analyses. Three SRs (two from periodontology field) generated information for conducting 27 meta-analyses. Meta-regression did not reveal significant differences in the relationship of the ROB level with the size of treatment effect estimates, although a trend for inflated estimates was observed in domains with unclear ROBs. In this sample of RCTs, high and (mainly) unclear risks of selection and detection biases did not seem to influence the size of treatment effect estimates, although several confounders might have influenced the strength of the association.

Comparing spatial regression to random forests for large environmental data sets

EPA Science Inventory

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputatio...

Evaluating an Organizational-Level Occupational Health Intervention in a Combined Regression Discontinuity and Randomized Control Design.

PubMed

Sørensen, By Ole H

2016-10-01

Organizational-level occupational health interventions have great potential to improve employees' health and well-being. However, they often compare unfavourably to individual-level interventions. This calls for improving methods for designing, implementing and evaluating organizational interventions. This paper presents and discusses the regression discontinuity design because, like the randomized control trial, it is a strong summative experimental design, but it typically fits organizational-level interventions better. The paper explores advantages and disadvantages of a regression discontinuity design with an embedded randomized control trial. It provides an example from an intervention study focusing on reducing sickness absence in 196 preschools. The paper demonstrates that such a design fits the organizational context, because it allows management to focus on organizations or workgroups with the most salient problems. In addition, organizations may accept an embedded randomized design because the organizations or groups with most salient needs receive obligatory treatment as part of the regression discontinuity design. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Inferring microhabitat preferences of Lilium catesbaei (Liliaceae).

PubMed

Sommers, Kristen Penney; Elswick, Michael; Herrick, Gabriel I; Fox, Gordon A

2011-05-01

Microhabitat studies use varied statistical methods, some treating site occupancy as a dependent and others as an independent variable. Using the rare Lilium catesbaei as an example, we show why approaches to testing hypotheses of differences between occupied and unoccupied sites can lead to erroneous conclusions about habitat preferences. Predictive approaches like logistic regression can better lead to understanding of habitat requirements. Using 32 lily locations and 30 random locations >2 m from a lily (complete data: 31 lily and 28 random spots), we measured physical conditions--photosynthetically active radiation (PAR), canopy cover, litter depth, distance to and height of nearest shrub, and soil moisture--and number and identity of neighboring plants. Twelve lilies were used to estimate a photosynthetic assimilation curve. Analyses used logistic regression, discriminant function analysis (DFA), (multivariate) analysis of variance, and resampled Wilcoxon tests. Logistic regression and DFA found identical predictors of presence (PAR, canopy cover, distance to shrub, litter), but hypothesis tests pointed to a different set (PAR, litter, canopy cover, height of nearest shrub). Lilies are mainly in high-PAR spots, often close to light saturation. By contrast, PAR in random spots was often near the lily light compensation point. Lilies were near Serenoa repens less than at random; otherwise, neighbor identity had no significant effect. Predictive methods are more useful in this context than the hypothesis tests. Light availability plays a big role in lily presence, which may help to explain increases in flowering and emergence after fire and roller-chopping.

The role of muscle strengthening in exercise therapy for knee osteoarthritis: A systematic review and meta-regression analysis of randomized trials.

PubMed

Bartholdy, Cecilie; Juhl, Carsten; Christensen, Robin; Lund, Hans; Zhang, Weiya; Henriksen, Marius

2017-08-01

To analyze if exercise interventions for patients with knee osteoarthritis (OA) following the American College of Sports Medicine (ACSM) definition of muscle strength training differs from other types of exercise, and to analyze associations between changes in muscle strength, pain, and disability. A systematic search in 5 electronic databases was performed to identify randomized controlled trials comparing exercise interventions with no intervention in knee OA, and reporting changes in muscle strength and in pain or disability assessed as standardized mean differences (SMD) with 95% confidence intervals (95% CI). Interventions were categorized as ACSM interventions or not-ACSM interventions and compared using stratified random effects meta-analysis models. Associations between knee extensor strength gain and changes in pain/disability were assessed using meta-regression analyses. The 45 eligible trials with 4699 participants and 56 comparisons (22 ACSM interventions) were included in this analysis. A statistically significant difference favoring the ACSM interventions with respect to knee extensor strength was found [SMD difference: 0.448 (95% CI: 0.091-0.805)]. No differences were observed regarding effects on pain and disability. The meta-regressions indicated that increases in knee extensor strength of 30-40% would be necessary for a likely concomitant beneficial effect on pain and disability, respectively. Exercise interventions following the ACSM criteria for strength training provide superior outcomes in knee extensor strength but not in pain or disability. An increase of less than 30% in knee extensor strength is not likely to be clinically beneficial in terms of changes in pain and disability (PROSPERO: CRD42014015344). Copyright © 2017 Elsevier Inc. All rights reserved.

Association of funding and conclusions in randomized drug trials: a reflection of treatment effect or adverse events?

PubMed

Als-Nielsen, Bodil; Chen, Wendong; Gluud, Christian; Kjaergard, Lise L

2003-08-20

Previous studies indicate that industry-sponsored trials tend to draw proindustry conclusions. To explore whether the association between funding and conclusions in randomized drug trials reflects treatment effects or adverse events. Observational study of 370 randomized drug trials included in meta-analyses from Cochrane reviews selected from the Cochrane Library, May 2001. From a random sample of 167 Cochrane reviews, 25 contained eligible meta-analyses (assessed a binary outcome; pooled at least 5 full-paper trials of which at least 1 reported adequate and 1 reported inadequate allocation concealment). The primary binary outcome from each meta-analysis was considered the primary outcome for all trials included in each meta-analysis. The association between funding and conclusions was analyzed by logistic regression with adjustment for treatment effect, adverse events, and additional confounding factors (methodological quality, control intervention, sample size, publication year, and place of publication). Conclusions in trials, classified into whether the experimental drug was recommended as the treatment of choice or not. The experimental drug was recommended as treatment of choice in 16% of trials funded by nonprofit organizations, 30% of trials not reporting funding, 35% of trials funded by both nonprofit and for-profit organizations, and 51% of trials funded by for-profit organizations (P<.001; chi2 test). Logistic regression analyses indicated that funding, treatment effect, and double blinding were the only significant predictors of conclusions. Adjusted analyses showed that trials funded by for-profit organizations were significantly more likely to recommend the experimental drug as treatment of choice (odds ratio, 5.3; 95% confidence interval, 2.0-14.4) compared with trials funded by nonprofit organizations. This association did not appear to reflect treatment effect or adverse events. Conclusions in trials funded by for-profit organizations may be more positive due to biased interpretation of trial results. Readers should carefully evaluate whether conclusions in randomized trials are supported by data.

[Modeling of an influence of indicators of social stress on demographic processes in regions of the Russian Federation].

PubMed

Burkin, M M; Molchanova, E V

To assess an impact of indicators of social stress on demographic processes in regions of the Russian Federation using statistical methods. The data of Rosstat «Regions of Russia» and «Health care in Russia» were used as information base. Indicators of about 80 subjects of the Russian Federation (without autonomous areas) for the ten-year period (2005-2014) have been created in the form of the database consisting of the following blocks: medico-demographic situation, level of economic development of the territory and wellbeing of the population, development of social infrastructure, ecological and climatic conditions, scientific researches and innovations. In total, there were about 70 indicators. Panel data for 80 regions of Russia in 10 years, which combine both indicators of spatial type (cross-section data), and information on temporary ranks (time-series data), were used. Various models of regression according to the panel data have been realized: the integrated model of regression (pooled model), regression model with the fixed effects (fixed effect model), regression model with random effects (random effect model). Main demographic indicators (life expectancy, birth rate, mortality from the external reasons) are to a great extent connected with socio-economic factors. Social tension (social stress) caused by transition to market economy plays an important role. The integral assessment of the impact of the average per capita monetary income, incidence of alcoholism and alcoholic psychoses, criminality, sales volume of alcoholic beverages per capita and marriage relations on demographic indicators is presented. Results of modeling allow to define the priority directions in the field of development of mental health and psychotherapeutic services in the regions of the Russian Federation.

Do antipsychotics lead to cognitive impairment in dementia? A meta-analysis of randomised placebo-controlled trials.

PubMed

Wolf, Alexander; Leucht, Stefan; Pajonk, Frank-Gerald

2017-04-01

Behavioural and psychological symptoms in dementia (BPSD) are common and often treated with antipsychotics, which are known to have small efficacy and to cause many side effects. One potential side effect might be cognitive decline. We searched MEDLINE, Scopus, CENTRAL and www.ClincalStudyResult.org for randomized, double-blind, placebo-controlled trials using antipsychotics for treating BPSD and evaluated cognitive functioning. The studies identified were summarized in a meta-analysis with the standardized mean difference (SMD, Hedges's g) as the effect size. Meta-regression was additionally performed to identify associated factors. Ten studies provided data on the course of cognitive functioning. The random effects model of the pooled analysis showed a not significant effect (SMD = -0.065, 95 % CI -0.186 to 0.057, I 2  = 41 %). Meta-regression revealed a significant correlation between cognitive impairment and treatment duration (R 2  = 0.78, p < 0.02) as well as baseline MMSE (R 2  = 0.92, p < 0.005). These correlations depend on only two out of ten studies and should interpret cautiously.

Development of a Random Field Model for Gas Plume Detection in Multiple LWIR Images.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heasler, Patrick G.

This report develops a random field model that describes gas plumes in LWIR remote sensing images. The random field model serves as a prior distribution that can be combined with LWIR data to produce a posterior that determines the probability that a gas plume exists in the scene and also maps the most probable location of any plume. The random field model is intended to work with a single pixel regression estimator--a regression model that estimates gas concentration on an individual pixel basis.

Shrinkage Estimation of Varying Covariate Effects Based On Quantile Regression

PubMed Central

Peng, Limin; Xu, Jinfeng; Kutner, Nancy

2013-01-01

Varying covariate effects often manifest meaningful heterogeneity in covariate-response associations. In this paper, we adopt a quantile regression model that assumes linearity at a continuous range of quantile levels as a tool to explore such data dynamics. The consideration of potential non-constancy of covariate effects necessitates a new perspective for variable selection, which, under the assumed quantile regression model, is to retain variables that have effects on all quantiles of interest as well as those that influence only part of quantiles considered. Current work on l1-penalized quantile regression either does not concern varying covariate effects or may not produce consistent variable selection in the presence of covariates with partial effects, a practical scenario of interest. In this work, we propose a shrinkage approach by adopting a novel uniform adaptive LASSO penalty. The new approach enjoys easy implementation without requiring smoothing. Moreover, it can consistently identify the true model (uniformly across quantiles) and achieve the oracle estimation efficiency. We further extend the proposed shrinkage method to the case where responses are subject to random right censoring. Numerical studies confirm the theoretical results and support the utility of our proposals. PMID:25332515

Genetic analysis of partial egg production records in Japanese quail using random regression models.

PubMed

Abou Khadiga, G; Mahmoud, B Y F; Farahat, G S; Emam, A M; El-Full, E A

2017-08-01

The main objectives of this study were to detect the most appropriate random regression model (RRM) to fit the data of monthly egg production in 2 lines (selected and control) of Japanese quail and to test the consistency of different criteria of model choice. Data from 1,200 female Japanese quails for the first 5 months of egg production from 4 consecutive generations of an egg line selected for egg production in the first month (EP1) was analyzed. Eight RRMs with different orders of Legendre polynomials were compared to determine the proper model for analysis. All criteria of model choice suggested that the adequate model included the second-order Legendre polynomials for fixed effects, and the third-order for additive genetic effects and permanent environmental effects. Predictive ability of the best model was the highest among all models (ρ = 0.987). According to the best model fitted to the data, estimates of heritability were relatively low to moderate (0.10 to 0.17) showed a descending pattern from the first to the fifth month of production. A similar pattern was observed for permanent environmental effects with greater estimates in the first (0.36) and second (0.23) months of production than heritability estimates. Genetic correlations between separate production periods were higher (0.18 to 0.93) than their phenotypic counterparts (0.15 to 0.87). The superiority of the selected line over the control was observed through significant (P < 0.05) linear contrast estimates. Significant (P < 0.05) estimates of covariate effect (age at sexual maturity) showed a decreased pattern with greater impact on egg production in earlier ages (first and second months) than later ones. A methodology based on random regression animal models can be recommended for genetic evaluation of egg production in Japanese quail. © 2017 Poultry Science Association Inc.

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Extension of the Haseman-Elston regression model to longitudinal data.

PubMed

Won, Sungho; Elston, Robert C; Park, Taesung

2006-01-01

We propose an extension to longitudinal data of the Haseman and Elston regression method for linkage analysis. The proposed model is a mixed model having several random effects. As response variable, we investigate the sibship sample mean corrected cross-product (smHE) and the BLUP-mean corrected cross product (pmHE), comparing them with the original squared difference (oHE), the overall mean corrected cross-product (rHE), and the weighted average of the squared difference and the squared mean-corrected sum (wHE). The proposed model allows for the correlation structure of longitudinal data. Also, the model can test for gene x time interaction to discover genetic variation over time. The model was applied in an analysis of the Genetic Analysis Workshop 13 (GAW13) simulated dataset for a quantitative trait simulating systolic blood pressure. Independence models did not preserve the test sizes, while the mixed models with both family and sibpair random effects tended to preserve size well. Copyright 2006 S. Karger AG, Basel.

Inferring genetic parameters of lactation in Tropical Milking Criollo cattle with random regression test-day models.

PubMed

Santellano-Estrada, E; Becerril-Pérez, C M; de Alba, J; Chang, Y M; Gianola, D; Torres-Hernández, G; Ramírez-Valverde, R

2008-11-01

This study inferred genetic and permanent environmental variation of milk yield in Tropical Milking Criollo cattle and compared 5 random regression test-day models using Wilmink's function and Legendre polynomials. Data consisted of 15,377 test-day records from 467 Tropical Milking Criollo cows that calved between 1974 and 2006 in the tropical lowlands of the Gulf Coast of Mexico and in southern Nicaragua. Estimated heritabilities of test-day milk yields ranged from 0.18 to 0.45, and repeatabilities ranged from 0.35 to 0.68 for the period spanning from 6 to 400 d in milk. Genetic correlation between days in milk 10 and 400 was around 0.50 but greater than 0.90 for most pairs of test days. The model that used first-order Legendre polynomials for additive genetic effects and second-order Legendre polynomials for permanent environmental effects gave the smallest residual variance and was also favored by the Akaike information criterion and likelihood ratio tests.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach.

PubMed

Xu, Pengpeng; Huang, Helai; Dong, Ni; Wong, S C

2017-01-01

This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.

Practical application of cure mixture model for long-term censored survivor data from a withdrawal clinical trial of patients with major depressive disorder.

PubMed

Arano, Ichiro; Sugimoto, Tomoyuki; Hamasaki, Toshimitsu; Ohno, Yuko

2010-04-23

Survival analysis methods such as the Kaplan-Meier method, log-rank test, and Cox proportional hazards regression (Cox regression) are commonly used to analyze data from randomized withdrawal studies in patients with major depressive disorder. However, unfortunately, such common methods may be inappropriate when a long-term censored relapse-free time appears in data as the methods assume that if complete follow-up were possible for all individuals, each would eventually experience the event of interest. In this paper, to analyse data including such a long-term censored relapse-free time, we discuss a semi-parametric cure regression (Cox cure regression), which combines a logistic formulation for the probability of occurrence of an event with a Cox proportional hazards specification for the time of occurrence of the event. In specifying the treatment's effect on disease-free survival, we consider the fraction of long-term survivors and the risks associated with a relapse of the disease. In addition, we develop a tree-based method for the time to event data to identify groups of patients with differing prognoses (cure survival CART). Although analysis methods typically adapt the log-rank statistic for recursive partitioning procedures, the method applied here used a likelihood ratio (LR) test statistic from a fitting of cure survival regression assuming exponential and Weibull distributions for the latency time of relapse. The method is illustrated using data from a sertraline randomized withdrawal study in patients with major depressive disorder. We concluded that Cox cure regression reveals facts on who may be cured, and how the treatment and other factors effect on the cured incidence and on the relapse time of uncured patients, and that cure survival CART output provides easily understandable and interpretable information, useful both in identifying groups of patients with differing prognoses and in utilizing Cox cure regression models leading to meaningful interpretations.

Ethanolic extract of Artemisia aucheri induces regression of aorta wall fatty streaks in hypercholesterolemic rabbits.

PubMed

Asgary, S; Dinani, N Jafari; Madani, H; Mahzouni, P

2008-05-01

Artemisia aucheri is a native-growing plant which is widely used in Iranian traditional medicine. This study was designed to evaluate the effects of A. aucheri on regression of atherosclerosis in hypercholesterolemic rabbits. Twenty five rabbits were randomly divided into five groups of five each and treated 3-months as follows: 1: normal diet, 2: hypercholesterolemic diet (HCD), 3 and 4: HCD for 60 days and then normal diet and normal diet + A. aucheri (100 mg x kg(-1) x day(-1)) respectively for an additional 30 days (regression period). In the regression period dietary use of A. aucheri in group 4 significantly decreased total cholesterol, triglyceride and LDL-cholesterol, while HDL-cholesterol was significantly increased. The atherosclerotic area was significantly decreased in this group. Animals, which received only normal diet in the regression period showed no regression but rather progression of atherosclerosis. These findings suggest that A. aucheri may cause regression of atherosclerotic lesions.

Lipid profile changes after pomegranate consumption: A systematic review and meta-analysis of randomized controlled trials.

PubMed

Sahebkar, Amirhossein; Simental-Mendía, Luis E; Giorgini, Paolo; Ferri, Claudio; Grassi, Davide

2016-10-15

Transport of oxidized low-density lipoprotein across the endothelium into the artery wall is considered a fundamental priming step for the atherosclerotic process. Recent studies reported potential therapeutic effects of micronutrients found in natural products, indicating positive applications for controlling the pathogenesis of chronic cardiovascular disease driven by cardiovascular risk factors and oxidative stress. A particular attention has been recently addressed to pomegranate; however findings of clinical studies have been contrasting. To evaluate the effects of pomegranate consumption on plasma lipid concentrations through a systematic review and meta-analysis of randomized controlled trials (RCTs). The study was designed according to the preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement. Scopus and Medline databases were searched to identify randomized placebo-controlled trials investigating the impact of pomegranate on plasma lipid concentrations. A fixed-effects model and the generic inverse variance method were used for quantitative data synthesis. Sensitivity analysis was conducted using the one-study remove approach. Random-effects meta-regression was performed to assess the impact of potential confounders on the estimated effect sizes. A total of 545 individuals were recruited from the 12 RCTs. Fixed-effect meta-analysis of data from 12 RCTs (13 treatment arms) did not show any significant effect of pomegranate consumption on plasma lipid concentrations. The results of meta-regression did not suggest any significant association between duration of supplementation and impact of pomegranate on total cholesterol and HDL-C, while an inverse association was found with changes in triglycerides levels (slope: -1.07; 95% CI: -2.03 to -0.11; p = 0.029). There was no association between the amount of pomegranate juice consumed per day and respective changes in plasma total cholesterol, LDL-C, HDL-C and triglycerides. The present meta-analysis of RCTs did not suggest any effect of pomegranate consumption on lipid profile in human. Copyright © 2016. Published by Elsevier GmbH.

Designing Studies That Would Address the Multilayered Nature of Health Care

PubMed Central

Pennell, Michael; Rhoda, Dale; Hade, Erinn M.; Paskett, Electra D.

2010-01-01

We review design and analytic methods available for multilevel interventions in cancer research with particular attention to study design, sample size requirements, and potential to provide statistical evidence for causal inference. The most appropriate methods will depend on the stage of development of the research and whether randomization is possible. Early on, fractional factorial designs may be used to screen intervention components, particularly when randomization of individuals is possible. Quasi-experimental designs, including time-series and multiple baseline designs, can be useful once the intervention is designed because they require few sites and can provide the preliminary evidence to plan efficacy studies. In efficacy and effectiveness studies, group-randomized trials are preferred when randomization is possible and regression discontinuity designs are preferred otherwise if assignment based on a quantitative score is possible. Quasi-experimental designs may be used, especially when combined with recent developments in analytic methods to reduce bias in effect estimates. PMID:20386057

Analysis of Machine Learning Techniques for Heart Failure Readmissions.

PubMed

Mortazavi, Bobak J; Downing, Nicholas S; Bucholz, Emily M; Dharmarajan, Kumar; Manhapra, Ajay; Li, Shu-Xia; Negahban, Sahand N; Krumholz, Harlan M

2016-11-01

The current ability to predict readmissions in patients with heart failure is modest at best. It is unclear whether machine learning techniques that address higher dimensional, nonlinear relationships among variables would enhance prediction. We sought to compare the effectiveness of several machine learning algorithms for predicting readmissions. Using data from the Telemonitoring to Improve Heart Failure Outcomes trial, we compared the effectiveness of random forests, boosting, random forests combined hierarchically with support vector machines or logistic regression (LR), and Poisson regression against traditional LR to predict 30- and 180-day all-cause readmissions and readmissions because of heart failure. We randomly selected 50% of patients for a derivation set, and a validation set comprised the remaining patients, validated using 100 bootstrapped iterations. We compared C statistics for discrimination and distributions of observed outcomes in risk deciles for predictive range. In 30-day all-cause readmission prediction, the best performing machine learning model, random forests, provided a 17.8% improvement over LR (mean C statistics, 0.628 and 0.533, respectively). For readmissions because of heart failure, boosting improved the C statistic by 24.9% over LR (mean C statistic 0.678 and 0.543, respectively). For 30-day all-cause readmission, the observed readmission rates in the lowest and highest deciles of predicted risk with random forests (7.8% and 26.2%, respectively) showed a much wider separation than LR (14.2% and 16.4%, respectively). Machine learning methods improved the prediction of readmission after hospitalization for heart failure compared with LR and provided the greatest predictive range in observed readmission rates. © 2016 American Heart Association, Inc.

Modeling Longitudinal Data Containing Non-Normal Within Subject Errors

NASA Technical Reports Server (NTRS)

Feiveson, Alan; Glenn, Nancy L.

2013-01-01

The mission of the National Aeronautics and Space Administration’s (NASA) human research program is to advance safe human spaceflight. This involves conducting experiments, collecting data, and analyzing data. The data are longitudinal and result from a relatively few number of subjects; typically 10 – 20. A longitudinal study refers to an investigation where participant outcomes and possibly treatments are collected at multiple follow-up times. Standard statistical designs such as mean regression with random effects and mixed–effects regression are inadequate for such data because the population is typically not approximately normally distributed. Hence, more advanced data analysis methods are necessary. This research focuses on four such methods for longitudinal data analysis: the recently proposed linear quantile mixed models (lqmm) by Geraci and Bottai (2013), quantile regression, multilevel mixed–effects linear regression, and robust regression. This research also provides computational algorithms for longitudinal data that scientists can directly use for human spaceflight and other longitudinal data applications, then presents statistical evidence that verifies which method is best for specific situations. This advances the study of longitudinal data in a broad range of applications including applications in the sciences, technology, engineering and mathematics fields.

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat.

PubMed

Nachit, M M; Nachit, G; Ketata, H; Gauch, H G; Zobel, R W

1992-03-01

The joint durum wheat (Triticum turgidum L var 'durum') breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environmental regressions, and by Additive Main effects and the Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.

Solvency supervision based on a total balance sheet approach

NASA Astrophysics Data System (ADS)

Pitselis, Georgios

2009-11-01

In this paper we investigate the adequacy of the own funds a company requires in order to remain healthy and avoid insolvency. Two methods are applied here; the quantile regression method and the method of mixed effects models. Quantile regression is capable of providing a more complete statistical analysis of the stochastic relationship among random variables than least squares estimation. The estimated mixed effects line can be considered as an internal industry equation (norm), which explains a systematic relation between a dependent variable (such as own funds) with independent variables (e.g. financial characteristics, such as assets, provisions, etc.). The above two methods are implemented with two data sets.

Graphical Models for Quasi-Experimental Designs

ERIC Educational Resources Information Center

Steiner, Peter M.; Kim, Yongnam; Hall, Courtney E.; Su, Dan

2017-01-01

Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand…

Cross-Sectional HIV Incidence Surveillance: A Benchmarking of Approaches for Estimating the 'Mean Duration of Recent Infection'.

PubMed

Kassanjee, Reshma; De Angelis, Daniela; Farah, Marian; Hanson, Debra; Labuschagne, Jan Phillipus Lourens; Laeyendecker, Oliver; Le Vu, Stéphane; Tom, Brian; Wang, Rui; Welte, Alex

2017-03-01

The application of biomarkers for 'recent' infection in cross-sectional HIV incidence surveillance requires the estimation of critical biomarker characteristics. Various approaches have been employed for using longitudinal data to estimate the Mean Duration of Recent Infection (MDRI) - the average time in the 'recent' state. In this systematic benchmarking of MDRI estimation approaches, a simulation platform was used to measure accuracy and precision of over twenty approaches, in thirty scenarios capturing various study designs, subject behaviors and test dynamics that may be encountered in practice. Results highlight that assuming a single continuous sojourn in the 'recent' state can produce substantial bias. Simple interpolation provides useful MDRI estimates provided subjects are tested at regular intervals. Regression performs the best - while 'random effects' describe the subject-clustering in the data, regression models without random effects proved easy to implement, stable, and of similar accuracy in scenarios considered; robustness to parametric assumptions was improved by regressing 'recent'/'non-recent' classifications rather than continuous biomarker readings. All approaches were vulnerable to incorrect assumptions about subjects' (unobserved) infection times. Results provided show the relationships between MDRI estimation performance and the number of subjects, inter-visit intervals, missed visits, loss to follow-up, and aspects of biomarker signal and noise.

Effect of soy isoflavone supplementation on plasma lipoprotein(a) concentrations: A meta-analysis.

PubMed

Simental-Mendía, Luis E; Gotto, Antonio M; Atkin, Stephen L; Banach, Maciej; Pirro, Matteo; Sahebkar, Amirhossein

Soy supplementation has been shown to reduce total and low-density lipoprotein cholesterol, while increasing high-density lipoprotein cholesterol. However, contradictory effects of soy isoflavone supplementation on lipoprotein(a) [Lp(a)] have been reported suggesting the need for a meta-analysis to be undertaken. The aim of the study was to investigate the impact of supplementation with soy isoflavones on plasma Lp(a) levels through a systematic review and meta-analysis of eligible randomized placebo-controlled trials. The search included PubMed-Medline, Scopus, ISI Web of Knowledge, and Google Scholar databases (by March 26, 2017), and quality of studies was evaluated according to Cochrane criteria. Quantitative data synthesis was performed using a random-effects model, with standardized mean difference and 95% confidence interval as summary statistics. Meta-regression and leave-one-out sensitivity analysis were performed to assess the modifiers of treatment response. Ten eligible studies comprising 11 treatment arms with 973 subjects were selected for the meta-analysis. Meta-analysis did not suggest any significant alteration of plasma Lp(a) levels after supplementation with soy isoflavones (standardized mean difference: 0.08, 95% confidence interval: -0.05, 0.20, P = .228). The effect size was robust in the leave-one-out sensitivity analysis. In meta-regression analysis, neither dose nor duration of supplementation with soy isoflavones was significantly associated with the effect size. This meta-analysis of the 10 available randomized placebo-controlled trials revealed no significant effect of soy isoflavones treatment on plasma Lp(a) concentrations. Copyright © 2017 National Lipid Association. Published by Elsevier Inc. All rights reserved.

The Influence of Neighborhood Characteristics and Parenting Practices on Academic Problems and Aggression Outcomes among Moderately to Highly Aggressive Children

ERIC Educational Resources Information Center

Barry, Tammy D.; Lochman, John E.; Fite, Paula J.; Wells, Karen C.; Colder, Craig R.

2012-01-01

The current study utilized a longitudinal design to examine the effects of neighborhood and parenting on 120 at-risk children's academic and aggressive outcomes, concurrently and at two later timepoints during the transition to middle school. Random effects regression models were estimated to examine whether neighborhood characteristics and harsh…

[Using fractional polynomials to estimate the safety threshold of fluoride in drinking water].

PubMed

Pan, Shenling; An, Wei; Li, Hongyan; Yang, Min

2014-01-01

To study the dose-response relationship between fluoride content in drinking water and prevalence of dental fluorosis on the national scale, then to determine the safety threshold of fluoride in drinking water. Meta-regression analysis was applied to the 2001-2002 national endemic fluorosis survey data of key wards. First, fractional polynomial (FP) was adopted to establish fixed effect model, determining the best FP structure, after that restricted maximum likelihood (REML) was adopted to estimate between-study variance, then the best random effect model was established. The best FP structure was first-order logarithmic transformation. Based on the best random effect model, the benchmark dose (BMD) of fluoride in drinking water and its lower limit (BMDL) was calculated as 0.98 mg/L and 0.78 mg/L. Fluoride in drinking water can only explain 35.8% of the variability of the prevalence, among other influencing factors, ward type was a significant factor, while temperature condition and altitude were not. Fractional polynomial-based meta-regression method is simple, practical and can provide good fitting effect, based on it, the safety threshold of fluoride in drinking water of our country is determined as 0.8 mg/L.

Moderating effect of intrinsic religiosity on the relationship between depression and cognitive function among community-dwelling older adults.

PubMed

Foong, Hui Foh; Hamid, Tengku Aizan; Ibrahim, Rahimah; Haron, Sharifah Azizah

2018-04-01

Research has found that depression in later life is associated with cognitive impairment. Thus, the mechanism to reduce the effect of depression on cognitive function is warranted. In this paper, we intend to examine whether intrinsic religiosity mediates the association between depression and cognitive function. The study included 2322 nationally representative community-dwelling elderly in Malaysia, randomly selected through a multi-stage proportional cluster random sampling from Peninsular Malaysia. The elderly were surveyed on socio-demographic information, cognitive function, depression and intrinsic religiosity. A four-step moderated hierarchical regression analysis was employed to test the moderating effect. Statistical analyses were performed using SPSS (version 15.0). Bivariate analyses showed that both depression and intrinsic religiosity had significant relationships with cognitive function. In addition, four-step moderated hierarchical regression analysis revealed that the intrinsic religiosity moderated the association between depression and cognitive function, after controlling for selected socio-demographic characteristics. Intrinsic religiosity might reduce the negative effect of depression on cognitive function. Professionals who are working with depressed older adults should seek ways to improve their intrinsic religiosity as one of the strategies to prevent cognitive impairment.

Generalized and synthetic regression estimators for randomized branch sampling

Treesearch

David L. R. Affleck; Timothy G. Gregoire

2015-01-01

In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (...

Effect of Risk of Bias on the Effect Size of Meta-Analytic Estimates in Randomized Controlled Trials in Periodontology and Implant Dentistry

PubMed Central

Faggion, Clovis Mariano; Wu, Yun-Chun; Scheidgen, Moritz; Tu, Yu-Kang

2015-01-01

Background Risk of bias (ROB) may threaten the internal validity of a clinical trial by distorting the magnitude of treatment effect estimates, although some conflicting information on this assumption exists. Objective The objective of this study was evaluate the effect of ROB on the magnitude of treatment effect estimates in randomized controlled trials (RCTs) in periodontology and implant dentistry. Methods A search for Cochrane systematic reviews (SRs), including meta-analyses of RCTs published in periodontology and implant dentistry fields, was performed in the Cochrane Library in September 2014. Random-effect meta-analyses were performed by grouping RCTs with different levels of ROBs in three domains (sequence generation, allocation concealment, and blinding of outcome assessment). To increase power and precision, only SRs with meta-analyses including at least 10 RCTs were included. Meta-regression was performed to investigate the association between ROB characteristics and the magnitudes of intervention effects in the meta-analyses. Results Of the 24 initially screened SRs, 21 SRs were excluded because they did not include at least 10 RCTs in the meta-analyses. Three SRs (two from periodontology field) generated information for conducting 27 meta-analyses. Meta-regression did not reveal significant differences in the relationship of the ROB level with the size of treatment effect estimates, although a trend for inflated estimates was observed in domains with unclear ROBs. Conclusion In this sample of RCTs, high and (mainly) unclear risks of selection and detection biases did not seem to influence the size of treatment effect estimates, although several confounders might have influenced the strength of the association. PMID:26422698

Comparative effectiveness research in cancer with observational data.

PubMed

Giordano, Sharon H

2015-01-01

Observational studies are increasingly being used for comparative effectiveness research. These studies can have the greatest impact when randomized trials are not feasible or when randomized studies have not included the population or outcomes of interest. However, careful attention must be paid to study design to minimize the likelihood of selection biases. Analytic techniques, such as multivariable regression modeling, propensity score analysis, and instrumental variable analysis, also can also be used to help address confounding. Oncology has many existing large and clinically rich observational databases that can be used for comparative effectiveness research. With careful study design, observational studies can produce valid results to assess the benefits and harms of a treatment or intervention in representative real-world populations.

The creation and evaluation of a model to simulate the probability of conception in seasonal-calving pasture-based dairy heifers.

PubMed

Fenlon, Caroline; O'Grady, Luke; Butler, Stephen; Doherty, Michael L; Dunnion, John

2017-01-01

Herd fertility in pasture-based dairy farms is a key driver of farm economics. Models for predicting nulliparous reproductive outcomes are rare, but age, genetics, weight, and BCS have been identified as factors influencing heifer conception. The aim of this study was to create a simulation model of heifer conception to service with thorough evaluation. Artificial Insemination service records from two research herds and ten commercial herds were provided to build and evaluate the models. All were managed as spring-calving pasture-based systems. The factors studied were related to age, genetics, and time of service. The data were split into training and testing sets and bootstrapping was used to train the models. Logistic regression (with and without random effects) and generalised additive modelling were selected as the model-building techniques. Two types of evaluation were used to test the predictive ability of the models: discrimination and calibration. Discrimination, which includes sensitivity, specificity, accuracy and ROC analysis, measures a model's ability to distinguish between positive and negative outcomes. Calibration measures the accuracy of the predicted probabilities with the Hosmer-Lemeshow goodness-of-fit, calibration plot and calibration error. After data cleaning and the removal of services with missing values, 1396 services remained to train the models and 597 were left for testing. Age, breed, genetic predicted transmitting ability for calving interval, month and year were significant in the multivariate models. The regression models also included an interaction between age and month. Year within herd was a random effect in the mixed regression model. Overall prediction accuracy was between 77.1% and 78.9%. All three models had very high sensitivity, but low specificity. The two regression models were very well-calibrated. The mean absolute calibration errors were all below 4%. Because the models were not adept at identifying unsuccessful services, they are not suggested for use in predicting the outcome of individual heifer services. Instead, they are useful for the comparison of services with different covariate values or as sub-models in whole-farm simulations. The mixed regression model was identified as the best model for prediction, as the random effects can be ignored and the other variables can be easily obtained or simulated.

Off-pump versus on-pump coronary artery bypass surgery: meta-analysis and meta-regression of 13,524 patients from randomized trials.

PubMed

Sá, Michel Pompeu Barros de Oliveira; Ferraz, Paulo Ernando; Escobar, Rodrigo Renda; Martins, Wendell Nunes; Lustosa, Pablo César; Nunes, Eliobas de Oliveira; Vasconcelos, Frederico Pires; Lima, Ricardo Carvalho

2012-12-01

Most recent published meta-analysis of randomized controlled trials (RCTs) showed that off-pump coronary artery bypass graft surgery (CABG) reduces incidence of stroke by 30% compared with on-pump CABG, but showed no difference in other outcomes. New RCTs were published, indicating need of new meta-analysis to investigate pooled results adding these further studies. MEDLINE, EMBASE, CENTRAL/CCTR, SciELO, LILACS, Google Scholar and reference lists of relevant articles were searched for RCTs that compared outcomes (30-day mortality for all-cause, myocardial infarction or stroke) between off-pump versus on-pump CABG until May 2012. The principal summary measures were relative risk (RR) with 95% Confidence Interval (CI) and P values (considered statistically significant when <0.05). The RR's were combined across studies using DerSimonian-Laird random effects weighted model. Meta-analysis and meta-regression were completed using the software Comprehensive Meta-Analysis version 2 (Biostat Inc., Englewood, New Jersey, USA). Forty-seven RCTs were identified and included 13,524 patients (6,758 for off-pump and 6,766 for on-pump CABG). There was no significant difference between off-pump and on-pump CABG groups in RR for 30-day mortality or myocardial infarction, but there was difference about stroke in favor to off-pump CABG (RR 0.793, 95% CI 0.660-0.920, P=0.049). It was observed no important heterogeneity of effects about any outcome, but it was observed publication bias about outcome "stroke". Meta-regression did not demonstrate influence of female gender, number of grafts or age in outcomes. Off-pump CABG reduces the incidence of post-operative stroke by 20.7% and has no substantial effect on mortality or myocardial infarction in comparison to on-pump CABG. Patient gender, number of grafts performed and age do not seem to explain the effect of off-pump CABG on mortality, myocardial infarction or stroke, respectively.

Metabolic Profiling of Adiponectin Levels in Adults: Mendelian Randomization Analysis.

PubMed

Borges, Maria Carolina; Barros, Aluísio J D; Ferreira, Diana L Santos; Casas, Juan Pablo; Horta, Bernardo Lessa; Kivimaki, Mika; Kumari, Meena; Menon, Usha; Gaunt, Tom R; Ben-Shlomo, Yoav; Freitas, Deise F; Oliveira, Isabel O; Gentry-Maharaj, Aleksandra; Fourkala, Evangelia; Lawlor, Debbie A; Hingorani, Aroon D

2017-12-01

Adiponectin, a circulating adipocyte-derived protein, has insulin-sensitizing, anti-inflammatory, antiatherogenic, and cardiomyocyte-protective properties in animal models. However, the systemic effects of adiponectin in humans are unknown. Our aims were to define the metabolic profile associated with higher blood adiponectin concentration and investigate whether variation in adiponectin concentration affects the systemic metabolic profile. We applied multivariable regression in ≤5909 adults and Mendelian randomization (using cis -acting genetic variants in the vicinity of the adiponectin gene as instrumental variables) for analyzing the causal effect of adiponectin in the metabolic profile of ≤37 545 adults. Participants were largely European from 6 longitudinal studies and 1 genome-wide association consortium. In the multivariable regression analyses, higher circulating adiponectin was associated with higher high-density lipoprotein lipids and lower very-low-density lipoprotein lipids, glucose levels, branched-chain amino acids, and inflammatory markers. However, these findings were not supported by Mendelian randomization analyses for most metabolites. Findings were consistent between sexes and after excluding high-risk groups (defined by age and occurrence of previous cardiovascular event) and 1 study with admixed population. Our findings indicate that blood adiponectin concentration is more likely to be an epiphenomenon in the context of metabolic disease than a key determinant. © 2017 The Authors.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

PubMed

Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao

2016-07-12

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

Optimal Design for Two-Level Random Assignment and Regression Discontinuity Studies

ERIC Educational Resources Information Center

Rhoads, Christopher H.; Dye, Charles

2016-01-01

An important concern when planning research studies is to obtain maximum precision of an estimate of a treatment effect given a budget constraint. When research designs have a "multilevel" or "hierarchical" structure changes in sample size at different levels of the design will impact precision differently. Furthermore, there…

Reiki therapy for postoperative oral pain in pediatric patients: pilot data from a double-blind, randomized clinical trial.

PubMed

Kundu, Anjana; Lin, Yuting; Oron, Assaf P; Doorenbos, Ardith Z

2014-02-01

To examine the effects of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. This was a double-blind, randomized controlled study of children undergoing dental procedures. Participants were randomly assigned to receive either Reiki therapy or the control therapy (sham Reiki) preoperatively. Postoperative pain scores, opioid requirements, and side effects were assessed. Family members were also asked about perioperative care satisfaction. Multiple linear regressions were used for analysis. Thirty-eight children participated. The blinding procedure was successful. No statistically significant difference was observed between groups on all outcome measures. Our study provides a successful example of a blinding procedure for Reiki therapy among children in the perioperative period. This study does not support the effectiveness of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. Copyright © 2013 Elsevier Ltd. All rights reserved.

Reiki therapy for postoperative oral pain in pediatric patients: Pilot data from a double-blind, randomized clinical trial

PubMed Central

Kundu, Anjana; Lin, Yuting; Oron, Assaf P.; Doorenbos, Ardith Z.

2014-01-01

Purpose To examine the effects of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. Methods This was a double-blind, randomized controlled study of children undergoing dental procedures. Participants were randomly assigned to receive either Reiki therapy or the control therapy (sham Reiki) preoperatively. Postoperative pain scores, opioid requirements, and side effects were assessed. Family members were also asked about perioperative care satisfaction. Multiple linear regressions were used for analysis. Results Thirty-eight children participated. The blinding procedure was successful. No statistically significant difference was observed between groups on all outcome measures. Implications Our study provides a successful example of a blinding procedure for Reiki therapy among children in the perioperative period. This study does not support the effectiveness of Reiki as an adjuvant therapy to opioid therapy for postoperative pain control in pediatric patients. PMID:24439640

Using Structured Additive Regression Models to Estimate Risk Factors of Malaria: Analysis of 2010 Malawi Malaria Indicator Survey Data

PubMed Central

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

Background After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. Methods We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Results Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. Conclusions The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities. PMID:24991915

Using structured additive regression models to estimate risk factors of malaria: analysis of 2010 Malawi malaria indicator survey data.

PubMed

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities.

Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

PubMed

Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

2003-11-01

Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

An Introduction to Recursive Partitioning: Rationale, Application, and Characteristics of Classification and Regression Trees, Bagging, and Random Forests

ERIC Educational Resources Information Center

Strobl, Carolin; Malley, James; Tutz, Gerhard

2009-01-01

Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and…

Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression

Treesearch

Jeffrey T. Walton

2008-01-01

Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (...

Baseline adjustments for binary data in repeated cross-sectional cluster randomized trials.

PubMed

Nixon, R M; Thompson, S G

2003-09-15

Analysis of covariance models, which adjust for a baseline covariate, are often used to compare treatment groups in a controlled trial in which individuals are randomized. Such analysis adjusts for any baseline imbalance and usually increases the precision of the treatment effect estimate. We assess the value of such adjustments in the context of a cluster randomized trial with repeated cross-sectional design and a binary outcome. In such a design, a new sample of individuals is taken from the clusters at each measurement occasion, so that baseline adjustment has to be at the cluster level. Logistic regression models are used to analyse the data, with cluster level random effects to allow for different outcome probabilities in each cluster. We compare the estimated treatment effect and its precision in models that incorporate a covariate measuring the cluster level probabilities at baseline and those that do not. In two data sets, taken from a cluster randomized trial in the treatment of menorrhagia, the value of baseline adjustment is only evident when the number of subjects per cluster is large. We assess the generalizability of these findings by undertaking a simulation study, and find that increased precision of the treatment effect requires both large cluster sizes and substantial heterogeneity between clusters at baseline, but baseline imbalance arising by chance in a randomized study can always be effectively adjusted for. Copyright 2003 John Wiley & Sons, Ltd.

Influence of Resistance Exercise on Lean Body Mass in Aging Adults: A Meta-Analysis

PubMed Central

Peterson, Mark D.; Sen, Ananda; Gordon, Paul M.

2010-01-01

Purpose Sarcopenia plays a principal role in the pathogenesis of frailty and functional impairment that occurs with aging. There are few published accounts which examine the overall benefit of resistance exercise (RE) for lean body mass (LBM), while considering a continuum of dosage schemes and/or age ranges. Therefore the purpose of this meta-analysis was to determine the effects of RE on LBM in older men and women, while taking these factors into consideration. Methods This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. Randomized controlled trials and randomized or non-randomized studies among adults ≥ 50 years, were included. Heterogeneity between studies was assessed using the Cochran Q and I2 statistics, and publication bias was evaluated through physical inspection of funnel plots as well as formal rank-correlation statistics. Mixed-effects meta-regression was incorporated to assess the relationship between RE dosage and changes in LBM. Results Data from forty-nine studies, representing a total of 1328 participants were pooled using random-effect models. Results demonstrated a positive effect for lean body mass and there was no evidence of publication bias. The Cochran Q statistic for heterogeneity was 497.8, which was significant (p < 0.01). Likewise, I2 was equal to 84%, representing rejection of the null hypothesis of homogeneity. The weighted pooled estimate of mean lean body mass change was 1.1 kg (95% CI, 0.9 kg to 1.2 kg). Meta-regression revealed that higher volume interventions were associated (β = 0.05, p < 0.01) with significantly greater increases in lean body mass, whereas older individuals experienced less increase (β = -0.03, p = 0.01). Conclusions RE is effective for eliciting gains in lean body mass among aging adults, particularly with higher volume programs. Findings suggest that RE participation earlier in life may provide superior effectiveness. PMID:20543750

Improved amputation-free survival in unreconstructable critical limb ischemia and its implications for clinical trial design and quality measurement.

PubMed

Benoit, Eric; O'Donnell, Thomas F; Kitsios, Georgios D; Iafrati, Mark D

2012-03-01

Amputation-free survival (AFS), a composite endpoint of mortality and amputation, is the preferred outcome measure in critical limb ischemia (CLI). Given the improvements in systemic management of atherosclerosis and interventional management of limb ischemia over the past 2 decades, we examined whether these outcomes have changed in patients with CLI without revascularization options (no option-critical limb ischemia [NO-CLI]). We reviewed the literature for published 1-year AFS, mortality, and amputation rates from control groups in NO-CLI trials. Summary proportions of events were estimated by conducting a random effects meta-analysis of proportions. To determine whether there had been any change in event rates over time, we performed a random effects meta-regression and a mixed effects logistic regression, both regressed against the variable "final year of recruitment." Eleven trials consisting of 886 patients satisfied search criteria, 7 of which presented AFS data. Summary proportion of events (95% confidence interval) were 0.551 (0.399 to 0.693) for AFS; 0.198 (0.116 to 0.317) for death; and 0.341 (0.209 to 0.487) for amputation. Regression analyses demonstrated that AFS has risen over time as mortality rates have fallen, and these improvements are statistically significant. The decrease in amputation rates failed to reach statistical significance. The lack of published data precluded a quantitative evaluation of any change in the clinical severity or comorbidities in the NO-CLI population. AFS and mortality rates in NO-CLI have improved over the past 2 decades. Due to declining event rates, clinical trials may underestimate treatment effects and thus fail to reach statistical significance unless sample sizes are increased or unless a subgroup with a higher event rate can be identified. Alternatively, comparing outcomes to historical values for quality measurement may overestimate treatment effects. Benchmark values of AFS and morality require periodic review and updating. Copyright © 2012 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

[Studies of marker screening efficiency and corresponding influencing factors in QTL composite interval mapping].

PubMed

Gao, Yong-Ming; Wan, Ping

2002-06-01

Screening markers efficiently is the foundation of mapping QTLs by composite interval mapping. Main and interaction markers distinguished, besides using background control for genetic variation, could also be used to construct intervals of two-way searching for mapping QTLs with epistasis, which can save a lot of calculation time. Therefore, the efficiency of marker screening would affect power and precision of QTL mapping. A doubled haploid population with 200 individuals and 5 chromosomes was constructed, with 50 markers evenly distributed at 10 cM space. Among a total of 6 QTLs, one was placed on chromosome I, two linked on chromosome II, and the other three linked on chromosome IV. QTL setting included additive effects and epistatic effects of additive x additive, the corresponding QTL interaction effects were set if data were collected under multiple environments. The heritability was assumed to be 0.5 if no special declaration. The power of marker screening by stepwise regression, forward regression, and three methods for random effect prediction, e.g. best linear unbiased prediction (BLUP), linear unbiased prediction (LUP) and adjusted unbiased prediction (AUP), was studied and compared through 100 Monte Carlo simulations. The results indicated that the marker screening power by stepwise regression at 0.1, 0.05 and 0.01 significant level changed from 2% to 68%, the power changed from 2% to 72% by forward regression. The larger the QTL effects, the higher the marker screening power. While the power of marker screening by three random effect prediction was very low, the maximum was only 13%. That suggested that regression methods were much better than those by using the approaches of random effect prediction to identify efficient markers flanking QTLs, and forward selection method was more simple and efficient. The results of simulation study on heritability showed that heightening of both general heritability and interaction heritability of genotype x environments could enhance marker screening power, the former had a greater influence on QTLs with larger main and/or epistatic effects, while the later on QTLs with small main and/or epistatic effects. The simulation of 100 times was also conducted to study the influence of different marker number and density on marker screening power. It is indicated that the marker screening power would decrease if there were too many markers, especially with high density in a mapping population, which suggested that a mapping population with definite individuals could only hold limited markers. According to the simulation study, the reasonable number of markers should not be more than individuals. The simulation study of marker screening under multiple environments showed high total power of marker screening. In order to relieve the problem that marker screening power restricted the efficiency of QTL mapping, markers identified in multiple environments could be used to construct two search intervals.

Effect of vitamin E supplementation on serum C-reactive protein level: a meta-analysis of randomized controlled trials.

PubMed

Saboori, S; Shab-Bidar, S; Speakman, J R; Yousefi Rad, E; Djafarian, K

2015-08-01

C-reactive protein (CRP), a marker of chronic inflammation, has a major role in the etiology of chronic disease. Vitamin E may have anti-inflammatory effects. However, there is no consensus on the effects of vitamin E supplementation on CRP levels in clinical trials. The aim of this study was to systematically review randomized controlled trials (RCTs) that report on the effects of vitamin E supplementation (α- and γ-tocopherols) on CRP levels. A systematic search of RCTs was conducted on Medline and EMBASE through PubMed, Scopus, Ovid and Science Direct, and completed by a manual review of the literature up to May 2014. Pooled effects were estimated by using random-effects models and heterogeneity was assessed by Cochran's Q and I(2) tests. Subgroup analyses and meta-regression analyses were also performed according to intervention duration, dose of supplementation and baseline level of CRP. Of 4734 potentially relevant studies, only 12 trials met the inclusion criteria with 246 participants in the intervention arms and 249 participants in control arms. Pooled analysis showed a significant reduction in CRP levels of 0.62 mg/l (95% confidence interval = -0.92, -0.31; P < 0.001) in vitamin E-treated individuals, with the evidence of heterogeneity across studies. This significant effect was maintained in all subgroups, although the univariate meta-regression analysis showed that the vitamin E supplementation dose, baseline level of CRP and duration of intervention were not the sources of the observed heterogeneity. The results of this meta-analysis suggest that supplementation with vitamin E in the form of either α-tocopherol or γ-tocopherol would reduce serum CRP levels.

Effect of sour tea (Hibiscus sabdariffa L.) on arterial hypertension: a systematic review and meta-analysis of randomized controlled trials.

PubMed

Serban, Corina; Sahebkar, Amirhossein; Ursoniu, Sorin; Andrica, Florina; Banach, Maciej

2015-06-01

Hibiscus sabdariffa L. is a tropical wild plant rich in organic acids, polyphenols, anthocyanins, polysaccharides, and volatile constituents that are beneficial for the cardiovascular system. Hibiscus sabdariffa beverages are commonly consumed to treat arterial hypertension, yet the evidence from randomized controlled trials (RCTs) has not been fully conclusive. Therefore, we aimed to assess the potential antihypertensive effects of H. sabdariffa through systematic review of literature and meta-analysis of available RCTs. The search included PUBMED, Cochrane Library, Scopus, and EMBASE (up to July 2014) to identify RCTs investigating the efficacy of H. sabdariffa supplementation on SBP and DBP values. Two independent reviewers extracted data on the study characteristics, methods, and outcomes. Quantitative data synthesis and meta-regression were performed using a fixed-effect model, and sensitivity analysis using leave-one-out method. Five RCTs (comprising seven treatment arms) were selected for the meta-analysis. In total, 390 participants were randomized, of whom 225 were allocated to the H. sabdariffa supplementation group and 165 to the control group in the selected studies. Fixed-effect meta-regression indicated a significant effect of H. sabdariffa supplementation in lowering both SBP (weighed mean difference -7.58 mmHg, 95% confidence interval -9.69 to -5.46, P < 0.00001) and DBP (weighed mean difference -3.53 mmHg, 95% confidence interval -5.16 to -1.89, P < 0.0001). These effects were inversely associated with baseline BP values, and were robust in sensitivity analyses. This meta-analysis of RCTs showed a significant effect of H. sabdariffa in lowering both SBP and DBP. Further well designed trials are necessary to validate these results.

Calibrating random forests for probability estimation.

PubMed

Dankowski, Theresa; Ziegler, Andreas

2016-09-30

Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

MIXOR: a computer program for mixed-effects ordinal regression analysis.

PubMed

Hedeker, D; Gibbons, R D

1996-03-01

MIXOR provides maximum marginal likelihood estimates for mixed-effects ordinal probit, logistic, and complementary log-log regression models. These models can be used for analysis of dichotomous and ordinal outcomes from either a clustered or longitudinal design. For clustered data, the mixed-effects model assumes that data within clusters are dependent. The degree of dependency is jointly estimated with the usual model parameters, thus adjusting for dependence resulting from clustering of the data. Similarly, for longitudinal data, the mixed-effects approach can allow for individual-varying intercepts and slopes across time, and can estimate the degree to which these time-related effects vary in the population of individuals. MIXOR uses marginal maximum likelihood estimation, utilizing a Fisher-scoring solution. For the scoring solution, the Cholesky factor of the random-effects variance-covariance matrix is estimated, along with the effects of model covariates. Examples illustrating usage and features of MIXOR are provided.

Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression.

PubMed

Candel, Math J J M; Van Breukelen, Gerard J P

2010-06-30

Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most. (c) 2010 John Wiley & Sons, Ltd.

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

PubMed

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

Quality Quandaries: Predicting a Population of Curves

DOE PAGES

Fugate, Michael Lynn; Hamada, Michael Scott; Weaver, Brian Phillip

2017-12-19

We present a random effects spline regression model based on splines that provides an integrated approach for analyzing functional data, i.e., curves, when the shape of the curves is not parametrically specified. An analysis using this model is presented that makes inferences about a population of curves as well as features of the curves.

Quality Quandaries: Predicting a Population of Curves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fugate, Michael Lynn; Hamada, Michael Scott; Weaver, Brian Phillip

We present a random effects spline regression model based on splines that provides an integrated approach for analyzing functional data, i.e., curves, when the shape of the curves is not parametrically specified. An analysis using this model is presented that makes inferences about a population of curves as well as features of the curves.

Simulation-Extrapolation for Estimating Means and Causal Effects with Mismeasured Covariates

ERIC Educational Resources Information Center

Lockwood, J. R.; McCaffrey, Daniel F.

2015-01-01

Regression, weighting and related approaches to estimating a population mean from a sample with nonrandom missing data often rely on the assumption that conditional on covariates, observed samples can be treated as random. Standard methods using this assumption generally will fail to yield consistent estimators when covariates are measured with…

Digital Games, Design, and Learning: A Systematic Review and Meta-Analysis

ERIC Educational Resources Information Center

Clark, Douglas B.; Tanner-Smith, Emily E.; Killingsworth, Stephen S.

2016-01-01

In this meta-analysis, we systematically reviewed research on digital games and learning for K-16 students. We synthesized comparisons of game versus nongame conditions (i.e., media comparisons) and comparisons of augmented games versus standard game designs (i.e., value-added comparisons). We used random-effects meta-regression models with robust…

Safety and efficacy of antihypertensive prescription at emergency department discharge.

PubMed

Brody, Aaron; Rahman, Tahsin; Reed, Brian; Millis, Scott; Ference, Brian; Flack, John M; Levy, Phillip D

2015-05-01

Poor blood pressure (BP) control is a primary risk factor for target organ damage in the heart, brain, and kidney. Uncontrolled hypertension is common among emergency department (ED) patients, particularly in underresourced settings, but it is unclear what role ED providers should play in the management of chronic antihypertensive therapy. The objective was to evaluate the safety and efficacy of prescribing antihypertensive therapy from the ED. This was a retrospective study of data pooled from two prospective, longitudinal, randomized controlled trials, both of which enrolled ED patients with asymptomatic hypertension. Antihypertensives were prescribed at emergency physician discretion, and this was not related to randomization arm. Demographic data, BP at screening and randomization visit, and data on adverse effects potentially related to antihypertensive therapy were compiled. Means were compared using Student's t-tests, and proportions were compared using chi-square tests. The effect of antihypertensive therapy on BP control was further analyzed using multivariable regression modeling controlling for age, race, sex, hypertension history, study cohort, and ED BP. Data were abstracted for 217 subjects. The median interval from ED visit to randomization was 12 days. Seventy-six subjects (35%) received one or more prescriptions for antihypertensive therapy. Age, sex, race, hypertension history, and mean duration of hypertension were equivalent between groups. Although mean ED BP was higher among those who received prescriptions, the mean systolic BP (sBP) reduction from ED to randomization was significantly greater (difference = 19 mm Hg, 95% confidence interval = 12 to 26 mm Hg). No patient in either group had an sBP less than 100 mm Hg at randomization. On multiple regression modeling, randomization sBP reduction was independently associated with antihypertensive prescription (p = 0.001). The incidence of adverse effects was equivalent and low in both groups. No new neurological deficits, ischemic events, or life-threatening anaphylactic reactions were reported in either group. Prescription of antihypertensive medication from the ED is associated with significantly lower sBP at short-term outpatient follow-up. Antihypertensive therapy was not associated with an increased incidence of adverse events, and BP reduction did not exceed potentially harmful levels. Initiation of chronic antihypertensive therapy in the ED is safe and effective and may be a reasonable consideration for at-risk populations. © 2015 by the Society for Academic Emergency Medicine.

Is It the Intervention or the Students? Using Linear Regression to Control for Student Characteristics in Undergraduate STEM Education Research

PubMed Central

Theobald, Roddy; Freeman, Scott

2014-01-01

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due to the effect of an instructional intervention or to differences in student characteristics when students cannot be assigned to control and treatment groups at random. Using pre- and posttest scores from an introductory biology course, we illustrate how the methods currently in wide use can lead to erroneous conclusions, and how multiple linear regression offers an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. In general, we recommend that researchers always use student-level regression models that control for possible differences in student ability and preparation to estimate the effect of any nonrandomized instructional intervention on student performance. PMID:24591502

Is it the intervention or the students? using linear regression to control for student characteristics in undergraduate STEM education research.

PubMed

Theobald, Roddy; Freeman, Scott

2014-01-01

Although researchers in undergraduate science, technology, engineering, and mathematics education are currently using several methods to analyze learning gains from pre- and posttest data, the most commonly used approaches have significant shortcomings. Chief among these is the inability to distinguish whether differences in learning gains are due to the effect of an instructional intervention or to differences in student characteristics when students cannot be assigned to control and treatment groups at random. Using pre- and posttest scores from an introductory biology course, we illustrate how the methods currently in wide use can lead to erroneous conclusions, and how multiple linear regression offers an effective framework for distinguishing the impact of an instructional intervention from the impact of student characteristics on test score gains. In general, we recommend that researchers always use student-level regression models that control for possible differences in student ability and preparation to estimate the effect of any nonrandomized instructional intervention on student performance.

Not all that glitters is RMT in the forecasting of risk of portfolios in the Brazilian stock market

NASA Astrophysics Data System (ADS)

Sandoval, Leonidas; Bortoluzzo, Adriana Bruscato; Venezuela, Maria Kelly

2014-09-01

Using stocks of the Brazilian stock exchange (BM&F-Bovespa), we build portfolios of stocks based on Markowitz's theory and test the predicted and realized risks. This is done using the correlation matrices between stocks, and also using Random Matrix Theory in order to clean such correlation matrices from noise. We also calculate correlation matrices using a regression model in order to remove the effect of common market movements and their cleaned versions using Random Matrix Theory. This is done for years of both low and high volatility of the Brazilian stock market, from 2004 to 2012. The results show that the use of regression to subtract the market effect on returns greatly increases the accuracy of the prediction of risk, and that, although the cleaning of the correlation matrix often leads to portfolios that better predict risks, in periods of high volatility of the market this procedure may fail to do so. The results may be used in the assessment of the true risks when one builds a portfolio of stocks during periods of crisis.

Computational simulation of coupled material degradation processes for probabilistic lifetime strength of aerospace materials

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.

1992-01-01

The research included ongoing development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primative variables. These primative variable may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above described constitutive equation using actual experimental materials data together with linear regression of that data, thereby predicting values for the empirical material constraints for each effect or primative variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from the open literature for materials typically of interest to those studying aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Effect of oral isoflavone supplementation on vascular endothelial function in postmenopausal women: a meta-analysis of randomized placebo-controlled trials.

PubMed

Li, Shao-Hua; Liu, Xu-Xia; Bai, Yong-Yi; Wang, Xiao-Jian; Sun, Kai; Chen, Jing-Zhou; Hui, Ru-Tai

2010-02-01

The effect of isoflavone on endothelial function in postmenopausal women is controversial. The objective of this study was to evaluate the effect of oral isoflavone supplementation on endothelial function, as measured by flow-mediated dilation (FMD), in postmenopausal women. A meta-analysis of randomized placebo-controlled trials was conducted to evaluate the effect of oral isoflavone supplementation on endothelial function in postmenopausal women. Trials were searched in PubMed, Embase, the Cochrane Library database, and reviews and reference lists of relevant articles. Summary estimates of weighted mean differences (WMDs) and 95% CIs were obtained by using random-effects models. Meta-regression and subgroup analyses were performed to identify the source of heterogeneity. A total of 9 trials were reviewed in the present meta-analysis. Overall, the results of the 9 trials showed that isoflavone significantly increased FMD (WMD: 1.75%; 95% CI: 0.83%, 2.67%; P = 0.0002). Meta-regression analysis indicated that the age-adjusted baseline FMD was inversely related to effect size. Subgroup analysis showed that oral supplementation of isoflavone had no influence on FMD if the age-adjusted baseline FMD was > or = 5.2% (4 trials; WMD: 0.24%; 95% CI: -0.94%, 1.42%; P = 0.69). This improvement seemed to be significant when the age-adjusted baseline FMD levels were <5.2% (5 trials; WMD: 2.22%; 95% CI: 1.15%, 3.30%; P < 0.0001), although significant heterogeneity was still detected in this low-baseline-FMD subgroup. Oral isoflavone supplementation does not improve endothelial function in postmenopausal women with high baseline FMD levels but leads to significant improvement in women with low baseline FMD levels.

Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.

PubMed

Bennett, Bradley C; Husby, Chad E

2008-03-28

Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws. We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador). We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families. Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels. Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.

Predictors of exercise capacity following exercise-based rehabilitation in patients with coronary heart disease and heart failure: A meta-regression analysis.

PubMed

Uddin, Jamal; Zwisler, Ann-Dorthe; Lewinter, Christian; Moniruzzaman, Mohammad; Lund, Ken; Tang, Lars H; Taylor, Rod S

2016-05-01

The aim of this study was to undertake a comprehensive assessment of the patient, intervention and trial-level factors that may predict exercise capacity following exercise-based rehabilitation in patients with coronary heart disease and heart failure. Meta-analysis and meta-regression analysis. Randomized controlled trials of exercise-based rehabilitation were identified from three published systematic reviews. Exercise capacity was pooled across trials using random effects meta-analysis, and meta-regression used to examine the association between exercise capacity and a range of patient (e.g. age), intervention (e.g. exercise frequency) and trial (e.g. risk of bias) factors. 55 trials (61 exercise-control comparisons, 7553 patients) were included. Following exercise-based rehabilitation compared to control, overall exercise capacity was on average 0.95 (95% CI: 0.76-1.41) standard deviation units higher, and in trials reporting maximum oxygen uptake (VO2max) was 3.3 ml/kg.min(-1) (95% CI: 2.6-4.0) higher. There was evidence of a high level of statistical heterogeneity across trials (I(2) statistic > 50%). In multivariable meta-regression analysis, only exercise intervention intensity was found to be significantly associated with VO2max (P = 0.04); those trials with the highest average exercise intensity had the largest mean post-rehabilitation VO2max compared to control. We found considerable heterogeneity across randomized controlled trials in the magnitude of improvement in exercise capacity following exercise-based rehabilitation compared to control among patients with coronary heart disease or heart failure. Whilst higher exercise intensities were associated with a greater level of post-rehabilitation exercise capacity, there was no strong evidence to support other intervention, patient or trial factors to be predictive. © The European Society of Cardiology 2015.

Random forest models to predict aqueous solubility.

PubMed

Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O

2007-01-01

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.

Efficacy of abstinence promotion media messages: findings from an online randomized trial.

PubMed

Evans, W Douglas; Davis, Kevin C; Ashley, Olivia Silber; Blitstein, Jonathan; Koo, Helen; Zhang, Yun

2009-10-01

We conducted an online randomized experiment to evaluate the efficacy of messages from the Parents Speak Up National Campaign (PSUNC) to promote parent-child communication about sex. We randomly assigned a national sample of 1,969 mothers and fathers to treatment (PSUNC exposure) and control (no exposure) conditions. Mothers were further randomized into treatment and booster (additional messages) conditions to evaluate dose-response effects. Participants were surveyed at baseline, 4 weeks postexposure, and 6 months postexposure. We used multivariable logistic regression procedures in our analysis. Treatment fathers were more likely than control fathers to initiate conversations about sex at 4 weeks, and treatment fathers and mothers were more likely than controls at 6 months to recommend that their children wait to have sex. Treatment fathers and mothers were far more likely than controls to use the campaign Web site. There was a dose-response effect for mothers' Web site use. Using new media methods, this study shows that PSUNC messages are efficacious in promoting parent-child communication about sex and abstinence. Future research should evaluate mechanisms and effectiveness in natural settings.

Comparison of partial least squares and random forests for evaluating relationship between phenolics and bioactivities of Neptunia oleracea.

PubMed

Lee, Soo Yee; Mediani, Ahmed; Maulidiani, Maulidiani; Khatib, Alfi; Ismail, Intan Safinar; Zawawi, Norhasnida; Abas, Faridah

2018-01-01

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials.

PubMed

Zheng, Han; Kimber, Alan; Goodwin, Victoria A; Pickering, Ruth M

2018-01-01

A common design for a falls prevention trial is to assess falling at baseline, randomize participants into an intervention or control group, and ask them to record the number of falls they experience during a follow-up period of time. This paper addresses how best to include the baseline count in the analysis of the follow-up count of falls in negative binomial (NB) regression. We examine the performance of various approaches in simulated datasets where both counts are generated from a mixed Poisson distribution with shared random subject effect. Including the baseline count after log-transformation as a regressor in NB regression (NB-logged) or as an offset (NB-offset) resulted in greater power than including the untransformed baseline count (NB-unlogged). Cook and Wei's conditional negative binomial (CNB) model replicates the underlying process generating the data. In our motivating dataset, a statistically significant intervention effect resulted from the NB-logged, NB-offset, and CNB models, but not from NB-unlogged, and large, outlying baseline counts were overly influential in NB-unlogged but not in NB-logged. We conclude that there is little to lose by including the log-transformed baseline count in standard NB regression compared to CNB for moderate to larger sized datasets. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Discontinuation, Efficacy, and Safety of Cholinesterase Inhibitors for Alzheimer’s Disease: a Meta-Analysis and Meta-Regression of 43 Randomized Clinical Trials Enrolling 16 106 Patients

PubMed Central

Blanco-Silvente, Lídia; Saez, Marc; Barceló, Maria Antònia; Garre-Olmo, Josep; Vilalta-Franch, Joan; Capellà, Dolors

2017-01-01

Abstract Background: We investigated the effect of cholinesterase inhibitors on all-cause discontinuation, efficacy and safety, and the effects of study design-, intervention-, and patient-related covariates on the risk-benefit of cholinesterase inhibitors for Alzheimer’s disease. Methods: A systematic review and meta-analysis of randomized placebo-controlled clinical trials comparing cholinesterase inhibitors and placebo was performed. The effect of covariates on study outcomes was analysed by means of meta-regression using a Bayesian framework. Results: Forty-three randomized placebo-controlled clinical trials involving 16106 patients were included. All-cause discontinuation was higher with cholinesterase inhibitors (OR = 1.66), as was discontinuation due to adverse events (OR=1.75). Cholinesterase inhibitors improved cognitive function (standardized mean difference = 0.38), global symptomatology (standardized mean difference=0.28) and functional capacity (standardized mean difference=0.16) but not neuropsychiatric symptoms. Rivastigmine was associated with a poorer outcome on all-cause discontinuation (Diff OR = 1.66) and donepezil with a higher efficacy on global change (Diff standardized mean difference = 0.41). The proportion of patients with serious adverse events decreased with age (Diff OR = -0.09). Mortality was lower with cholinesterase inhibitors than with placebo (OR = 0.65). Conclusion: While cholinesterase inhibitors show a poor risk-benefit relationship as indicated by mild symptom improvement and a higher than placebo all-cause discontinuation, a reduction of mortality was suggested. Intervention- and patient-related factors modify the effect of cholinesterase inhibitors in patients with Alzheimer’s disease. PMID:28201726

Discontinuation, Efficacy, and Safety of Cholinesterase Inhibitors for Alzheimer's Disease: a Meta-Analysis and Meta-Regression of 43 Randomized Clinical Trials Enrolling 16 106 Patients.

PubMed

Blanco-Silvente, Lídia; Castells, Xavier; Saez, Marc; Barceló, Maria Antònia; Garre-Olmo, Josep; Vilalta-Franch, Joan; Capellà, Dolors

2017-07-01

We investigated the effect of cholinesterase inhibitors on all-cause discontinuation, efficacy and safety, and the effects of study design-, intervention-, and patient-related covariates on the risk-benefit of cholinesterase inhibitors for Alzheimer's disease. A systematic review and meta-analysis of randomized placebo-controlled clinical trials comparing cholinesterase inhibitors and placebo was performed. The effect of covariates on study outcomes was analysed by means of meta-regression using a Bayesian framework. Forty-three randomized placebo-controlled clinical trials involving 16106 patients were included. All-cause discontinuation was higher with cholinesterase inhibitors (OR = 1.66), as was discontinuation due to adverse events (OR=1.75). Cholinesterase inhibitors improved cognitive function (standardized mean difference = 0.38), global symptomatology (standardized mean difference=0.28) and functional capacity (standardized mean difference=0.16) but not neuropsychiatric symptoms. Rivastigmine was associated with a poorer outcome on all-cause discontinuation (Diff OR = 1.66) and donepezil with a higher efficacy on global change (Diff standardized mean difference = 0.41). The proportion of patients with serious adverse events decreased with age (Diff OR = -0.09). Mortality was lower with cholinesterase inhibitors than with placebo (OR = 0.65). While cholinesterase inhibitors show a poor risk-benefit relationship as indicated by mild symptom improvement and a higher than placebo all-cause discontinuation, a reduction of mortality was suggested. Intervention- and patient-related factors modify the effect of cholinesterase inhibitors in patients with Alzheimer's disease. © The Author 2017. Published by Oxford University Press on behalf of CINP.

Ready-to-use supplementary food increases fat mass and BMI in Haitian school-aged children.

PubMed

Iannotti, Lora L; Henretty, Nicole M; Delnatus, Jacques Raymond; Previl, Windy; Stehl, Tom; Vorkoper, Susan; Bodden, Jaime; Maust, Amanda; Smidt, Rachel; Nash, Marilyn L; Tamimie, Courtney A; Owen, Bridget C; Wolff, Patricia B

2015-04-01

In Haiti and other countries, large-scale investments in school feeding programs have been made with marginal evidence of nutrition outcomes. We aimed to examine the effectiveness of a fortified ready-to-use supplementary food (RUSF), Mamba, on reduced anemia and improved body composition in school-aged children compared to an unfortified cereal bar, Tablet Yo, and control groups. A cluster, randomized trial with children ages 3-13 y (n = 1167) was conducted in the north of Haiti. Six schools were matched and randomized to the control group, Tablet Yo group (42 g, 165 kcal), or Mamba group (50 g, 260 kcal, and >75% of the RDA for critical micronutrients). Children in the supplementation groups received the snack daily for 100 d, and all were followed longitudinally for hemoglobin concentrations, anthropometry, and bioelectrical impedance measures: baseline (December 2012), midline (March 2013), and endline (June 2013). Parent surveys were conducted at baseline and endline to examine secondary outcomes of morbidities and dietary intakes. Longitudinal regression modeling using generalized least squares and logit with random effects tested the main effects. At baseline,14.0% of children were stunted, 14.5% underweight, 9.1% thin, and 73% anemic. Fat mass percentage (mean ± SD) was 8.1% ± 4.3% for boys and 12.5% ± 4.4% for girls. In longitudinal modeling, Mamba supplementation increased body mass index z score (regression coefficient ± SEE) 0.25 ± 0.06, fat mass 0.45 ± 0.14 kg, and percentage fat mass 1.28% ± 0.27% compared with control at each time point (P < 0.001). Among boys, Mamba increased fat mass (regression coefficient ± SEE) 0.73 ± 0.19 kg and fat-free mass 0.62 ± 0.34 kg compared with control (P < 0.001). Mamba reduced the odds of developing anemia by 28% compared to control (adjusted OR: 0.72; 95% CI: 0.57, 0.91; P < 0.001). No treatment effect was found for hemoglobin concentration. To our knowledge, this is the first study to give evidence of body composition effects from an RUSF in school-aged children. © 2015 American Society for Nutrition.

Black Clouds vs Random Variation in Hospital Admissions.

PubMed

Ong, Luei Wern; Dawson, Jeffrey D; Ely, John W

2018-06-01

Physicians often accuse their peers of being "black clouds" if they repeatedly have more than the average number of hospital admissions while on call. Our purpose was to determine whether the black-cloud phenomenon is real or explainable by random variation. We analyzed hospital admissions to the University of Iowa family medicine service from July 1, 2010 to June 30, 2015. Analyses were stratified by peer group (eg, night shift attending physicians, day shift senior residents). We analyzed admission numbers to find evidence of black-cloud physicians (those with significantly more admissions than their peers) and white-cloud physicians (those with significantly fewer admissions). The statistical significance of whether there were actual differences across physicians was tested with mixed-effects negative binomial regression. The 5-year study included 96 physicians and 6,194 admissions. The number of daytime admissions ranged from 0 to 10 (mean 2.17, SD 1.63). Night admissions ranged from 0 to 11 (mean 1.23, SD 1.22). Admissions increased from 1,016 in the first year to 1,523 in the fifth year. We found 18 white-cloud and 16 black-cloud physicians in simple regression models that did not control for this upward trend. After including study year and other potential confounding variables in the regression models, there were no significant associations between physicians and admission numbers and therefore no true black or white clouds. In this study, apparent black-cloud and white-cloud physicians could be explained by random variation in hospital admissions. However, this randomness incorporated a wide range in workload among physicians, with potential impact on resident education at the low end and patient safety at the high end.

Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.

PubMed

Baba, Hiromi; Takahara, Jun-ichi; Yamashita, Fumiyoshi; Hashida, Mitsuru

2015-11-01

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Treesearch

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Correction of confounding bias in non-randomized studies by appropriate weighting.

PubMed

Schmoor, Claudia; Gall, Christine; Stampf, Susanne; Graf, Erika

2011-03-01

In non-randomized studies, the assessment of a causal effect of treatment or exposure on outcome is hampered by possible confounding. Applying multiple regression models including the effects of treatment and covariates on outcome is the well-known classical approach to adjust for confounding. In recent years other approaches have been promoted. One of them is based on the propensity score and considers the effect of possible confounders on treatment as a relevant criterion for adjustment. Another proposal is based on using an instrumental variable. Here inference relies on a factor, the instrument, which affects treatment but is thought to be otherwise unrelated to outcome, so that it mimics randomization. Each of these approaches can basically be interpreted as a simple reweighting scheme, designed to address confounding. The procedures will be compared with respect to their fundamental properties, namely, which bias they aim to eliminate, which effect they aim to estimate, and which parameter is modelled. We will expand our overview of methods for analysis of non-randomized studies to methods for analysis of randomized controlled trials and show that analyses of both study types may target different effects and different parameters. The considerations will be illustrated using a breast cancer study with a so-called Comprehensive Cohort Study design, including a randomized controlled trial and a non-randomized study in the same patient population as sub-cohorts. This design offers ideal opportunities to discuss and illustrate the properties of the different approaches. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Brief Intervention Decreases Drinking Frequency in HIV-Infected, Heavy Drinking Women: Results of a Randomized Controlled Trial

PubMed Central

Chander, Geetanjali; Hutton, Heidi E.; Lau, Bryan; Xu, Xiaoqiang; McCaul, Mary E.

2015-01-01

Objective Hazardous alcohol use by HIV-infected women is associated with poor HIV outcomes and HIV transmission risk behaviors. We examined the effectiveness of brief alcohol intervention (BI) among hazardous drinking women receiving care in an urban, HIV clinic. Methods Women were randomized to a 2-session BI or usual care. Outcomes assessed at baseline, 3, 6 and 12 months included 90-day frequency of any alcohol use and heavy/binge drinking (≥4 drinks per occasion), and average drinks per drinking episode. Secondary outcomes included HIV medication and appointment adherence, HIV1-RNA suppression, and days of unprotected vaginal sex. We examined intervention effectiveness using generalized mixed effect models and quantile regression. Results Of 148 eligible women, 74 were randomized to each arm. In mixed effects models, 90-day drinking frequency decreased among intervention group compared to control, with women in the intervention condition less likely to have a drinking day (OR: 0.42 (95% CI: 0.23–0.75). Heavy/binge drinking days and drinks per drinking day did not differ significantly between groups. Quantile regression demonstrated a decrease in drinking frequency in the middle to upper ranges of the distribution of drinking days and heavy/binge drinking days that differed significantly between intervention and control conditions. At follow-up, the intervention group had significantly fewer episodes of unprotected vaginal sex. No intervention effects were observed for other outcomes. Conclusions Brief alcohol intervention reduces frequency of alcohol use and unprotected vaginal sex among HIV-infected women. More intensive services may be needed to lower drinks per drinking day and enhance care for more severely affected drinkers. PMID:25967270

[Associations between dormitory environment/other factors and sleep quality of medical students].

PubMed

Zheng, Bang; Wang, Kailu; Pan, Ziqi; Li, Man; Pan, Yuting; Liu, Ting; Xu, Dan; Lyu, Jun

2016-03-01

To investigate the sleep quality and related factors among medical students in China, understand the association between dormitory environment and sleep quality, and provide evidence and recommendations for sleep hygiene intervention. A total of 555 undergraduate students were selected from a medical school of an university in Beijing through stratified-cluster random-sampling to conduct a questionnaire survey by using Chinese version of Pittsburgh Sleep Quality Index (PSQI) and self-designed questionnaire. Analyses were performed by using multiple logistic regression model as well as multilevel linear regression model. The prevalence of sleep disorder was 29.1%(149/512), and 39.1%(200/512) of the students reported that the sleep quality was influenced by dormitory environment. PSQI score was negatively correlated with self-reported rating of dormitory environment (γs=-0.310, P<0.001). Logistic regression analysis showed the related factors of sleep disorder included grade, sleep regularity, self-rated health status, pressures of school work and employment, as well as dormitory environment. RESULTS of multilevel regression analysis also indicated that perception on dormitory environment (individual level) was associated with sleep quality with the dormitory level random effects under control (b=-0.619, P<0.001). The prevalence of sleep disorder was high in medical students, which was associated with multiple factors. Dormitory environment should be taken into consideration when the interventions are taken to improve the sleep quality of students.

A method for fitting regression splines with varying polynomial order in the linear mixed model.

PubMed

Edwards, Lloyd J; Stewart, Paul W; MacDougall, James E; Helms, Ronald W

2006-02-15

The linear mixed model has become a widely used tool for longitudinal analysis of continuous variables. The use of regression splines in these models offers the analyst additional flexibility in the formulation of descriptive analyses, exploratory analyses and hypothesis-driven confirmatory analyses. We propose a method for fitting piecewise polynomial regression splines with varying polynomial order in the fixed effects and/or random effects of the linear mixed model. The polynomial segments are explicitly constrained by side conditions for continuity and some smoothness at the points where they join. By using a reparameterization of this explicitly constrained linear mixed model, an implicitly constrained linear mixed model is constructed that simplifies implementation of fixed-knot regression splines. The proposed approach is relatively simple, handles splines in one variable or multiple variables, and can be easily programmed using existing commercial software such as SAS or S-plus. The method is illustrated using two examples: an analysis of longitudinal viral load data from a study of subjects with acute HIV-1 infection and an analysis of 24-hour ambulatory blood pressure profiles.

Linear Regression with a Randomly Censored Covariate: Application to an Alzheimer's Study.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2017-01-01

The association between maternal age of onset of dementia and amyloid deposition (measured by in vivo positron emission tomography (PET) imaging) in cognitively normal older offspring is of interest. In a regression model for amyloid, special methods are required due to the random right censoring of the covariate of maternal age of onset of dementia. Prior literature has proposed methods to address the problem of censoring due to assay limit of detection, but not random censoring. We propose imputation methods and a survival regression method that do not require parametric assumptions about the distribution of the censored covariate. Existing imputation methods address missing covariates, but not right censored covariates. In simulation studies, we compare these methods to the simple, but inefficient complete case analysis, and to thresholding approaches. We apply the methods to the Alzheimer's study.

[Theory, method and application of method R on estimation of (co)variance components].

PubMed

Liu, Wen-Zhong

2004-07-01

Theory, method and application of Method R on estimation of (co)variance components were reviewed in order to make the method be reasonably used. Estimation requires R values,which are regressions of predicted random effects that are calculated using complete dataset on predicted random effects that are calculated using random subsets of the same data. By using multivariate iteration algorithm based on a transformation matrix,and combining with the preconditioned conjugate gradient to solve the mixed model equations, the computation efficiency of Method R is much improved. Method R is computationally inexpensive,and the sampling errors and approximate credible intervals of estimates can be obtained. Disadvantages of Method R include a larger sampling variance than other methods for the same data,and biased estimates in small datasets. As an alternative method, Method R can be used in larger datasets. It is necessary to study its theoretical properties and broaden its application range further.

A randomized controlled trial of the effects of hypnosis with 3-D virtual reality animation on tiredness, mood, and salivary cortisol.

PubMed

Thompson, Trevor; Steffert, Tony; Steed, Anthony; Gruzelier, John

2011-01-01

Case studies suggest hypnosis with a virtual reality (VR) component may be an effective intervention; although few follow-up randomized, controlled trials have been performed comparing such interventions with standard hypnotic treatments. Thirty-five healthy participants were randomized to self-hypnosis with VR imagery, standard self-hypnosis, or relaxation interventions. Changes in sleep, cortisol levels, and mood were examined. Self-hypnosis involved 10- to 20-min. sessions visualizing a healthy immune scenario. Trait absorption was also recorded as a possible moderator. Moderated regression indicated that both hypnosis interventions produced significantly lower tiredness ratings than relaxation when trait absorption was high. When trait absorption was low, VR resulted in significantly higher engagement ratings, although this did not translate to demonstrable improvement in outcome. Results suggest that VR imagery may increase engagement relative to traditional methods, but further investigation into its potential to enhance therapeutic efficacy is required.

An application of quantile random forests for predictive mapping of forest attributes

Treesearch

E.A. Freeman; G.G. Moisen

2015-01-01

Increasingly, random forest models are used in predictive mapping of forest attributes. Traditional random forests output the mean prediction from the random trees. Quantile regression forests (QRF) is an extension of random forests developed by Nicolai Meinshausen that provides non-parametric estimates of the median predicted value as well as prediction quantiles. It...

Human Capital Background and the Educational Attainment of Second-Generation Immigrants in France

ERIC Educational Resources Information Center

Dos Santos, Manon Domingues; Wolff, Francois-Charles

2011-01-01

In this paper, we study the impact of parental human capital background on ethnic educational gaps between second-generation immigrants using a large data set conducted in France in 2003. Estimates from censored random effect ordered Probit regressions show that the skills of immigrants explain in the most part, the ethnic educational gap between…

Causal Inference and Omitted Variable Bias in Financial Aid Research: Assessing Solutions

ERIC Educational Resources Information Center

Riegg, Stephanie K.

2008-01-01

This article highlights the problem of omitted variable bias in research on the causal effect of financial aid on college-going. I first describe the problem of self-selection and the resulting bias from omitted variables. I then assess and explore the strengths and weaknesses of random assignment, multivariate regression, proxy variables, fixed…

Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning

ERIC Educational Resources Information Center

Feng, Mingyu; Beck, Joseph E.; Heffernan, Neil T.

2009-01-01

A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by…

Investigating the Effect of Complexity Factors in Stoichiometry Problems Using Logistic Regression and Eye Tracking

ERIC Educational Resources Information Center

Tang, Hui; Kirk, John; Pienta, Norbert J.

2014-01-01

This paper includes two experiments, one investigating complexity factors in stoichiometry word problems, and the other identifying students' problem-solving protocols by using eye-tracking technology. The word problems used in this study had five different complexity factors, which were randomly assigned by a Web-based tool that we developed. The…

Measuring Foundation School Effectiveness Using English Administrative Data, Survey Data and a Regression Discontinuity Design

ERIC Educational Resources Information Center

Allen, Rebecca

2013-01-01

Apparently sophisticated school performance measures have been used to claim that giving schools autonomy from local government control improves pupil exam performance. This paper explores the extent to which inferring causality between autonomy and pupil achievement is reasonable given that pupils are not randomly assigned to schools and schools…

Effects of the TCARE® intervention on caregiver burden and depressive symptoms: preliminary findings from a randomized controlled study.

PubMed

Montgomery, Rhonda J V; Kwak, Jung; Kosloski, Karl; O'Connell Valuch, Katharine

2011-09-01

We examined the effects of a manualized care management protocol specifically designed for care managers working with caregivers, the Tailored Caregiver Assessment and Referral® (TCARE®) protocol, on caregiver identity discrepancy, burden, and depressive symptoms. Preliminary data from a longitudinal, randomized, controlled intervention study with 266 family caregivers served by 52 care managers in 4 states were analyzed using repeated measures random effects regression procedures. Caregivers in the intervention and control groups were repeatedly assessed for up to 9 months on caregiver identity discrepancy, 3 areas of caregiving burden-objective, relationship, and stress burdens; depression; and intention for nursing home placement. We found significant group by time interaction effects for caregiver identity discrepancy, relationship burden, stress burden, depression, and intention for nursing home placement. Caregivers in the intervention group experienced significant improvement on these measures, whereas caregivers in the control group worsened on these measures over time. The preliminary findings provide strong support for effectiveness of the TCARE® protocol on improving caregiver well-being and mental health outcomes.

Effects of the TCARE® Intervention on Caregiver Burden and Depressive Symptoms: Preliminary Findings From a Randomized Controlled Study

PubMed Central

Kwak, Jung; Kosloski, Karl; O’Connell Valuch, Katharine

2011-01-01

Objectives. We examined the effects of a manualized care management protocol specifically designed for care managers working with caregivers, the Tailored Caregiver Assessment and Referral® (TCARE®) protocol, on caregiver identity discrepancy, burden, and depressive symptoms. Methods. Preliminary data from a longitudinal, randomized, controlled intervention study with 266 family caregivers served by 52 care managers in 4 states were analyzed using repeated measures random effects regression procedures. Caregivers in the intervention and control groups were repeatedly assessed for up to 9 months on caregiver identity discrepancy, 3 areas of caregiving burden—objective, relationship, and stress burdens; depression; and intention for nursing home placement. Results. We found significant group by time interaction effects for caregiver identity discrepancy, relationship burden, stress burden, depression, and intention for nursing home placement. Caregivers in the intervention group experienced significant improvement on these measures, whereas caregivers in the control group worsened on these measures over time. Discussion. The preliminary findings provide strong support for effectiveness of the TCARE® protocol on improving caregiver well-being and mental health outcomes. PMID:21840840

Cooperation without culture? The null effect of generalized trust on intentional homicide: a cross-national panel analysis, 1995-2009.

PubMed

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation.

Concepts for a theoretical and experimental study of lifting rotor random loads and vibrations. Phase 6-B: Experiments with progressing/regressing forced rotor flapping modes

NASA Technical Reports Server (NTRS)

Hohenemser, K. H.; Crews, S. T.

1972-01-01

A two bladed 16-inch hingeless rotor model was built and tested outside and inside a 24 by 24 inch wind tunnel test section at collective pitch settings up to 5 deg and rotor advance ratios up to .4. The rotor model has a simple eccentric mechanism to provide progressing or regressing cyclic pitch excitation. The flapping responses were compared to analytically determined responses which included flap-bending elasticity but excluded rotor wake effects. Substantial systematic deviations of the measured responses from the computed responses were found, which were interpreted as the effects of interaction of the blades with a rotating asymmetrical wake.

Effectiveness of Using Incentives to Improve Parolee Admission and Attendance in Community Addiction Treatment

PubMed Central

Prendergast, Michael L.; Hall, Elizabeth A.; Grossman, Jason; Veliz, Robert; Gregorio, Liliana; Warda, Umme S.; Van Unen, Kory; Knight, Chloe

2017-01-01

This study is a randomized effectiveness trial of the use of incentives to improve treatment utilization among parolees in community treatment. In prison, Admission phase parolees were randomized to Admission Incentive (N=31) or Education (N=29). Attendance phase parolees entering community treatment were randomized to Attendance Incentive (N=104) or Education (N=98). There was no main effect for incentives in either study phase. Neither admission to community treatment (Incentive 60%, Education 64%; p =.74), nor intervention completion (Incentive 22%; Education 27%; p =.46) appeared to be impacted. Time-in-treatment was predicted by age, first arrest age, and type of parole status (Cox regression p<.05), but not by treatment group. Providing incentives did not increase the likelihood that parolees enrolled in or stayed in community treatment. In light of this finding, criminal justice practitioners who are considering incentives to increase admission or retention should be aware that they may not produce the desired outcomes. PMID:28331241

Growing literature, stagnant science? Systematic review, meta-regression and cumulative analysis of audit and feedback interventions in health care.

PubMed

Ivers, Noah M; Grimshaw, Jeremy M; Jamtvedt, Gro; Flottorp, Signe; O'Brien, Mary Ann; French, Simon D; Young, Jane; Odgaard-Jensen, Jan

2014-11-01

This paper extends the findings of the Cochrane systematic review of audit and feedback on professional practice to explore the estimate of effect over time and examine whether new trials have added to knowledge regarding how optimize the effectiveness of audit and feedback. We searched the Cochrane Central Register of Controlled Trials, MEDLINE, and EMBASE for randomized trials of audit and feedback compared to usual care, with objectively measured outcomes assessing compliance with intended professional practice. Two reviewers independently screened articles and abstracted variables related to the intervention, the context, and trial methodology. The median absolute risk difference in compliance with intended professional practice was determined for each study, and adjusted for baseline performance. The effect size across studies was recalculated as studies were added to the cumulative analysis. Meta-regressions were conducted for studies published up to 2002, 2006, and 2010 in which characteristics of the intervention, the recipients, and trial risk of bias were tested as predictors of effect size. Of the 140 randomized clinical trials (RCTs) included in the Cochrane review, 98 comparisons from 62 studies met the criteria for inclusion. The cumulative analysis indicated that the effect size became stable in 2003 after 51 comparisons from 30 trials. Cumulative meta-regressions suggested new trials are contributing little further information regarding the impact of common effect modifiers. Feedback appears most effective when: delivered by a supervisor or respected colleague; presented frequently; featuring both specific goals and action-plans; aiming to decrease the targeted behavior; baseline performance is lower; and recipients are non-physicians. There is substantial evidence that audit and feedback can effectively improve quality of care, but little evidence of progress in the field. There are opportunity costs for patients, providers, and health care systems when investigators test quality improvement interventions that do not build upon, or contribute toward, extant knowledge.

Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants.

PubMed

Pierce, Brandon L; Ahsan, Habibul; Vanderweele, Tyler J

2011-06-01

Mendelian Randomization (MR) studies assess the causality of an exposure-disease association using genetic determinants [i.e. instrumental variables (IVs)] of the exposure. Power and IV strength requirements for MR studies using multiple genetic variants have not been explored. We simulated cohort data sets consisting of a normally distributed disease trait, a normally distributed exposure, which affects this trait and a biallelic genetic variant that affects the exposure. We estimated power to detect an effect of exposure on disease for varying allele frequencies, effect sizes and samples sizes (using two-stage least squares regression on 10,000 data sets-Stage 1 is a regression of exposure on the variant. Stage 2 is a regression of disease on the fitted exposure). Similar analyses were conducted using multiple genetic variants (5, 10, 20) as independent or combined IVs. We assessed IV strength using the first-stage F statistic. Simulations of realistic scenarios indicate that MR studies will require large (n > 1000), often very large (n > 10,000), sample sizes. In many cases, so-called 'weak IV' problems arise when using multiple variants as independent IVs (even with as few as five), resulting in biased effect estimates. Combining genetic factors into fewer IVs results in modest power decreases, but alleviates weak IV problems. Ideal methods for combining genetic factors depend upon knowledge of the genetic architecture underlying the exposure. The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.

Effect of latitude on the rate of change in incidence of Lyme disease in the United States

PubMed Central

Tuite, Ashleigh R.; Greer, Amy L.

2013-01-01

Background Tick-borne illnesses represent an important class of emerging zoonoses, with climate change projected to increase the geographic range within which tick-borne zoonoses might become endemic. We evaluated the impact of latitude on the rate of change in the incidence of Lyme disease in the United States, using publicly available data. Methods We estimated state-level year-on-year incidence rate ratios (IRRs) for Lyme disease for the period 1993 to 2007 using Poisson regression methods. We evaluated between-state heterogeneity in IRRs using a random-effects meta-analytic approach. We identified state-level characteristics associated with increasing incidence using random-effects meta-regression. Results The incidence of Lyme disease in the US increased by about 80% between 1993 and 2007 (IRR per year 1.049, 95% CI [confidence interval] 1.048 to 1.050). There was marked between-state heterogeneity in the average incidence of Lyme disease, ranging from 0.008 per 100 000 person-years in Colorado to 75 per 100 000 in Connecticut, and significant between-state heterogeneity in temporal trends (p < 0.001). In multivariable meta-regression models, increasing incidence showed a linear association with state latitude and population density. These 2 factors explained 27% of the between-state variation in IRRs. No independent association was identified for other state-level characteristics. Interpretation Lyme disease incidence increased in the US as a whole during the study period, but the changes were not uniform. Marked increases were identified in northern-most states, whereas southern states experienced stable or declining rates of Lyme disease. PMID:25077101

Estimating current and future streamflow characteristics at ungaged sites, central and eastern Montana, with application to evaluating effects of climate change on fish populations

USGS Publications Warehouse

Sando, Roy; Chase, Katherine J.

2017-03-23

A common statistical procedure for estimating streamflow statistics at ungaged locations is to develop a relational model between streamflow and drainage basin characteristics at gaged locations using least squares regression analysis; however, least squares regression methods are parametric and make constraining assumptions about the data distribution. The random forest regression method provides an alternative nonparametric method for estimating streamflow characteristics at ungaged sites and requires that the data meet fewer statistical conditions than least squares regression methods.Random forest regression analysis was used to develop predictive models for 89 streamflow characteristics using Precipitation-Runoff Modeling System simulated streamflow data and drainage basin characteristics at 179 sites in central and eastern Montana. The predictive models were developed from streamflow data simulated for current (baseline, water years 1982–99) conditions and three future periods (water years 2021–38, 2046–63, and 2071–88) under three different climate-change scenarios. These predictive models were then used to predict streamflow characteristics for baseline conditions and three future periods at 1,707 fish sampling sites in central and eastern Montana. The average root mean square error for all predictive models was about 50 percent. When streamflow predictions at 23 fish sampling sites were compared to nearby locations with simulated data, the mean relative percent difference was about 43 percent. When predictions were compared to streamflow data recorded at 21 U.S. Geological Survey streamflow-gaging stations outside of the calibration basins, the average mean absolute percent error was about 73 percent.

Impact of Dual Lipid-Lowering Strategy With Ezetimibe and Atorvastatin on Coronary Plaque Regression in Patients With Percutaneous Coronary Intervention: The Multicenter Randomized Controlled PRECISE-IVUS Trial.

PubMed

Tsujita, Kenichi; Sugiyama, Seigo; Sumida, Hitoshi; Shimomura, Hideki; Yamashita, Takuro; Yamanaga, Kenshi; Komura, Naohiro; Sakamoto, Kenji; Oka, Hideki; Nakao, Koichi; Nakamura, Sunao; Ishihara, Masaharu; Matsui, Kunihiko; Sakaino, Naritsugu; Nakamura, Natsuki; Yamamoto, Nobuyasu; Koide, Shunichi; Matsumura, Toshiyuki; Fujimoto, Kazuteru; Tsunoda, Ryusuke; Morikami, Yasuhiro; Matsuyama, Koushi; Oshima, Shuichi; Kaikita, Koichi; Hokimoto, Seiji; Ogawa, Hisao

2015-08-04

Despite standard statin therapy, a majority of patients retain a high "residual risk" of cardiovascular events. The aim of this study was to evaluate the effects of ezetimibe plus atorvastatin versus atorvastatin monotherapy on the lipid profile and coronary atherosclerosis in Japanese patients who underwent percutaneous coronary intervention (PCI). This trial was a prospective, randomized, controlled, multicenter study. Eligible patients who underwent PCI were randomly assigned to atorvastatin alone or atorvastatin plus ezetimibe (10 mg) daily. Atorvastatin was uptitrated with a treatment goal of low-density lipoprotein cholesterol (LDL-C) <70 mg/dl. Serial volumetric intravascular ultrasound was performed at baseline and again at 9 to 12 months to quantify the coronary plaque response in 202 patients. The combination of atorvastatin/ezetimibe resulted in lower levels of LDL-C than atorvastatin monotherapy (63.2 ± 16.3 mg/dl vs. 73.3 ± 20.3 mg/dl; p < 0.001). For the absolute change in percent atheroma volume (PAV), the mean difference between the 2 groups (-1.538%; 95% confidence interval [CI]: -3.079% to 0.003%) did not exceed the pre-defined noninferiority margin of 3%, but the absolute change in PAV did show superiority for the dual lipid-lowering strategy (-1.4%; 95% CI: -3.4% to -0.1% vs. -0.3%; 95% CI: -1.9% to 0.9% with atorvastatin alone; p = 0.001). For PAV, a significantly greater percentage of patients who received atorvastatin/ezetimibe showed coronary plaque regression (78% vs. 58%; p = 0.004). Both strategies had acceptable side effect profiles, with a low incidence of laboratory abnormalities and cardiovascular events. Compared with standard statin monotherapy, the combination of statin plus ezetimibe showed greater coronary plaque regression, which might be attributed to cholesterol absorption inhibition-induced aggressive lipid lowering. (Plaque Regression With Cholesterol Absorption Inhibitor or Synthesis Inhibitor Evaluated by Intravascular Ultrasound [PRECISE-IVUS]; NCT01043380). Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Random forests as cumulative effects models: A case study of lakes and rivers in Muskoka, Canada.

PubMed

Jones, F Chris; Plewes, Rachel; Murison, Lorna; MacDougall, Mark J; Sinclair, Sarah; Davies, Christie; Bailey, John L; Richardson, Murray; Gunn, John

2017-10-01

Cumulative effects assessment (CEA) - a type of environmental appraisal - lacks effective methods for modeling cumulative effects, evaluating indicators of ecosystem condition, and exploring the likely outcomes of development scenarios. Random forests are an extension of classification and regression trees, which model response variables by recursive partitioning. Random forests were used to model a series of candidate ecological indicators that described lakes and rivers from a case study watershed (The Muskoka River Watershed, Canada). Suitability of the candidate indicators for use in cumulative effects assessment and watershed monitoring was assessed according to how well they could be predicted from natural habitat features and how sensitive they were to human land-use. The best models explained 75% of the variation in a multivariate descriptor of lake benthic-macroinvertebrate community structure, and 76% of the variation in the conductivity of river water. Similar results were obtained by cross-validation. Several candidate indicators detected a simulated doubling of urban land-use in their catchments, and a few were able to detect a simulated doubling of agricultural land-use. The paper demonstrates that random forests can be used to describe the combined and singular effects of multiple stressors and natural environmental factors, and furthermore, that random forests can be used to evaluate the performance of monitoring indicators. The numerical methods presented are applicable to any ecosystem and indicator type, and therefore represent a step forward for CEA. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Conditional Monte Carlo randomization tests for regression models.

PubMed

Parhat, Parwen; Rosenberger, William F; Diao, Guoqing

2014-08-15

We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

Comparison of methods for the analysis of relatively simple mediation models.

PubMed

Rijnhart, Judith J M; Twisk, Jos W R; Chinapaw, Mai J M; de Boer, Michiel R; Heymans, Martijn W

2017-09-01

Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.

Simultaneous confidence bands for Cox regression from semiparametric random censorship.

PubMed

Mondal, Shoubhik; Subramanian, Sundarraman

2016-01-01

Cox regression is combined with semiparametric random censorship models to construct simultaneous confidence bands (SCBs) for subject-specific survival curves. Simulation results are presented to compare the performance of the proposed SCBs with the SCBs that are based only on standard Cox. The new SCBs provide correct empirical coverage and are more informative. The proposed SCBs are illustrated with two real examples. An extension to handle missing censoring indicators is also outlined.

Exploring the use of random regression models with legendre polynomials to analyze measures of volume of ejaculate in Holstein bulls.

PubMed

Carabaño, M J; Díaz, C; Ugarte, C; Serrano, M

2007-02-01

Artificial insemination centers routinely collect records of quantity and quality of semen of bulls throughout the animals' productive period. The goal of this paper was to explore the use of random regression models with orthogonal polynomials to analyze repeated measures of semen production of Spanish Holstein bulls. A total of 8,773 records of volume of first ejaculate (VFE) collected between 12 and 30 mo of age from 213 Spanish Holstein bulls was analyzed under alternative random regression models. Legendre polynomial functions of increasing order (0 to 6) were fitted to the average trajectory, additive genetic and permanent environmental effects. Age at collection and days in production were used as time variables. Heterogeneous and homogeneous residual variances were alternatively assumed. Analyses were carried out within a Bayesian framework. The logarithm of the marginal density and the cross-validation predictive ability of the data were used as model comparison criteria. Based on both criteria, age at collection as a time variable and heterogeneous residuals models are recommended to analyze changes of VFE over time. Both criteria indicated that fitting random curves for genetic and permanent environmental components as well as for the average trajector improved the quality of models. Furthermore, models with a higher order polynomial for the permanent environmental (5 to 6) than for the genetic components (4 to 5) and the average trajectory (2 to 3) tended to perform best. High-order polynomials were needed to accommodate the highly oscillating nature of the phenotypic values. Heritability and repeatability estimates, disregarding the extremes of the studied period, ranged from 0.15 to 0.35 and from 0.20 to 0.50, respectively, indicating that selection for VFE may be effective at any stage. Small differences among models were observed. Apart from the extremes, estimated correlations between ages decreased steadily from 0.9 and 0.4 for measures 1 mo apart to 0.4 and 0.2 for most distant measures for additive genetic and phenotypic components, respectively. Further investigation to account for environmental factors that may be responsible for the oscillating observations of VFE is needed.

Regression analysis utilizing subjective evaluation of emotional experience in PET studies on emotions.

PubMed

Aalto, Sargo; Wallius, Esa; Näätänen, Petri; Hiltunen, Jaana; Metsähonkala, Liisa; Sipilä, Hannu; Karlsson, Hasse

2005-09-01

A methodological study on subject-specific regression analysis (SSRA) exploring the correlation between the neural response and the subjective evaluation of emotional experience in eleven healthy females is presented. The target emotions, i.e., amusement and sadness, were induced using validated film clips, regional cerebral blood flow (rCBF) was measured using positron emission tomography (PET), and the subjective intensity of the emotional experience during the PET scanning was measured using a category ratio (CR-10) scale. Reliability analysis of the rating data indicated that the subjects rated the intensity of their emotional experience fairly consistently on the CR-10 scale (Cronbach alphas 0.70-0.97). A two-phase random-effects analysis was performed to ensure the generalizability and inter-study comparability of the SSRA results. Random-effects SSRAs using Statistical non-Parametric Mapping 99 (SnPM99) showed that rCBF correlated with the self-rated intensity of the emotional experience mainly in the brain regions that were identified in the random-effects subtraction analyses using the same imaging data. Our results give preliminary evidence of a linear association between the neural responses related to amusement and sadness and the self-evaluated intensity of the emotional experience in several regions involved in the emotional response. SSRA utilizing subjective evaluation of emotional experience turned out a feasible and promising method of analysis. It allows versatile exploration of the neurobiology of emotions and the neural correlates of actual and individual emotional experience. Thus, SSRA might be able to catch the idiosyncratic aspects of the emotional response better than traditional subtraction analysis.

A randomized-controlled trial examining the effects of reflexology on anxiety of patients undergoing coronary angiography.

PubMed

Molavi Vardanjani, Mehdi; Masoudi Alavi, Negin; Razavi, Narges Sadat; Aghajani, Mohammad; Azizi-Fini, Esmail; Vaghefi, Seied Morteza

2013-09-01

The anxiety reduction before coronary angiography has clinical advantages and is one of the objectives of nursing. Reflexology is a non-invasive method that has been used in several clinical situations. Applying reflexology might have effect on the reduction of anxiety before coronary angiography. The aim of this randomized clinical trial was to investigate the effect of reflexology on anxiety among patients undergoing coronary angiography. This trial was conducted in Shahid Beheshti Hospital, in Kashan, Iran. One hundred male patients who were undergoing coronary angiography were randomly enrolled into intervention and placebo groups. The intervention protocol was included 30 minutes of general foot massage and the stimulation of three reflex points including solar plexus, pituitary gland, and heart. The placebo group only received the general foot massage. Spielbergers state trait anxiety inventory was used to assess the anxiety experienced by patients. Data was analyzed using Man-Witney, Wilcoxon and Chi-square tests. The stepwise multiple regressions used to analyze the variables that are involved in anxiety reduction. The mean range of anxiety decreased from 53.24 to 45.24 in reflexology group which represented 8 score reduction (P = 0.0001). The reduction in anxiety was 5.9 score in placebo group which was also significant (P = 0.0001). The anxiety reduction was significantly higher in reflexology group (P = 0.014). The stepwise multiple regression analysis showed that doing reflexology can explain the 7.5% of anxiety reduction which made a significant model. Reflexology can decrease the anxiety level before coronary angiography. Therefore, reflexology before coronary angiography is recommended.

Regression Discontinuity and Randomized Controlled Trial Estimates: An Application to The Mycotic Ulcer Treatment Trials.

PubMed

Oldenburg, Catherine E; Venkatesh Prajna, N; Krishnan, Tiruvengada; Rajaraman, Revathi; Srinivasan, Muthiah; Ray, Kathryn J; O'Brien, Kieran S; Glymour, M Maria; Porco, Travis C; Acharya, Nisha R; Rose-Nussbaumer, Jennifer; Lietman, Thomas M

2018-08-01

We compare results from regression discontinuity (RD) analysis to primary results of a randomized controlled trial (RCT) utilizing data from two contemporaneous RCTs for treatment of fungal corneal ulcers. Patients were enrolled in the Mycotic Ulcer Treatment Trials I and II (MUTT I & MUTT II) based on baseline visual acuity: patients with acuity ≤ 20/400 (logMAR 1.3) enrolled in MUTT I, and >20/400 in MUTT II. MUTT I investigated the effect of topical natamycin versus voriconazole on best spectacle-corrected visual acuity. MUTT II investigated the effect of topical voriconazole plus placebo versus topical voriconazole plus oral voriconazole. We compared the RD estimate (natamycin arm of MUTT I [N = 162] versus placebo arm of MUTT II [N = 54]) to the RCT estimate from MUTT I (topical natamycin [N = 162] versus topical voriconazole [N = 161]). In the RD, patients receiving natamycin had mean improvement of 4-lines of visual acuity at 3 months (logMAR -0.39, 95% CI: -0.61, -0.17) compared to topical voriconazole plus placebo, and 2-lines in the RCT (logMAR -0.18, 95% CI: -0.30, -0.05) compared to topical voriconazole. The RD and RCT estimates were similar, although the RD design overestimated effects compared to the RCT.

The effect of green tea extract supplementation on sputum smear conversion and weight changes in pulmonary TB patients: A randomized controlled trial

PubMed Central

Honarvar, Mohammad Reza; Eghtesadi, Shahryar; Gill, Pooria; Jazayeri, Shima; Vakili, Mohammad Ali; Shamsardekani, Mohammad Reza; Abbasi, Abdollah

2016-01-01

Background: Acceleration in sputum smear conversion helps faster improvement and decreased probability of the transfer of TB. In this study, we aimed to investigate the effect of green tea extract supplementation on sputum smear conversion and weight changes in smear positive pulmonary TB patients in Iran. Methods: In this double blind clinical study, TB patients were divided into intervention, (n=43) receiving 500 mg green tea extract (GTE), and control groups (n=40) receiving placebo for two months, using balanced randomization. Random allocation and allocation concealment were observed. Height and weight were measured at the beginning, and two and six months post-treatment. Evaluations were performed on three slides, using the ZiehlNeelsen method. Independent and paired t test, McNemar’s, Wilcoxon, Kaplan-Meier, Cox regression model and Log-Rank test were utilized. Statistical significance was set at p<0.05. This trial was registered under IRCT201212232602N11. Results: The interventional changes and the interactive effect of intervention on weight were not significant (p>0.05). In terms of shortening the duration of conversion, the case to control proportion showed a significant difference (p=0.032). Based on the Cox regression model, the hazard ratio of the relative risk of delay in sputum smear conversion was 3.7 (p=0.002) in the higher microbial load group compared to the placebo group and 0.54 (95% CI: 0.31-0.94) in the intervention compared to the placebo group. Conclusion: GTE decreases the risk of delay in sputum smear conversion, but has no effect on weight gain. Moreover, it may be used as an adjuvant therapy for faster rehabilitation for pulmonary TB patients. PMID:27493925

Genetic evaluation and selection response for growth in meat-type quail through random regression models using B-spline functions and Legendre polynomials.

PubMed

Mota, L F M; Martins, P G M A; Littiere, T O; Abreu, L R A; Silva, M A; Bonafé, C M

2018-04-01

The objective was to estimate (co)variance functions using random regression models (RRM) with Legendre polynomials, B-spline function and multi-trait models aimed at evaluating genetic parameters of growth traits in meat-type quail. A database containing the complete pedigree information of 7000 meat-type quail was utilized. The models included the fixed effects of contemporary group and generation. Direct additive genetic and permanent environmental effects, considered as random, were modeled using B-spline functions considering quadratic and cubic polynomials for each individual segment, and Legendre polynomials for age. Residual variances were grouped in four age classes. Direct additive genetic and permanent environmental effects were modeled using 2 to 4 segments and were modeled by Legendre polynomial with orders of fit ranging from 2 to 4. The model with quadratic B-spline adjustment, using four segments for direct additive genetic and permanent environmental effects, was the most appropriate and parsimonious to describe the covariance structure of the data. The RRM using Legendre polynomials presented an underestimation of the residual variance. Lesser heritability estimates were observed for multi-trait models in comparison with RRM for the evaluated ages. In general, the genetic correlations between measures of BW from hatching to 35 days of age decreased as the range between the evaluated ages increased. Genetic trend for BW was positive and significant along the selection generations. The genetic response to selection for BW in the evaluated ages presented greater values for RRM compared with multi-trait models. In summary, RRM using B-spline functions with four residual variance classes and segments were the best fit for genetic evaluation of growth traits in meat-type quail. In conclusion, RRM should be considered in genetic evaluation of breeding programs.

Randomized trial of parent training to prevent adolescent problem behaviors during the high school transition.

PubMed

Mason, W Alex; Fleming, Charles B; Gross, Thomas J; Thompson, Ronald W; Parra, Gilbert R; Haggerty, Kevin P; Snyder, James J

2016-12-01

This randomized controlled trial tested a widely used general parent training program, Common Sense Parenting (CSP), with low-income 8th graders and their families to support a positive transition to high school. The program was tested in its original 6-session format and in a modified format (CSP-Plus), which added 2 sessions that included adolescents. Over 2 annual cohorts, 321 families were enrolled and randomly assigned to either the CSP, CSP-Plus, or minimal-contact control condition. Pretest, posttest, 1-year follow-up, and 2-year follow-up survey data on parenting as well as youth school bonding, social skills, and problem behaviors were collected from parents and youth (94% retention). Extending prior examinations of posttest outcomes, intent-to-treat regression analyses tested for intervention effects at the 2 follow-up assessments, and growth curve analyses examined experimental condition differences in yearly change across time. Separate exploratory tests of moderation by youth gender, youth conduct problems, and family economic hardship also were conducted. Out of 52 regression models predicting 1- and 2-year follow-up outcomes, only 2 out of 104 possible intervention effects were statistically significant. No statistically significant intervention effects were found in the growth curve analyses. Tests of moderation also showed few statistically significant effects. Because CSP already is in widespread use, findings have direct implications for practice. Specifically, findings suggest that the program may not be efficacious with parents of adolescents in a selective prevention context and may reveal the limits of brief, general parent training for achieving outcomes with parents of adolescents. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Daily potassium intake and sodium-to-potassium ratio in the reduction of blood pressure: a meta-analysis of randomized controlled trials.

PubMed

Binia, Aristea; Jaeger, Jonathan; Hu, Youyou; Singh, Anurag; Zimmermann, Diane

2015-08-01

To evaluate the efficacy of daily potassium intake on decreasing blood pressure in non-medicated normotensive or hypertensive patients, and to determine the relationship between potassium intake, sodium-to-potassium ratio and reduction in blood pressure. Mixed-effect meta-analyses and meta-regression models. Medline and the references of previous meta-analyses. Randomized controlled trials with potassium supplementation, with blood pressure as the primary outcome, in non-medicated patients. Fifteen randomized controlled trials of potassium supplementation in patients without antihypertensive medication were selected for the meta-analyses (917 patients). Potassium supplementation resulted in reduction of SBP by 4.7 mmHg [95% confidence interval (CI) 2.4-7.0] and DBP by 3.5 mmHg (95% CI 1.3-5.7) in all patients. The effect was found to be greater in hypertensive patients, with a reduction of SBP by 6.8 mmHg (95% CI 4.3-9.3) and DBP by 4.6 mmHg (95% CI 1.8-7.5). Meta-regression analysis showed that both increased daily potassium excretion and decreased sodium-to-potassium ratio were associated with blood pressure reduction (P < 0.05). Increased total daily potassium urinary excretion from 60 to 100 mmol/day and decrease of sodium-to-potassium ratio were shown to be necessary to explain the estimated effect. Potassium supplementation is associated with reduction of blood pressure in patients who are not on antihypertensive medication, and the effect is significant in hypertensive patients. The reduction in blood pressure significantly correlates with decreased daily urinary sodium-to-potassium ratio and increased urinary potassium. Patients with elevated blood pressure may benefit from increased potassium intake along with controlled or decreased sodium intake.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

Multi-fidelity Gaussian process regression for prediction of random fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parussini, L.; Venturi, D., E-mail: venturi@ucsc.edu; Perdikaris, P.

We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgersmore » equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.« less

Epidemiological characteristics of reported sporadic and outbreak cases of E. coli O157 in people from Alberta, Canada (2000-2002): methodological challenges of comparing clustered to unclustered data.

PubMed

Pearl, D L; Louie, M; Chui, L; Doré, K; Grimsrud, K M; Martin, S W; Michel, P; Svenson, L W; McEwen, S A

2008-04-01

Using multivariable models, we compared whether there were significant differences between reported outbreak and sporadic cases in terms of their sex, age, and mode and site of disease transmission. We also determined the potential role of administrative, temporal, and spatial factors within these models. We compared a variety of approaches to account for clustering of cases in outbreaks including weighted logistic regression, random effects models, general estimating equations, robust variance estimates, and the random selection of one case from each outbreak. Age and mode of transmission were the only epidemiologically and statistically significant covariates in our final models using the above approaches. Weighing observations in a logistic regression model by the inverse of their outbreak size appeared to be a relatively robust and valid means for modelling these data. Some analytical techniques, designed to account for clustering, had difficulty converging or producing realistic measures of association.

Application of random effects to the study of resource selection by animals

USGS Publications Warehouse

Gillies, C.S.; Hebblewhite, M.; Nielsen, S.E.; Krawchuk, M.A.; Aldridge, Cameron L.; Frair, J.L.; Saher, D.J.; Stevens, C.E.; Jerde, C.L.

2006-01-01

1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence.2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability.3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed.4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects.5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection.6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.

Application of random effects to the study of resource selection by animals.

PubMed

Gillies, Cameron S; Hebblewhite, Mark; Nielsen, Scott E; Krawchuk, Meg A; Aldridge, Cameron L; Frair, Jacqueline L; Saher, D Joanne; Stevens, Cameron E; Jerde, Christopher L

2006-07-01

1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.

Effect of Metformin on Plasma Fibrinogen Concentrations: A Systematic Review and Meta-Analysis of Randomized Placebo-Controlled Trials.

PubMed

Simental-Mendia, Luis E; Pirro, Matteo; Atkin, Stephen L; Banach, Maciej; Mikhailidis, Dimitri P; Sahebkar, Amirhossein

2018-01-01

Fibrinogen is a key mediator of thrombosis and it has been implicated in the pathogenesis of atherosclerosis. Because metformin has shown a potential protective effect on different atherothrombotic risk factors, we assessed in this meta-analysis its effect on plasma fibrinogen concentrations. A systematic review and meta-analysis was carried out to identify randomized placebo-controlled trials evaluating the effect of metformin administration on fibrinogen levels. The search included PubMed-Medline, Scopus, ISI Web of Knowledge and Google Scholar databases (by June 2, 2017) and quality of studies was performed according to Cochrane criteria. Quantitative data synthesis was conducted using a random-effects model and sensitivity analysis by the leave-one-out method. Meta-regression analysis was performed to assess the modifiers of treatment response. Meta-analysis of data from 9 randomized placebo-controlled clinical trials with 2302 patients comprising 10 treatment arms did not suggest a significant change in plasma fibrinogen concentrations following metformin therapy (WMD: -0.25 g/L, 95% CI: -0.53, 0.04, p = 0.092). The effect size was robust in the leave-one-out sensitivity analysis and remained non-significant after omission of each single study from the meta-analysis. No significant effect of metformin on plasma fibrinogen concentrations was demonstrated in the current meta-analysis. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

A Highly Efficient Design Strategy for Regression with Outcome Pooling

PubMed Central

Mitchell, Emily M.; Lyles, Robert H.; Manatunga, Amita K.; Perkins, Neil J.; Schisterman, Enrique F.

2014-01-01

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. PMID:25220822

A highly efficient design strategy for regression with outcome pooling.

PubMed

Mitchell, Emily M; Lyles, Robert H; Manatunga, Amita K; Perkins, Neil J; Schisterman, Enrique F

2014-12-10

The potential for research involving biospecimens can be hindered by the prohibitive cost of performing laboratory assays on individual samples. To mitigate this cost, strategies such as randomly selecting a portion of specimens for analysis or randomly pooling specimens prior to performing laboratory assays may be employed. These techniques, while effective in reducing cost, are often accompanied by a considerable loss of statistical efficiency. We propose a novel pooling strategy based on the k-means clustering algorithm to reduce laboratory costs while maintaining a high level of statistical efficiency when predictor variables are measured on all subjects, but the outcome of interest is assessed in pools. We perform simulations motivated by the BioCycle study to compare this k-means pooling strategy with current pooling and selection techniques under simple and multiple linear regression models. While all of the methods considered produce unbiased estimates and confidence intervals with appropriate coverage, pooling under k-means clustering provides the most precise estimates, closely approximating results from the full data and losing minimal precision as the total number of pools decreases. The benefits of k-means clustering evident in the simulation study are then applied to an analysis of the BioCycle dataset. In conclusion, when the number of lab tests is limited by budget, pooling specimens based on k-means clustering prior to performing lab assays can be an effective way to save money with minimal information loss in a regression setting. Copyright © 2014 John Wiley & Sons, Ltd.

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

PubMed

Ishwaran, Hemant; Lu, Min

2018-06-04

Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric measure of variable importance (VIMP). A current limitation of VIMP, however, is that no systematic method exists for estimating its variance. As a solution, we propose a subsampling approach that can be used to estimate the variance of VIMP and for constructing confidence intervals. The method is general enough that it can be applied to many useful settings, including regression, classification, and survival problems. Using extensive simulations, we demonstrate the effectiveness of the subsampling estimator and in particular find that the delete-d jackknife variance estimator, a close cousin, is especially effective under low subsampling rates due to its bias correction properties. These 2 estimators are highly competitive when compared with the .164 bootstrap estimator, a modified bootstrap procedure designed to deal with ties in out-of-sample data. Most importantly, subsampling is computationally fast, thus making it especially attractive for big data settings. Copyright © 2018 John Wiley & Sons, Ltd.

Polynomial order selection in random regression models via penalizing adaptively the likelihood.

PubMed

Corrales, J D; Munilla, S; Cantet, R J C

2015-08-01

Orthogonal Legendre polynomials (LP) are used to model the shape of additive genetic and permanent environmental effects in random regression models (RRM). Frequently, the Akaike (AIC) and the Bayesian (BIC) information criteria are employed to select LP order. However, it has been theoretically shown that neither AIC nor BIC is simultaneously optimal in terms of consistency and efficiency. Thus, the goal was to introduce a method, 'penalizing adaptively the likelihood' (PAL), as a criterion to select LP order in RRM. Four simulated data sets and real data (60,513 records, 6675 Colombian Holstein cows) were employed. Nested models were fitted to the data, and AIC, BIC and PAL were calculated for all of them. Results showed that PAL and BIC identified with probability of one the true LP order for the additive genetic and permanent environmental effects, but AIC tended to favour over parameterized models. Conversely, when the true model was unknown, PAL selected the best model with higher probability than AIC. In the latter case, BIC never favoured the best model. To summarize, PAL selected a correct model order regardless of whether the 'true' model was within the set of candidates. © 2015 Blackwell Verlag GmbH.

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian; Punjabi, Naresh M.

2013-01-01

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased to non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear models with GEE for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis. PMID:22241689

Composition-dependence of stacking fault energy in austenitic stainless steels through linear regression with random intercepts

NASA Astrophysics Data System (ADS)

Meric de Bellefon, G.; van Duysen, J. C.; Sridharan, K.

2017-08-01

The stacking fault energy (SFE) plays an important role in deformation behavior and radiation damage of FCC metals and alloys such as austenitic stainless steels. In the present communication, existing expressions to calculate SFE in those steels from chemical composition are reviewed and an improved multivariate linear regression with random intercepts is used to analyze a new database of 144 SFE measurements collected from 30 literature references. It is shown that the use of random intercepts can account for experimental biases in these literature references. A new expression to predict SFE from austenitic stainless steel compositions is proposed.

The PX-EM algorithm for fast stable fitting of Henderson's mixed model

PubMed Central

Foulley, Jean-Louis; Van Dyk, David A

2000-01-01

This paper presents procedures for implementing the PX-EM algorithm of Liu, Rubin and Wu to compute REML estimates of variance covariance components in Henderson's linear mixed models. The class of models considered encompasses several correlated random factors having the same vector length e.g., as in random regression models for longitudinal data analysis and in sire-maternal grandsire models for genetic evaluation. Numerical examples are presented to illustrate the procedures. Much better results in terms of convergence characteristics (number of iterations and time required for convergence) are obtained for PX-EM relative to the basic EM algorithm in the random regression. PMID:14736399

Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data

PubMed Central

DiPrete, Thomas A.; Burik, Casper A. P.; Koellinger, Philipp D.

2018-01-01

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA. PMID:29686100

Genetic instrumental variable regression: Explaining socioeconomic and health outcomes in nonexperimental data.

PubMed

DiPrete, Thomas A; Burik, Casper A P; Koellinger, Philipp D

2018-05-29

Identifying causal effects in nonexperimental data is an enduring challenge. One proposed solution that recently gained popularity is the idea to use genes as instrumental variables [i.e., Mendelian randomization (MR)]. However, this approach is problematic because many variables of interest are genetically correlated, which implies the possibility that many genes could affect both the exposure and the outcome directly or via unobserved confounding factors. Thus, pleiotropic effects of genes are themselves a source of bias in nonexperimental data that would also undermine the ability of MR to correct for endogeneity bias from nongenetic sources. Here, we propose an alternative approach, genetic instrumental variable (GIV) regression, that provides estimates for the effect of an exposure on an outcome in the presence of pleiotropy. As a valuable byproduct, GIV regression also provides accurate estimates of the chip heritability of the outcome variable. GIV regression uses polygenic scores (PGSs) for the outcome of interest which can be constructed from genome-wide association study (GWAS) results. By splitting the GWAS sample for the outcome into nonoverlapping subsamples, we obtain multiple indicators of the outcome PGSs that can be used as instruments for each other and, in combination with other methods such as sibling fixed effects, can address endogeneity bias from both pleiotropy and the environment. In two empirical applications, we demonstrate that our approach produces reasonable estimates of the chip heritability of educational attainment (EA) and show that standard regression and MR provide upwardly biased estimates of the effect of body height on EA. Copyright © 2018 the Author(s). Published by PNAS.

Assessing mediation using marginal structural models in the presence of confounding and moderation

PubMed Central

Coffman, Donna L.; Zhong, Wei

2012-01-01

This paper presents marginal structural models (MSMs) with inverse propensity weighting (IPW) for assessing mediation. Generally, individuals are not randomly assigned to levels of the mediator. Therefore, confounders of the mediator and outcome may exist that limit causal inferences, a goal of mediation analysis. Either regression adjustment or IPW can be used to take confounding into account, but IPW has several advantages. Regression adjustment of even one confounder of the mediator and outcome that has been influenced by treatment results in biased estimates of the direct effect (i.e., the effect of treatment on the outcome that does not go through the mediator). One advantage of IPW is that it can properly adjust for this type of confounding, assuming there are no unmeasured confounders. Further, we illustrate that IPW estimation provides unbiased estimates of all effects when there is a baseline moderator variable that interacts with the treatment, when there is a baseline moderator variable that interacts with the mediator, and when the treatment interacts with the mediator. IPW estimation also provides unbiased estimates of all effects in the presence of non-randomized treatments. In addition, for testing mediation we propose a test of the null hypothesis of no mediation. Finally, we illustrate this approach with an empirical data set in which the mediator is continuous, as is often the case in psychological research. PMID:22905648

Cooperation without Culture? The Null Effect of Generalized Trust on Intentional Homicide: A Cross-National Panel Analysis, 1995–2009

PubMed Central

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation. PMID:23527211

How Does Physical Activity Intervention Improve Self-Esteem and Self-Concept in Children and Adolescents? Evidence from a Meta-Analysis.

PubMed

Liu, Mingli; Wu, Lang; Ming, Qingsen

2015-01-01

To perform a systematic review and meta-analysis for the effects of physical activity intervention on self-esteem and self-concept in children and adolescents, and to identify moderator variables by meta-regression. A meta-analysis and meta-regression. Relevant studies were identified through a comprehensive search of electronic databases. Study inclusion criteria were: (1) intervention should be supervised physical activity, (2) reported sufficient data to estimate pooled effect sizes of physical activity intervention on self-esteem or self-concept, (3) participants' ages ranged from 3 to 20 years, and (4) a control or comparison group was included. For each study, study design, intervention design and participant characteristics were extracted. R software (version 3.1.3) and Stata (version 12.0) were used to synthesize effect sizes and perform moderation analyses for determining moderators. Twenty-five randomized controlled trial (RCT) studies and 13 non-randomized controlled trial (non-RCT) studies including a total of 2991 cases were identified. Significant positive effects were found in RCTs for intervention of physical activity alone on general self outcomes (Hedges' g = 0.29, 95% confidence interval [CI]: 0.14 to 0.45; p = 0.001), self-concept (Hedges' g = 0.49, 95%CI: 0.10 to 0.88, p = 0.014) and self-worth (Hedges' g = 0.31, 95%CI: 0.13 to 0.49, p = 0.005). There was no significant effect of intervention of physical activity alone on any outcomes in non-RCTs, as well as in studies with intervention of physical activity combined with other strategies. Meta-regression analysis revealed that higher treatment effects were associated with setting of intervention in RCTs (β = 0.31, 95%CI: 0.07 to 0.55, p = 0.013). Intervention of physical activity alone is associated with increased self-concept and self-worth in children and adolescents. And there is a stronger association with school-based and gymnasium-based intervention compared with other settings.

Meta-analysis and meta-regression of omega-3 polyunsaturated fatty acid supplementation for major depressive disorder.

PubMed

Mocking, R J T; Harmsen, I; Assies, J; Koeter, M W J; Ruhé, H G; Schene, A H

2016-03-15

Omega-3 polyunsaturated fatty acid (PUFA) supplementation has been proposed as (adjuvant) treatment for major depressive disorder (MDD). In the present meta-analysis, we pooled randomized placebo-controlled trials assessing the effects of omega-3 PUFA supplementation on depressive symptoms in MDD. Moreover, we performed meta-regression to test whether supplementation effects depended on eicosapentaenoic acid (EPA) or docosahexaenoic acid dose, their ratio, study duration, participants' age, percentage antidepressant users, baseline MDD symptom severity, publication year and study quality. To limit heterogeneity, we only included studies in adult patients with MDD assessed using standardized clinical interviews, and excluded studies that specifically studied perinatal/perimenopausal or comorbid MDD. Our PubMED/EMBASE search resulted in 1955 articles, from which we included 13 studies providing 1233 participants. After taking potential publication bias into account, meta-analysis showed an overall beneficial effect of omega-3 PUFAs on depressive symptoms in MDD (standardized mean difference=0.398 (0.114-0.682), P=0.006, random-effects model). As an explanation for significant heterogeneity (I(2)=73.36, P<0.001), meta-regression showed that higher EPA dose (β=0.00037 (0.00009-0.00065), P=0.009), higher percentage antidepressant users (β=0.0058 (0.00017-0.01144), P=0.044) and earlier publication year (β=-0.0735 (-0.143 to 0.004), P=0.04) were significantly associated with better outcome for PUFA supplementation. Additional sensitivity analyses were performed. In conclusion, present meta-analysis suggested a beneficial overall effect of omega-3 PUFA supplementation in MDD patients, especially for higher doses of EPA and in participants taking antidepressants. Future precision medicine trials should establish whether possible interactions between EPA and antidepressants could provide targets to improve antidepressant response and its prediction. Furthermore, potential long-term biochemical side effects of high-dosed add-on EPA supplementation should be carefully monitored.

Re-analysis of health and educational impacts of a school-based deworming programme in western Kenya: a statistical replication of a cluster quasi-randomized stepped-wedge trial

PubMed Central

Davey, Calum; Aiken, Alexander M; Hayes, Richard J; Hargreaves, James R

2015-01-01

Introduction: Helminth (worm) infections cause morbidity among poor communities worldwide. An influential study conducted in Kenya in 1998–99 reported that a school-based drug-and-educational intervention had benefits for worm infections and school attendance. Methods: In this statistical replication, we re-analysed data from this cluster quasi-randomized stepped-wedge trial, specifying two co-primary outcomes: school attendance and examination performance. We estimated intention-to-treat effects using year-stratified cluster-summary analysis and observation-level random-effects regression, and combined both years with a random-effects model accounting for year. The participants were not blinded to allocation status, and other interventions were concurrently conducted in a sub-set of schools. A protocol guiding outcome data collection was not available. Results: Quasi-randomization resulted in three similar groups of 25 schools. There was a substantial amount of missing data. In year-stratified cluster-summary analysis, there was no clear evidence for improvement in either school attendance or examination performance. In year-stratified regression models, there was some evidence of improvement in school attendance [adjusted odds ratios (aOR): year 1: 1.48, 95% confidence interval (CI) 0.88–2.52, P = 0.147; year 2: 1.23, 95% CI 1.01–1.51, P = 0.044], but not examination performance (adjusted differences: year 1: −0.135, 95% CI −0.323–0.054, P = 0.161; year 2: −0.017, 95% CI −0.201–0.166, P = 0.854). When both years were combined, there was strong evidence of an effect on attendance (aOR 1.82, 95% CI 1.74–1.91, P < 0.001), but not examination performance (adjusted difference −0.121, 95% CI −0.293–0.052, P = 0.169). Conclusions: The evidence supporting an improvement in school attendance differed by analysis method. This, and various other important limitations of the data, caution against over-interpretation of the results. We find that the study provides some evidence, but with high risk of bias, that a school-based drug-treatment and health-education intervention improved school attendance and no evidence of effect on examination performance. PMID:26203171

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

The Effect of Sitagliptin on the Regression of Carotid Intima-Media Thickening in Patients with Type 2 Diabetes Mellitus: A Post Hoc Analysis of the Sitagliptin Preventive Study of Intima-Media Thickness Evaluation.

PubMed

Mita, Tomoya; Katakami, Naoto; Shiraiwa, Toshihiko; Yoshii, Hidenori; Gosho, Masahiko; Shimomura, Iichiro; Watada, Hirotaka

2017-01-01

Background. The effect of dipeptidyl peptidase-4 (DPP-4) inhibitors on the regression of carotid IMT remains largely unknown. The present study aimed to clarify whether sitagliptin, DPP-4 inhibitor, could regress carotid intima-media thickness (IMT) in insulin-treated patients with type 2 diabetes mellitus (T2DM). Methods . This is an exploratory analysis of a randomized trial in which we investigated the effect of sitagliptin on the progression of carotid IMT in insulin-treated patients with T2DM. Here, we compared the efficacy of sitagliptin treatment on the number of patients who showed regression of carotid IMT of ≥0.10 mm in a post hoc analysis. Results . The percentages of the number of the patients who showed regression of mean-IMT-CCA (28.9% in the sitagliptin group versus 16.4% in the conventional group, P  = 0.022) and left max-IMT-CCA (43.0% in the sitagliptin group versus 26.2% in the conventional group, P  = 0.007), but not right max-IMT-CCA, were higher in the sitagliptin treatment group compared with those in the non-DPP-4 inhibitor treatment group. In multiple logistic regression analysis, sitagliptin treatment significantly achieved higher target attainment of mean-IMT-CCA ≥0.10 mm and right and left max-IMT-CCA ≥0.10 mm compared to conventional treatment. Conclusions . Our data suggested that DPP-4 inhibitors were associated with the regression of carotid atherosclerosis in insulin-treated T2DM patients. This study has been registered with the University Hospital Medical Information Network Clinical Trials Registry (UMIN000007396).

Genetic correlations among body condition score, yield, and fertility in first-parity cows estimated by random regression models.

PubMed

Veerkamp, R F; Koenen, E P; De Jong, G

2001-10-01

Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.

Normalization Regression Estimation With Application to a Nonorthogonal, Nonrecursive Model of School Learning.

ERIC Educational Resources Information Center

Bulcock, J. W.; And Others

Advantages of normalization regression estimation over ridge regression estimation are demonstrated by reference to Bloom's model of school learning. Theoretical concern centered on the structure of scholastic achievement at grade 10 in Canadian high schools. Data on 886 students were randomly sampled from the Carnegie Human Resources Data Bank.…

Biases and Standard Errors of Standardized Regression Coefficients

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Chan, Wai

2011-01-01

The paper obtains consistent standard errors (SE) and biases of order O(1/n) for the sample standardized regression coefficients with both random and given predictors. Analytical results indicate that the formulas for SEs given in popular text books are consistent only when the population value of the regression coefficient is zero. The sample…

Optimizing Prophylactic CPAP in Patients Without Obstructive Sleep Apnoea for High-Risk Abdominal Surgeries: A Meta-regression Analysis.

PubMed

Singh, Preet Mohinder; Borle, Anuradha; Shah, Dipal; Sinha, Ashish; Makkar, Jeetinder Kaur; Trikha, Anjan; Goudra, Basavana Gouda

2016-04-01

Prophylactic continuous positive airway pressure (CPAP) can prevent pulmonary adverse events following upper abdominal surgeries. The present meta-regression evaluates and quantifies the effect of degree/duration of (CPAP) on the incidence of postoperative pulmonary events. Medical databases were searched for randomized controlled trials involving adult patients, comparing the outcome in those receiving prophylactic postoperative CPAP versus no CPAP, undergoing high-risk abdominal surgeries. Our meta-analysis evaluated the relationship between the postoperative pulmonary complications and the use of CPAP. Furthermore, meta-regression was used to quantify the effect of cumulative duration and degree of CPAP on the measured outcomes. Seventy-three potentially relevant studies were identified, of which 11 had appropriate data, allowing us to compare a total of 362 and 363 patients in CPAP and control groups, respectively. Qualitatively, Odds ratio for CPAP showed protective effect for pneumonia [0.39 (0.19-0.78)], atelectasis [0.51 (0.32-0.80)] and pulmonary complications [0.37 (0.24-0.56)] with zero heterogeneity. For prevention of pulmonary complications, odds ratio was better for continuous than intermittent CPAP. Meta-regression demonstrated a positive correlation between the degree of CPAP and the incidence of pneumonia with a regression coefficient of +0.61 (95 % CI 0.02-1.21, P = 0.048, τ (2) = 0.078, r (2) = 7.87 %). Overall, adverse effects were similar with or without the use of CPAP. Prophylactic postoperative use of continuous CPAP significantly reduces the incidence of postoperative pneumonia, atelectasis and pulmonary complications in patients undergoing high-risk abdominal surgeries. Quantitatively, increasing the CPAP levels does not necessarily enhance the protective effect against pneumonia. Instead, protective effect diminishes with increasing degree of CPAP.

Generated effect modifiers (GEM’s) in randomized clinical trials

PubMed Central

Petkova, Eva; Tarpey, Thaddeus; Su, Zhe; Ogden, R. Todd

2017-01-01

In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an “effect modifier”. Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration. PMID:27465235

Exploring reasons for the observed inconsistent trial reports on intra-articular injections with hyaluronic acid in the treatment of osteoarthritis: Meta-regression analyses of randomized trials.

PubMed

Johansen, Mette; Bahrt, Henriette; Altman, Roy D; Bartels, Else M; Juhl, Carsten B; Bliddal, Henning; Lund, Hans; Christensen, Robin

2016-08-01

The aim was to identify factors explaining inconsistent observations concerning the efficacy of intra-articular hyaluronic acid compared to intra-articular sham/control, or non-intervention control, in patients with symptomatic osteoarthritis, based on randomized clinical trials (RCTs). A systematic review and meta-regression analyses of available randomized trials were conducted. The outcome, pain, was assessed according to a pre-specified hierarchy of potentially available outcomes. Hedges׳s standardized mean difference [SMD (95% CI)] served as effect size. REstricted Maximum Likelihood (REML) mixed-effects models were used to combine study results, and heterogeneity was calculated and interpreted as Tau-squared and I-squared, respectively. Overall, 99 studies (14,804 patients) met the inclusion criteria: Of these, only 71 studies (72%), including 85 comparisons (11,216 patients), had adequate data available for inclusion in the primary meta-analysis. Overall, compared with placebo, intra-articular hyaluronic acid reduced pain with an effect size of -0.39 [-0.47 to -0.31; P < 0.001], combining very heterogeneous trial findings (I(2) = 73%). The three most important covariates in reducing heterogeneity were overall risk of bias, blinding of personnel and trial size, reducing heterogeneity with 26%, 26%, and 25%, respectively (Interaction: P ≤ 0.001). Adjusting for publication/selective outcome reporting bias (by imputing "null effects") in 24 of the comparisons with no data available reduced the combined estimate to -0.30 [-0.36 to -0.23; P < 0.001] still in favor of hyaluronic acid. Based on available trial data, intra-articular hyaluronic acid showed a better effect than intra-articular saline on pain reduction in osteoarthritis. Publication bias and the risk of selective outcome reporting suggest only small clinical effect compared to saline. Copyright © 2016 Elsevier Inc. All rights reserved.

Maternal steroid therapy for fetuses with second-degree immune-mediated congenital atrioventricular block: a systematic review and meta-analysis.

PubMed

Ciardulli, Andrea; D'Antonio, Francesco; Magro-Malosso, Elena R; Manzoli, Lamberto; Anisman, Paul; Saccone, Gabriele; Berghella, Vincenzo

2018-03-07

To explore the effect of maternal fluorinated steroid therapy on fetuses affected by second-degree immune-mediated congenital atrioventricular block. Studies reporting the outcome of fetuses with second-degree immune-mediated congenital atrioventricular block diagnosed on prenatal ultrasound and treated with fluorinated steroids compared with those not treated were included. The primary outcome was the overall progression of congenital atrioventricular block to either continuous or intermittent third-degree congenital atrioventricular block at birth. Meta-analyses of proportions using random effect model and meta-analyses using individual data random-effect logistic regression were used. Five studies (71 fetuses) were included. The progression rate to congenital atrioventricular block at birth in fetuses treated with steroids was 52% (95% confidence interval 23-79) and in fetuses not receiving steroid therapy 73% (95% confidence interval 39-94). The overall rate of regression to either first-degree, intermittent first-/second-degree or sinus rhythm in fetuses treated with steroids was 25% (95% confidence interval 12-41) compared with 23% (95% confidence interval 8-44) in those not treated. Stable (constant) second-degree congenital atrioventricular block at birth was present in 11% (95% confidence interval 2-27) of cases in the treated group and in none of the newborns in the untreated group, whereas complete regression to sinus rhythm occurred in 21% (95% confidence interval 6-42) of fetuses receiving steroids vs. 9% (95% confidence interval 0-41) of those untreated. There is still limited evidence as to the benefit of administered fluorinated steroids in terms of affecting outcome of fetuses with second-degree immune-mediated congenital atrioventricular block. © 2018 Nordic Federation of Societies of Obstetrics and Gynecology.

Teaching quality: High school students' autonomy and competence.

PubMed

León, Jaime; Medina-Garrido, Elena; Ortega, Miriam

2018-05-01

How teachers manage class learning and interact with students affects students’ motivation and engagement. However, it could be that the effect of students’ representation of teaching quality on the students’ motivation varies between classes. Students from 90 classes participated in the study. We used multilevel random structural equation modeling to analyze whether the relationship of the students’ perception of teaching quality (as an indicator of the students’ mental representation) and students’ motivation varies between classes, and if this variability depends on the class assessment of teaching quality (as an indicator of teaching quality). The effect of teachers’ structure on the regression slope of student perception of student competence was .127. The effect of teachers’ autonomy support on the regression slope of student perception of student autonomy was .066. With this study we contribute a more detailed description of the relationship between teaching quality, competence and autonomy.

Less money, more problems: How changes in disposable income affect child maltreatment.

PubMed

McLaughlin, Michael

2017-05-01

A number of research studies have documented an association between child maltreatment and family income. Yet, little is known about the specific types of economic shocks that affect child maltreatment rates. The paucity of information is troubling given that more than six million children are reported for maltreatment annually in the U.S. alone. This study examines whether an exogenous shock to families' disposable income, a change in the price of gasoline, predicts changes in child maltreatment. The findings of a fixed-effects regression show that increases in state-level gas prices are associated with increases in state-level child maltreatment referral rates, even after controlling for demographic and other economic variables. The results are robust to the manner of estimation; random-effects and mixed-effects regressions produce similar estimates. The findings suggest that fluctuations in the price of gas may have important consequences for children. Copyright © 2017 Elsevier Ltd. All rights reserved.

Retinal microaneurysm count predicts progression and regression of diabetic retinopathy. Post-hoc results from the DIRECT Programme.

PubMed

Sjølie, A K; Klein, R; Porta, M; Orchard, T; Fuller, J; Parving, H H; Bilous, R; Aldington, S; Chaturvedi, N

2011-03-01

To study the association between baseline retinal microaneurysm score and progression and regression of diabetic retinopathy, and response to treatment with candesartan in people with diabetes. This was a multicenter randomized clinical trial. The progression analysis included 893 patients with Type 1 diabetes and 526 patients with Type 2 diabetes with retinal microaneurysms only at baseline. For regression, 438 with Type 1 and 216 with Type 2 diabetes qualified. Microaneurysms were scored from yearly retinal photographs according to the Early Treatment Diabetic Retinopathy Study (ETDRS) protocol. Retinopathy progression and regression was defined as two or more step change on the ETDRS scale from baseline. Patients were normoalbuminuric, and normotensive with Type 1 and Type 2 diabetes or treated hypertensive with Type 2 diabetes. They were randomized to treatment with candesartan 32 mg daily or placebo and followed for 4.6 years. A higher microaneurysm score at baseline predicted an increased risk of retinopathy progression (HR per microaneurysm score 1.08, P < 0.0001 in Type 1 diabetes; HR 1.07, P = 0.0174 in Type 2 diabetes) and reduced the likelihood of regression (HR 0.79, P < 0.0001 in Type 1 diabetes; HR 0.85, P = 0.0009 in Type 2 diabetes), all adjusted for baseline variables and treatment. Candesartan reduced the risk of microaneurysm score progression. Microaneurysm counts are important prognostic indicators for worsening of retinopathy, thus microaneurysms are not benign. Treatment with renin-angiotensin system inhibitors is effective in the early stages and may improve mild diabetic retinopathy. Microaneurysm scores may be useful surrogate endpoints in clinical trials. © 2011 The Authors. Diabetic Medicine © 2011 Diabetes UK.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

USGS Publications Warehouse

Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

2017-01-01

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

Systematic review using meta-analyses to estimate dose-response relationships between iodine intake and biomarkers of iodine status in different population groups.

PubMed

Ristić-Medić, Danijela; Dullemeijer, Carla; Tepsić, Jasna; Petrović-Oggiano, Gordana; Popović, Tamara; Arsić, Aleksandra; Glibetić, Marija; Souverein, Olga W; Collings, Rachel; Cavelaars, Adriënne; de Groot, Lisette; van't Veer, Pieter; Gurinović, Mirjana

2014-03-01

The objective of this systematic review was to identify studies investigating iodine intake and biomarkers of iodine status, to assess the data of the selected studies, and to estimate dose-response relationships using meta-analysis. All randomized controlled trials, prospective cohort studies, nested case-control studies, and cross-sectional studies that supplied or measured dietary iodine and measured iodine biomarkers were included. The overall pooled regression coefficient (β) and the standard error of β were calculated by random-effects meta-analysis on a double-log scale, using the calculated intake-status regression coefficient (β) for each individual study. The results of pooled randomized controlled trials indicated that the doubling of dietary iodine intake increased urinary iodine concentrations by 14% in children and adolescents, by 57% in adults and the elderly, and by 81% in pregnant women. The dose-response relationship between iodine intake and biomarkers of iodine status indicated a 12% decrease in thyroid-stimulating hormone and a 31% decrease in thyroglobulin in pregnant women. The model of dose-response quantification used to describe the relationship between iodine intake and biomarkers of iodine status may be useful for providing complementary evidence to support recommendations for iodine intake in different population groups.

The effect of a school-based intervention on sunbed use in Danish pupils at continuation schools: a cluster-randomized controlled trial.

PubMed

Aarestrup, Cecilie; Bonnesen, Camilla T; Thygesen, Lau C; Krarup, Anne F; Waagstein, Anne B; Jensen, Poul D; Bentzen, Joan

2014-02-01

To examine the effect of an educational intervention on sunbed use and intentions and attitudes toward sunbed use in 14- to 18-year-olds at continuation schools. We randomized 33 continuation schools either to receive the educational intervention (n = 16) or to be controls (n = 17). Intervention schools received an e-magazine addressing the health risks of sunbed use. Information on behavior and intentions and attitudes toward sunbed use was gathered through self-administrated questionnaires before the intervention and at 6 months as a follow-up. The effect of the intervention was examined by multilevel linear regression and logistic regression. Sunbed use was significantly lower at follow-up among pupils at intervention schools versus pupils at control schools (girls: odds ratio .60, 95% confidence interval .42-.86; Boys: odds ratio .58, 95% confidence interval .35-.96). The intervention had no effect on intention to use sunbeds or attitudes toward sunbed use. The analyses revealed a significant impact of school on attitudes toward sunbed; the intraclass correlation coefficient was estimated to be 6.0% and 7.8% for girls and boys, respectively. The findings from the present study provide new evidence of a positive effect of an educational intervention on sunbed use among pupils aged 14-18 years at continuation schools. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Effects of a randomized controlled trial to assess the six-months effects of a school based smoking prevention program in Saudi Arabia.

PubMed

Mohammed, Mutaz; Eggers, Sander Matthijs; Alotaiby, Fahad F; de Vries, Nanne; de Vries, Hein

2016-09-01

To examine the efficacy of a smoking prevention program which aimed to address smoking related cognitions and smoking behavior among Saudi adolescents age 13 to 15. A randomized controlled trial was used. Respondents in the experimental group (N=698) received five in-school sessions, while those in the control group (N=683) received no smoking prevention information (usual curriculum). Post-intervention data was collected six months after baseline. Logistic regression analysis was applied to assess effects on smoking initiation, and linear regression analysis was applied to assess changes in beliefs and analysis of covariance (ANCOVA) was used to assess intervention effects. All analyses were adjusted for the nested structure of students within schools. At post-intervention respondents from the experimental group reported in comparison with those from the control group a significantly more negative attitude towards smoking, stronger social norms against smoking, higher self-efficacy towards non-smoking, more action planning to remain a non-smoker, and lower intentions to smoke in the future. Smoking initiation was 3.2% in the experimental group and 8.8% in the control group (p<0.01). The prevention program reinforced non-smoking cognitions and non-smoking behavior. Therefore it is recommended to implement the program at a national level in Saudi-Arabia. Future studies are recommended to assess long term program effects and the conditions favoring national implementation of the program. Copyright © 2016 Elsevier Inc. All rights reserved.

Effectiveness of Psychological and/or Educational Interventions in the Prevention of Anxiety: A Systematic Review, Meta-analysis, and Meta-regression.

PubMed

Moreno-Peral, Patricia; Conejo-Cerón, Sonia; Rubio-Valera, Maria; Fernández, Anna; Navas-Campaña, Desirée; Rodríguez-Morejón, Alberto; Motrico, Emma; Rigabert, Alina; Luna, Juan de Dios; Martín-Pérez, Carlos; Rodríguez-Bayón, Antonina; Ballesta-Rodríguez, María Isabel; Luciano, Juan Vicente; Bellón, Juan Ángel

2017-10-01

To our knowledge, no systematic reviews or meta-analyses have been conducted to assess the effectiveness of preventive psychological and/or educational interventions for anxiety in varied populations. To evaluate the effectiveness of preventive psychological and/or educational interventions for anxiety in varied population types. A systematic review and meta-analysis was conducted based on literature searches of MEDLINE, PsycINFO, Web of Science, EMBASE, OpenGrey, Cochrane Central Register of Controlled Trials, and other sources from inception to March 7, 2017. A search was performed of randomized clinical trials assessing the effectiveness of preventive psychological and/or educational interventions for anxiety in varying populations free of anxiety at baseline as measured using validated instruments. There was no setting or language restriction. Eligibility criteria assessment was conducted by 2 of us. Data extraction and assessment of risk of bias (Cochrane Collaboration's tool) were performed by 2 of us. Pooled standardized mean differences (SMDs) were calculated using random-effect models. Heterogeneity was explored by random-effects meta-regression. Incidence of new cases of anxiety disorders or reduction of anxiety symptoms as measured by validated instruments. Of the 3273 abstracts reviewed, 131 were selected for full-text review, and 29 met the inclusion criteria, representing 10 430 patients from 11 countries on 4 continents. Meta-analysis calculations were based on 36 comparisons. The pooled SMD was -0.31 (95% CI, -0.40 to -0.21; P < .001) and heterogeneity was substantial (I2 = 61.1%; 95% CI, 44% to 73%). There was evidence of publication bias, but the effect size barely varied after adjustment (SMD, -0.27; 95% CI, -0.37 to -0.17; P < .001). Sensitivity analyses confirmed the robustness of effect size results. A meta-regression including 5 variables explained 99.6% of between-study variability, revealing an association between higher SMD, waiting list (comparator) (β = -0.33 [95% CI, -0.55 to -0.11]; P = .005) and a lower sample size (lg) (β = 0.15 [95% CI, 0.06 to 0.23]; P = .001). No association was observed with risk of bias, family physician providing intervention, and use of standardized interviews as outcomes. Psychological and/or educational interventions had a small but statistically significant benefit for anxiety prevention in all populations evaluated. Although more studies with larger samples and active comparators are needed, these findings suggest that anxiety prevention programs should be further developed and implemented.

Regression Artifacts in Nonequivalent Control Group Designs: An Empirical Investigation of Bias in ANCOVA and Matching Designs.

ERIC Educational Resources Information Center

Vermillion, James E.

The presence of artifactual bias in analysis of covariance (ANCOVA) and in matching nonequivalent control group (NECG) designs was empirically investigated. The data set was obtained from a study of the effects of a television program on children from three day care centers in Mexico in which the subjects had been randomly selected within centers.…

Subgroup Analysis of Trials Is Rarely Easy (SATIRE): a study protocol for a systematic review to characterize the analysis, reporting, and claim of subgroup effects in randomized trials.

PubMed

Sun, Xin; Briel, Matthias; Busse, Jason W; Akl, Elie A; You, John J; Mejza, Filip; Bala, Malgorzata; Diaz-Granados, Natalia; Bassler, Dirk; Mertz, Dominik; Srinathan, Sadeesh K; Vandvik, Per Olav; Malaga, German; Alshurafa, Mohamed; Dahm, Philipp; Alonso-Coello, Pablo; Heels-Ansdell, Diane M; Bhatnagar, Neera; Johnston, Bradley C; Wang, Li; Walter, Stephen D; Altman, Douglas G; Guyatt, Gordon H

2009-11-09

Subgroup analyses in randomized trials examine whether effects of interventions differ between subgroups of study populations according to characteristics of patients or interventions. However, findings from subgroup analyses may be misleading, potentially resulting in suboptimal clinical and health decision making. Few studies have investigated the reporting and conduct of subgroup analyses and a number of important questions remain unanswered. The objectives of this study are: 1) to describe the reporting of subgroup analyses and claims of subgroup effects in randomized controlled trials, 2) to assess study characteristics associated with reporting of subgroup analyses and with claims of subgroup effects, and 3) to examine the analysis, and interpretation of subgroup effects for each study's primary outcome. We will conduct a systematic review of 464 randomized controlled human trials published in 2007 in the 118 Core Clinical Journals defined by the National Library of Medicine. We will randomly select journal articles, stratified in a 1:1 ratio by higher impact versus lower impact journals. According to 2007 ISI total citations, we consider the New England Journal of Medicine, JAMA, Lancet, Annals of Internal Medicine, and BMJ as higher impact journals. Teams of two reviewers will independently screen full texts of reports for eligibility, and abstract data, using standardized, pilot-tested extraction forms. We will conduct univariable and multivariable logistic regression analyses to examine the association of pre-specified study characteristics with reporting of subgroup analyses and with claims of subgroup effects for the primary and any other outcomes. A clear understanding of subgroup analyses, as currently conducted and reported in published randomized controlled trials, will reveal both strengths and weaknesses of this practice. Our findings will contribute to a set of recommendations to optimize the conduct and reporting of subgroup analyses, and claim and interpretation of subgroup effects in randomized trials.

Subgroup Analysis of Trials Is Rarely Easy (SATIRE): a study protocol for a systematic review to characterize the analysis, reporting, and claim of subgroup effects in randomized trials

PubMed Central

Sun, Xin; Briel, Matthias; Busse, Jason W; Akl, Elie A; You, John J; Mejza, Filip; Bala, Malgorzata; Diaz-Granados, Natalia; Bassler, Dirk; Mertz, Dominik; Srinathan, Sadeesh K; Vandvik, Per Olav; Malaga, German; Alshurafa, Mohamed; Dahm, Philipp; Alonso-Coello, Pablo; Heels-Ansdell, Diane M; Bhatnagar, Neera; Johnston, Bradley C; Wang, Li; Walter, Stephen D; Altman, Douglas G; Guyatt, Gordon H

2009-01-01

Background Subgroup analyses in randomized trials examine whether effects of interventions differ between subgroups of study populations according to characteristics of patients or interventions. However, findings from subgroup analyses may be misleading, potentially resulting in suboptimal clinical and health decision making. Few studies have investigated the reporting and conduct of subgroup analyses and a number of important questions remain unanswered. The objectives of this study are: 1) to describe the reporting of subgroup analyses and claims of subgroup effects in randomized controlled trials, 2) to assess study characteristics associated with reporting of subgroup analyses and with claims of subgroup effects, and 3) to examine the analysis, and interpretation of subgroup effects for each study's primary outcome. Methods We will conduct a systematic review of 464 randomized controlled human trials published in 2007 in the 118 Core Clinical Journals defined by the National Library of Medicine. We will randomly select journal articles, stratified in a 1:1 ratio by higher impact versus lower impact journals. According to 2007 ISI total citations, we consider the New England Journal of Medicine, JAMA, Lancet, Annals of Internal Medicine, and BMJ as higher impact journals. Teams of two reviewers will independently screen full texts of reports for eligibility, and abstract data, using standardized, pilot-tested extraction forms. We will conduct univariable and multivariable logistic regression analyses to examine the association of pre-specified study characteristics with reporting of subgroup analyses and with claims of subgroup effects for the primary and any other outcomes. Discussion A clear understanding of subgroup analyses, as currently conducted and reported in published randomized controlled trials, will reveal both strengths and weaknesses of this practice. Our findings will contribute to a set of recommendations to optimize the conduct and reporting of subgroup analyses, and claim and interpretation of subgroup effects in randomized trials. PMID:19900273

Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic.

PubMed

Bowden, Jack; Del Greco M, Fabiola; Minelli, Cosetta; Davey Smith, George; Sheehan, Nuala A; Thompson, John R

2016-12-01

: MR-Egger regression has recently been proposed as a method for Mendelian randomization (MR) analyses incorporating summary data estimates of causal effect from multiple individual variants, which is robust to invalid instruments. It can be used to test for directional pleiotropy and provides an estimate of the causal effect adjusted for its presence. MR-Egger regression provides a useful additional sensitivity analysis to the standard inverse variance weighted (IVW) approach that assumes all variants are valid instruments. Both methods use weights that consider the single nucleotide polymorphism (SNP)-exposure associations to be known, rather than estimated. We call this the `NO Measurement Error' (NOME) assumption. Causal effect estimates from the IVW approach exhibit weak instrument bias whenever the genetic variants utilized violate the NOME assumption, which can be reliably measured using the F-statistic. The effect of NOME violation on MR-Egger regression has yet to be studied. An adaptation of the I2 statistic from the field of meta-analysis is proposed to quantify the strength of NOME violation for MR-Egger. It lies between 0 and 1, and indicates the expected relative bias (or dilution) of the MR-Egger causal estimate in the two-sample MR context. We call it IGX2 . The method of simulation extrapolation is also explored to counteract the dilution. Their joint utility is evaluated using simulated data and applied to a real MR example. In simulated two-sample MR analyses we show that, when a causal effect exists, the MR-Egger estimate of causal effect is biased towards the null when NOME is violated, and the stronger the violation (as indicated by lower values of IGX2 ), the stronger the dilution. When additionally all genetic variants are valid instruments, the type I error rate of the MR-Egger test for pleiotropy is inflated and the causal effect underestimated. Simulation extrapolation is shown to substantially mitigate these adverse effects. We demonstrate our proposed approach for a two-sample summary data MR analysis to estimate the causal effect of low-density lipoprotein on heart disease risk. A high value of IGX2 close to 1 indicates that dilution does not materially affect the standard MR-Egger analyses for these data. : Care must be taken to assess the NOME assumption via the IGX2 statistic before implementing standard MR-Egger regression in the two-sample summary data context. If IGX2 is sufficiently low (less than 90%), inferences from the method should be interpreted with caution and adjustment methods considered. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association.

Effective search for stable segregation configurations at grain boundaries with data-mining techniques

NASA Astrophysics Data System (ADS)

Kiyohara, Shin; Mizoguchi, Teruyasu

2018-03-01

Grain boundary segregation of dopants plays a crucial role in materials properties. To investigate the dopant segregation behavior at the grain boundary, an enormous number of combinations have to be considered in the segregation of multiple dopants at the complex grain boundary structures. Here, two data mining techniques, the random-forests regression and the genetic algorithm, were applied to determine stable segregation sites at grain boundaries efficiently. Using the random-forests method, a predictive model was constructed from 2% of the segregation configurations and it has been shown that this model could determine the stable segregation configurations. Furthermore, the genetic algorithm also successfully determined the most stable segregation configuration with great efficiency. We demonstrate that these approaches are quite effective to investigate the dopant segregation behaviors at grain boundaries.

Genetic evaluation of egg production curve in Thai native chickens by random regression and spline models.

PubMed

Mookprom, S; Boonkum, W; Kunhareang, S; Siripanya, S; Duangjinda, M

2017-02-01

The objective of this research is to investigate appropriate random regression models with various covariance functions, for the genetic evaluation of test-day egg production. Data included 7,884 monthly egg production records from 657 Thai native chickens (Pradu Hang Dam) that were obtained during the first to sixth generation and were born during 2007 to 2014 at the Research and Development Network Center for Animal Breeding (Native Chickens), Khon Kaen University. Average annual and monthly egg productions were 117 ± 41 and 10.20 ± 6.40 eggs, respectively. Nine random regression models were analyzed using the Wilmink function (WM), Koops and Grossman function (KG), Legendre polynomials functions with second, third, and fourth orders (LG2, LG3, LG4), and spline functions with 4, 5, 6, and 8 knots (SP4, SP5, SP6, and SP8). All covariance functions were nested within the same additive genetic and permanent environmental random effects, and the variance components were estimated by Restricted Maximum Likelihood (REML). In model comparisons, mean square error (MSE) and the coefficient of detemination (R 2 ) calculated the goodness of fit; and the correlation between observed and predicted values [Formula: see text] was used to calculate the cross-validated predictive abilities. We found that the covariance functions of SP5, SP6, and SP8 proved appropriate for the genetic evaluation of the egg production curves for Thai native chickens. The estimated heritability of monthly egg production ranged from 0.07 to 0.39, and the highest heritability was found during the first to third months of egg production. In conclusion, the spline functions within monthly egg production can be applied to breeding programs for the improvement of both egg number and persistence of egg production. © 2016 Poultry Science Association Inc.

Predictors of pain relief following spinal cord stimulation in chronic back and leg pain and failed back surgery syndrome: a systematic review and meta-regression analysis.

PubMed

Taylor, Rod S; Desai, Mehul J; Rigoard, Philippe; Taylor, Rebecca J

2014-07-01

We sought to assess the extent to which pain relief in chronic back and leg pain (CBLP) following spinal cord stimulation (SCS) is influenced by patient-related factors, including pain location, and technology factors. A number of electronic databases were searched with citation searching of included papers and recent systematic reviews. All study designs were included. The primary outcome was pain relief following SCS, we also sought pain score (pre- and post-SCS). Multiple predictive factors were examined: location of pain, history of back surgery, initial level of pain, litigation/worker's compensation, age, gender, duration of pain, duration of follow-up, publication year, continent of data collection, study design, quality score, method of SCS lead implant, and type of SCS lead. Between-study association in predictive factors and pain relief were assessed by meta-regression. Seventy-four studies (N = 3,025 patients with CBLP) met the inclusion criteria; 63 reported data to allow inclusion in a quantitative analysis. Evidence of substantial statistical heterogeneity (P < 0.0001) in level of pain relief following SCS was noted. The mean level of pain relief across studies was 58% (95% CI: 53% to 64%, random effects) at an average follow-up of 24 months. Multivariable meta-regression analysis showed no predictive patient or technology factors. SCS was effective in reducing pain irrespective of the location of CBLP. This review supports SCS as an effective pain relieving treatment for CBLP with predominant leg pain with or without a prior history of back surgery. Randomized controlled trials need to confirm the effectiveness and cost-effectiveness of SCS in the CLBP population with predominant low back pain. © 2013 The Authors Pain Practice Published by Wiley Periodicals, Inc. on behalf of World Institute of Pain.

Aspirin as a potential modality for the chemoprevention of breast cancer: A dose-response meta-analysis of cohort studies from 857,831 participants

PubMed Central

Lu, Liming; Shi, Leiyu; Zeng, Jingchun; Wen, Zehuai

2017-01-01

Background Previous meta-analyses on the relationship between aspirin use and breast cancer risk have drawn inconsistent results. In addition, the threshold effect of different doses, frequencies and durations of aspirin use in preventing breast cancer have yet to be established. Results The search yielded 13 prospective cohort studies (N=857,831 participants) that reported an average of 7.6 cases/1,000 person-years of breast cancer during a follow-up period of from 4.4 to 14 years. With a random effects model, a borderline significant inverse association was observed between overall aspirin use and breast cancer risk, with a summarized RR = 0.94 (P = 0.051, 95% CI 0.87-1.01). The linear regression model was a better fit for the dose-response relationship, which displayed a potential relationship between the frequency of aspirin use and breast cancer risk (RR = 0.97, 0.95 and 0.90 for 5, 10 and 20 times/week aspirin use, respectively). It was also a better fit for the duration of aspirin use and breast cancer risk (RR = 0.86, 0.73 and 0.54 for 5, 10 and 20 years of aspirin use). Methods We searched MEDLINE, EMBASE and CENTRAL databases through early October 2016 for relevant prospective cohort studies of aspirin use and breast cancer risk. Meta-analysis of relative risks (RR) estimates associated with aspirin intake were presented by fixed or random effects models. The dose-response meta-analysis was performed by linear trend regression and restricted cubic spline regression. Conclusion Our study confirmed a dose-response relationship between aspirin use and breast cancer risk. For clinical prevention, long term (>5 years) consistent use (2-7 times/week) of aspirin appears to be more effective in achieving a protective effect against breast cancer. PMID:28418881

Aspirin as a potential modality for the chemoprevention of breast cancer: A dose-response meta-analysis of cohort studies from 857,831 participants.

PubMed

Lu, Liming; Shi, Leiyu; Zeng, Jingchun; Wen, Zehuai

2017-06-20

Previous meta-analyses on the relationship between aspirin use and breast cancer risk have drawn inconsistent results. In addition, the threshold effect of different doses, frequencies and durations of aspirin use in preventing breast cancer have yet to be established. The search yielded 13 prospective cohort studies (N=857,831 participants) that reported an average of 7.6 cases/1,000 person-years of breast cancer during a follow-up period of from 4.4 to 14 years. With a random effects model, a borderline significant inverse association was observed between overall aspirin use and breast cancer risk, with a summarized RR = 0.94 (P = 0.051, 95% CI 0.87-1.01). The linear regression model was a better fit for the dose-response relationship, which displayed a potential relationship between the frequency of aspirin use and breast cancer risk (RR = 0.97, 0.95 and 0.90 for 5, 10 and 20 times/week aspirin use, respectively). It was also a better fit for the duration of aspirin use and breast cancer risk (RR = 0.86, 0.73 and 0.54 for 5, 10 and 20 years of aspirin use). We searched MEDLINE, EMBASE and CENTRAL databases through early October 2016 for relevant prospective cohort studies of aspirin use and breast cancer risk. Meta-analysis of relative risks (RR) estimates associated with aspirin intake were presented by fixed or random effects models. The dose-response meta-analysis was performed by linear trend regression and restricted cubic spline regression. Our study confirmed a dose-response relationship between aspirin use and breast cancer risk. For clinical prevention, long term (>5 years) consistent use (2-7 times/week) of aspirin appears to be more effective in achieving a protective effect against breast cancer.

A Randomized-Controlled Trial Examining the Effects of Reflexology on Anxiety of Patients Undergoing Coronary Angiography

PubMed Central

Molavi Vardanjani, Mehdi; Masoudi Alavi, Negin; Razavi, Narges Sadat; Aghajani, Mohammad; Azizi-Fini, Esmail; Vaghefi, Seied Morteza

2013-01-01

Background: The anxiety reduction before coronary angiography has clinical advantages and is one of the objectives of nursing. Reflexology is a non-invasive method that has been used in several clinical situations. Applying reflexology might have effect on the reduction of anxiety before coronary angiography. Objectives: The aim of this randomized clinical trial was to investigate the effect of reflexology on anxiety among patients undergoing coronary angiography. Patients and Methods: This trial was conducted in Shahid Beheshti Hospital, in Kashan, Iran. One hundred male patients who were undergoing coronary angiography were randomly enrolled into intervention and placebo groups. The intervention protocol was included 30 minutes of general foot massage and the stimulation of three reflex points including solar plexus, pituitary gland, and heart. The placebo group only received the general foot massage. Spielbergers state trait anxiety inventory was used to assess the anxiety experienced by patients. Data was analyzed using Man-Witney, Wilcoxon and Chi-square tests. The stepwise multiple regressions used to analyze the variables that are involved in anxiety reduction. Results: The mean range of anxiety decreased from 53.24 to 45.24 in reflexology group which represented 8 score reduction (P = 0.0001). The reduction in anxiety was 5.9 score in placebo group which was also significant (P = 0.0001). The anxiety reduction was significantly higher in reflexology group (P = 0.014). The stepwise multiple regression analysis showed that doing reflexology can explain the 7.5% of anxiety reduction which made a significant model. Conclusions: Reflexology can decrease the anxiety level before coronary angiography. Therefore, reflexology before coronary angiography is recommended. PMID:25414869

Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran

NASA Astrophysics Data System (ADS)

Zeraatpisheh, Mojtaba; Ayoubi, Shamsollah; Jafari, Azam; Finke, Peter

2017-05-01

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the 'noisiness' of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

Effects of a price increase on purchases of sugar sweetened beverages. Results from a randomized controlled trial.

PubMed

Waterlander, Wilma Elzeline; Ni Mhurchu, Cliona; Steenhuis, Ingrid H M

2014-07-01

Sugar sweetened beverage (SSB) taxes are receiving increased political interest. However, there have been no experimental studies of the effects of price increases on SSBs or the effects on close substitutes such as diet drinks, alcohol or sugary snacks. Therefore, the aim of this study was to examine the effects of a price increase on SSBs on beverage and snack purchases using a randomized controlled design within a three-dimensional web-based supermarket. The trial contained two conditions: experimental condition with a 19% tax on SSBs (to reflect an increase in Dutch value added tax from 6% to 19%); and a control condition with regular prices. N = 102 participants were randomized and purchased groceries on a single occasion at a three-dimensional Virtual Supermarket. Data were analysed using independent t-tests and regression analysis. Results showed that participants in the price increase condition purchased significantly less SSBs than the control group (B = -.90; 95% CI = -1.70 to -.10 L per household per week). There were no significant effects on purchases in other beverage or snack food categories. This means that the higher VAT rate was effective in reducing SSB purchases and had no negative side-effects. Copyright © 2014. Published by Elsevier Ltd.

Analysis of threats to research validity introduced by audio recording clinic visits: Selection bias, Hawthorne effect, both, or neither?

PubMed Central

Henry, Stephen G.; Jerant, Anthony; Iosif, Ana-Maria; Feldman, Mitchell D.; Cipri, Camille; Kravitz, Richard L.

2015-01-01

Objective To identify factors associated with participant consent to record visits; to estimate effects of recording on patient-clinician interactions Methods Secondary analysis of data from a randomized trial studying communication about depression; participants were asked for optional consent to audio record study visits. Multiple logistic regression was used to model likelihood of patient and clinician consent. Multivariable regression and propensity score analyses were used to estimate effects of audio recording on 6 dependent variables: discussion of depressive symptoms, preventive health, and depression diagnosis; depression treatment recommendations; visit length; visit difficulty. Results Of 867 visits involving 135 primary care clinicians, 39% were recorded. For clinicians, only working in academic settings (P=0.003) and having worked longer at their current practice (P=0.02) were associated with increased likelihood of consent. For patients, white race (P=0.002) and diabetes (P=0.03) were associated with increased likelihood of consent. Neither multivariable regression nor propensity score analyses revealed any significant effects of recording on the variables examined. Conclusion Few clinician or patient characteristics were significantly associated with consent. Audio recording had no significant effect on any dependent variables. Practice Implications Benefits of recording clinic visits likely outweigh the risks of bias in this setting. PMID:25837372

Lidocaine dose-response effect on postoperative cognitive deficit: meta-analysis and meta-regression.

PubMed

Habibi, Mohammad Reza; Habibi, Valiollah; Habibi, Ali; Soleimani, Aria

2018-04-01

The true influence of the perioperative intravenous lidocaine on the development of postoperative cognitive deficit (POCD) in coronary artery bypass grafting (CABG) remains controversial. The principal aim is to undertake a meta-regression to determine whether moderator variables mediate the relationship between lidocaine and POCD. Areas covered: We searched the Web of Science, PubMed database, Scopus and the Cochrane Library database (up to June 2017) and systematically reviewed a list of retrieved articles. Our final review includes only randomized controlled trials (RCTs) that compared infusion of lidocaine and placebo during cardiopulmonary bypass (CPB). Mantel-Haenszel risk ratio (MH RR) and corresponding 95% confidence interval (CI) was used to report the overall effect and meta-regression analysis. A total of 688 patients in five RCTs were included. POCD occurred in 34% of all cases. Perioperative lidocaine reduces POCD (MH RR 0.702 (95% CI: 0.541-0.909). Younger age, male gender, longer CPB and higher concentration of lidocaine significantly mediate the relationship between lidocaine and POCD in favour of the neuroprotective effect of lidocaine. Expert commentary: The neuroprotective effect of lidocaine on POCD is consistent in spite of longer CPB time. A higher concentration of lidocaine strengthened the neuroprotective effect of lidocaine.

Systematic review of treatment modalities for gingival depigmentation: a random-effects poisson regression analysis.

PubMed

Lin, Yi Hung; Tu, Yu Kang; Lu, Chun Tai; Chung, Wen Chen; Huang, Chiung Fang; Huang, Mao Suan; Lu, Hsein Kun

2014-01-01

Repigmentation variably occurs with different treatment methods in patients with gingival pigmentation. A systemic review was conducted of various treatment modalities for eliminating melanin pigmentation of the gingiva, comprising bur abrasion, scalpel surgery, cryosurgery, electrosurgery, gingival grafts, and laser techniques, to compare the recurrence rates (Rrs) of these treatment procedures. Electronic databases, including PubMed, Web of Science, Google, and Medline were comprehensively searched, and manual searches were conducted for studies published from January 1951 to June 2013. After applying inclusion and exclusion criteria, the final list of articles was reviewed in depth to achieve the objectives of this review. A Poisson regression was used to analyze the outcome of depigmentation using the various treatment methods. The systematic review was based on case reports mainly. In total, 61 eligible publications met the defined criteria. The various therapeutic procedures showed variable clinical results with a wide range of Rrs. A random-effects Poisson regression showed that cryosurgery (Rr = 0.32%), electrosurgery (Rr = 0.74%), and laser depigmentation (Rr = 1.16%) yielded superior result, whereas bur abrasion yielded the highest Rr (8.89%). Within the limit of the sampling level, the present evidence-based results show that cryosurgery exhibits the optimal predictability for depigmentation of the gingiva among all procedures examined, followed by electrosurgery and laser techniques. It is possible to treat melanin pigmentation of the gingiva with various methods and prevent repigmentation. Among those treatment modalities, cryosurgery, electrosurgery, and laser surgery appear to be the best choices for treating gingival pigmentation. © 2014 Wiley Periodicals, Inc.

3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

NASA Astrophysics Data System (ADS)

Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

2015-03-01

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

Genetic evaluation of weekly body weight in Japanese quail using random regression models.

PubMed

Karami, K; Zerehdaran, S; Tahmoorespur, M; Barzanooni, B; Lotfi, E

2017-02-01

1. A total of 11 826 records from 2489 quails, hatched between 2012 and 2013, were used to estimate genetic parameters for BW (body weight) of Japanese quail using random regression models. Weekly BW was measured from hatch until 49 d of age. WOMBAT software (University of New England, Australia) was used for estimating genetic and phenotypic parameters. 2. Nineteen models were evaluated to identify the best orders of Legendre polynomials. A model with Legendre polynomial of order 3 for additive genetic effect, order 3 for permanent environmental effects and order 1 for maternal permanent environmental effects was chosen as the best model. 3. According to the best model, phenotypic and genetic variances were higher at the end of the rearing period. Although direct heritability for BW reduced from 0.18 at hatch to 0.12 at 7 d of age, it gradually increased to 0.42 at 49 d of age. It indicates that BW at older ages is more controlled by genetic components in Japanese quail. 4. Phenotypic and genetic correlations between adjacent periods except hatching weight were more closely correlated than remote periods. The present results suggested that BW at earlier ages, especially at hatch, are different traits compared to BW at older ages. Therefore, BW at earlier ages could not be used as a selection criterion for improving BW at slaughter age.

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

PubMed

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

The degree of color change, rebound effect and sensitivity of bleached teeth associated with at-home and power bleaching techniques: A randomized clinical trial

PubMed Central

Moghadam, Fatemeh Velayati; Majidinia, Sara; Chasteen, Joseph; Ghavamnasiri, Marjaneh

2013-01-01

Aim: The purpose of the present randomized clinical trial was to evaluate the color change, rebound effect and sensitivity of at-home bleaching with 15% carbamide peroxide and power bleaching using 38% hydrogen peroxide. Materials and Methods: For bleaching techniques, 20 subjects were randomized in a split mouth design (at-home and power bleaching): In maxillary and mandibular anterior teeth (n = 20). Color was recorded before bleaching, immediately after bleaching, at 2 weeks, 1, 3 and 6 month intervals. Tooth sensitivity was recorded using the visual analog scale. The Mann-Whitney test was used to compare both groups regarding bleaching effectiveness (ΔE1), rebound effect (ΔE2) and color difference between the rebounded tooth color and unbleached teeth (ΔE3) while the Wilcoxon compared ΔE within each group. Distribution of sensitivity was evaluated using the Chi-square test (α =0.05). Results: There was no significant difference between groups regarding ΔE1 and ΔE3 (P > 0.05). Even though, ΔE2 showed no significant difference between groups after bleaching as well as at 2 week, 1 month and 3 month follow-up periods (P > 0.05). Although, significant difference was found in ΔE2 (P < 0.05 Mann-Whitney) between two methods after 6 months and a high degree of rebound effect was obtained with power bleaching. Within each group, there was no significant difference between ΔE1 and ΔE3 (P < 0.05 Wilcoxon). The distribution of sensitivity was identical with both techniques (P > 0.05). Conclusion: Bleaching techniques resulted in identical tooth whitening and post-operative sensitivity using both techniques, but faster color regression was found with power bleaching even though color regression to the baseline of the teeth in both groups was the same after 6 months. PMID:24932113

An overview of longitudinal data analysis methods for neurological research.

PubMed

Locascio, Joseph J; Atri, Alireza

2011-01-01

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models.

Assessing the effectiveness and cost-effectiveness of audit and feedback on physician’s prescribing indicators: study protocol of a randomized controlled trial with economic evaluation

PubMed Central

2012-01-01

Background Physician prescribing is the most frequent medical intervention with a highest impact on healthcare costs and outcomes. Therefore improving and promoting rational drug use is a great interest. We aimed to assess the effectiveness and cost-effectiveness of two forms of conducting prescribing audit and feedback interventions and a printed educational material intervention in improving physician prescribing. Method/design A four-arm randomized trial with economic evaluation will be conducted in Tehran. Three interventions (routine feedback, revised feedback, and printed educational material) and a no intervention control arm will be compared. Physicians working in outpatient practices are randomly allocated to one of the four arms using stratified randomized sampling. The interventions are developed based on a review of literature, physician interviews, current experiences in Iran and with theoretical insights from the Theory of Planned Behavior. Effects of the interventions on improving antibiotics and corticosteroids prescribing will be assessed in regression analyses. Cost data will be assessed from a health care provider’s perspective and incremental cost-effectiveness ratios will be calculated. Discussion This study will determine the effectiveness and cost-effectiveness of three interventions and allow us to determine the most effective interventions in improving prescribing pattern. If the interventions are cost-effective, they will likely be applied nationwide. Trial registration Iranian Registry of Clinical Trials Registration Number: IRCT201106086740N1Pharmaceutical Sciences Research Center of TUMS Ethics Committee Registration Number: 90-02-27-07 PMID:23351564

Evaluation of a reduced nicotine product standard: moderating effects of and impact on cannabis use*

PubMed Central

Pacek, Lauren R.; Vandrey, Ryan; Dermody, Sarah S.; Denlinger, Rachel L.; Lemieux, Andrine; Tidey, Jennifer W.; McClernon, F. Joseph; Bangdiwala, Ananta S.; Drobes, David J.; al'Absi, Mustafa; Strasser, Andrew A.; Koopmeiners, Joseph S.; Hatsukami, Dorothy K.; Donny, Eric C.

2016-01-01

Introduction The Family Smoking Prevention and Tobacco Control Act authorized the FDA to reduce the nicotine content in cigarettes. Research is needed to guide proposed regulations, including evaluation of consequences to public health. This study evaluated how a reduced nicotine product standard might be moderated by and impact cannabis use. Methods Secondary analysis of a controlled clinical trial examining the effects of nicotine content in cigarettes in adult daily smokers. Linear regression assessed whether baseline cannabis use moderated behavioral, subjective, or physiological effects of smoking very low nicotine content (VLNC) versus normal nicotine content (NNC) cigarettes. Repeated measures analysis of associations between nicotine condition and prevalence and frequency of cannabis use was completed using generalized estimating equations (GEE). Results Among cannabis users and non-users, smokers randomized to VLNC cigarettes reported lower nicotine dependence, cigarettes per day, biomarkers of nicotine exposure, and craving compared to smokers randomized to NNC cigarettes. Non-cannabis using smokers randomized to VLNC cigarettes also reported lower smoking dependence motives and had lower tobacco-specific nitrosamine exposure and total puff volume versus smokers randomized to NNC cigarettes. For cannabis users, smokers randomized to VLNC cigarettes reported decreased positive affect. Cannabis use did not moderate most effects of VLNC cigarettes. VLNC cigarette use did not impact the prevalence or frequency of cannabis use. Discussion Findings provide evidence that nicotine reduction in cigarettes could have beneficial effects on cigarette smoking regardless of cannabis use. Results suggest that transitioning to VLNC cigarettes is unlikely to alter current rates of cannabis use. PMID:27590743

Evaluation of a reduced nicotine product standard: Moderating effects of and impact on cannabis use.

PubMed

Pacek, Lauren R; Vandrey, Ryan; Dermody, Sarah S; Denlinger-Apte, Rachel L; Lemieux, Andrine; Tidey, Jennifer W; McClernon, F Joseph; Bangdiwala, Ananta S; Drobes, David J; al'Absi, Mustafa; Strasser, Andrew A; Koopmeiners, Joseph S; Hatsukami, Dorothy K; Donny, Eric C

2016-10-01

The Family Smoking Prevention and Tobacco Control Act authorized the FDA to reduce the nicotine content in cigarettes. Research is needed to guide proposed regulations, including evaluation of consequences to public health. This study evaluated how a reduced nicotine product standard might be moderated by and impact cannabis use. Secondary analysis of a controlled clinical trial examining the effects of nicotine content in cigarettes in adult daily smokers. Linear regression assessed whether baseline cannabis use moderated behavioral, subjective, or physiological effects of smoking very low nicotine content (VLNC) versus normal nicotine content (NNC) cigarettes. Repeated measures analysis of associations between nicotine condition and prevalence and frequency of cannabis use was completed using generalized estimating equations (GEE). Cannabis use did not moderate most of the following effects of VLNC cigarettes: Among cannabis users and non-users, smokers randomized to VLNC cigarettes reported lower nicotine dependence, cigarettes per day, biomarkers of nicotine exposure, and craving compared to smokers randomized to NNC cigarettes. Non-cannabis using smokers randomized to VLNC cigarettes also reported lower smoking dependence motives and had lower tobacco-specific nitrosamine exposure and total puff volume versus smokers randomized to NNC cigarettes. For cannabis users, smokers randomized to VLNC cigarettes reported decreased positive affect. VLNC cigarette use did not impact the prevalence or frequency of cannabis use. Findings provide evidence that nicotine reduction in cigarettes could have beneficial effects on cigarette smoking regardless of cannabis use. Results suggest that transitioning to VLNC cigarettes is unlikely to alter current rates of cannabis use. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The effect of a health promotion intervention for construction workers on work-related outcomes: results from a randomized controlled trial.

PubMed

Viester, Laura; Verhagen, Evert A L M; Bongers, Paulien M; van der Beek, Allard J

2015-08-01

The objective of the present study is to investigate the effects of a worksite health promotion intervention on musculoskeletal symptoms, physical functioning, work ability, work-related vitality, work performance, and sickness absence. In a randomized controlled design, 314 construction workers were randomized into an intervention group (n = 162) receiving personal coaching, tailored information, and materials, and a control group (n = 152) receiving usual care. Sickness absence was recorded continuously in company records, and questionnaires were completed before, directly after the 6-month intervention period, and 12 months after baseline measurements. Linear and logistic regression analyses were performed to determine intervention effects. No significant changes at 6 or 12 months of follow-up were observed in musculoskeletal symptoms, physical functioning, work ability, work-related vitality, work performance, and sickness absence as a result of the intervention. This study shows that the intervention was not statistically significantly effective on secondary outcomes. Although the intervention improved physical activity, dietary, and weight-related outcomes, it was not successful in decreasing musculoskeletal symptoms and improving other work-related measures. Presumably, more multifaceted interventions are required to establish significant change in these outcomes.

Meta-regression analyses, meta-analyses, and trial sequential analyses of the effects of supplementation with beta-carotene, vitamin A, and vitamin E singly or in different combinations on all-cause mortality: do we have evidence for lack of harm?

PubMed

Bjelakovic, Goran; Nikolova, Dimitrinka; Gluud, Christian

2013-01-01

Evidence shows that antioxidant supplements may increase mortality. Our aims were to assess whether different doses of beta-carotene, vitamin A, and vitamin E affect mortality in primary and secondary prevention randomized clinical trials with low risk of bias. The present study is based on our 2012 Cochrane systematic review analyzing beneficial and harmful effects of antioxidant supplements in adults. Using random-effects meta-analyses, meta-regression analyses, and trial sequential analyses, we examined the association between beta-carotene, vitamin A, and vitamin E, and mortality according to their daily doses and doses below and above the recommended daily allowances (RDA). We included 53 randomized trials with low risk of bias (241,883 participants, aged 18 to 103 years, 44.6% women) assessing beta-carotene, vitamin A, and vitamin E. Meta-regression analysis showed that the dose of vitamin A was significantly positively associated with all-cause mortality. Beta-carotene in a dose above 9.6 mg significantly increased mortality (relative risk (RR) 1.06, 95% confidence interval (CI) 1.02 to 1.09, I(2) = 13%). Vitamin A in a dose above the RDA (> 800 µg) did not significantly influence mortality (RR 1.08, 95% CI 0.98 to 1.19, I(2) = 53%). Vitamin E in a dose above the RDA (> 15 mg) significantly increased mortality (RR 1.03, 95% CI 1.00 to 1.05, I(2) = 0%). Doses below the RDAs did not affect mortality, but data were sparse. Beta-carotene and vitamin E in doses higher than the RDA seem to significantly increase mortality, whereas we lack information on vitamin A. Dose of vitamin A was significantly associated with increased mortality in meta-regression. We lack information on doses below the RDA. All essential compounds to stay healthy cannot be synthesized in our body. Therefore, these compounds must be taken through our diet or obtained in other ways [1]. Oxidative stress has been suggested to cause a variety of diseases [2]. Therefore, it is speculated that antioxidant supplements could have a potential role in preventing diseases and death. Despite the fact that a normal diet in high-income countries may provide sufficient amounts of antioxidants [3,4], more than one third of adults regularly take antioxidant supplements [5,6].

Effectiveness of Mindfulness Intervention on Psychological Behaviors Among Adolescents With Parental HIV Infection: A Group-Randomized Controlled Trial.

PubMed

Mon, Myo-Myo; Liabsuetrakul, Tippawan; Htut, Kyaw-Min

2016-11-01

This study aims to identify the effectiveness of mindfulness intervention on the psychological behaviors of adolescents with parental HIV infection and its associated factors in Myanmar. A total of 80 adolescents from 2 intervention townships and 80 adolescents from 2 control townships were enrolled in a group randomized controlled trial with assessments at baseline and 6 months follow-up. The mindfulness intervention involved monthly group sessions for 3 consecutive months led by an experienced mindfulness trainer. Three domains of psychological behaviors-namely, emotional, conduct, and social behaviors-were assessed at baseline and compared after 6 months. Multilevel regression analysis was used to determine the effectiveness of the intervention and associated factors for psychological behaviors. The intervention significantly improved emotional and conduct behaviors at 6 months (P < .001) but had no effect on social behavior. The significant effect of the intervention existed after adjusting for gender, family type, child age, and orphan status. © 2016 APJPH.

A meta-regression evaluating the effectiveness and prognostic factors of oral phosphodiesterase type 5 inhibitors for the treatment of erectile dysfunction

PubMed Central

Yuan, Jin-Qiu; Mao, Chen; Yang, Zu-Yao; Fu, Xiao-Hong; Wong, Samuel Y; Tang, Jin-Ling

2016-01-01

The effectiveness of phosphodiesterase type 5 inhibitors (PDE5-Is) for erectile dysfunction (ED) varies considerably among trials, but available studies investigating the factors that affect the effectiveness are few and findings are not consistent. A systematic search was performed in PubMed, Cochrane Library, and EMBASE to identify randomized controlled trials comparing PDE5-Is with placebo for the treatment of ED. The methodological quality of included studies was assessed by the Cochrane Collaboration's tool for assessing risk of bias. The associations between prespecified study-level factors and effectiveness were tested by a random effects meta-regression model. This study included 93 trials with 26 139 patients. When all PDE5-Is were grouped together, Caucasian ethnicity was associated with 15.636% (95% confidence interval [CI]: 0.858% to 32.579%) increase in risk ratio (RR) for Global Assessment Questionnaire question-1 (GAQ-1), and 1.473 (95% CI: 0.406 to 2.338) score increase in mean difference (MD) for posttreatment International Index of Erectile Function-Erectile Function domain score (IIEF-EF), compared to Asian ethnicity. A one-score increase in baseline IIEF-EF was associated with −5.635% (95% CI: −9.120% to −2.017%) reduction in RR for GAQ-1, and −0.229 (95% CI: −0.425 to −0.042) score decrease in MD for posttreatment IIEF-EF. In conclusion, PDE5-Is are more effective in Caucasians than Asians, and in patients with more severe ED. PMID:25966626

Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing Large White pigs.

PubMed

Huynh-Tran, V H; Gilbert, H; David, I

2017-11-01

The objective of the present study was to compare a random regression model, usually used in genetic analyses of longitudinal data, with the structured antedependence (SAD) model to study the longitudinal feed conversion ratio (FCR) in growing Large White pigs and to propose criteria for animal selection when used for genetic evaluation. The study was based on data from 11,790 weekly FCR measures collected on 1,186 Large White male growing pigs. Random regression (RR) using orthogonal polynomial Legendre and SAD models was used to estimate genetic parameters and predict FCR-based EBV for each of the 10 wk of the test. The results demonstrated that the best SAD model (1 order of antedependence of degree 2 and a polynomial of degree 2 for the innovation variance for the genetic and permanent environmental effects, i.e., 12 parameters) provided a better fit for the data than RR with a quadratic function for the genetic and permanent environmental effects (13 parameters), with Bayesian information criteria values of -10,060 and -9,838, respectively. Heritabilities with the SAD model were higher than those of RR over the first 7 wk of the test. Genetic correlations between weeks were higher than 0.68 for short intervals between weeks and decreased to 0.08 for the SAD model and -0.39 for RR for the longest intervals. These differences in genetic parameters showed that, contrary to the RR approach, the SAD model does not suffer from border effect problems and can handle genetic correlations that tend to 0. Summarized breeding values were proposed for each approach as linear combinations of the individual weekly EBV weighted by the coefficients of the first or second eigenvector computed from the genetic covariance matrix of the additive genetic effects. These summarized breeding values isolated EBV trajectories over time, capturing either the average general value or the slope of the trajectory. Finally, applying the SAD model over a reduced period of time suggested that similar selection choices would result from the use of the records from the first 8 wk of the test. To conclude, the SAD model performed well for the genetic evaluation of longitudinal phenotypes.

Analysis of a Split-Plot Experimental Design Applied to a Low-Speed Wind Tunnel Investigation

NASA Technical Reports Server (NTRS)

Erickson, Gary E.

2013-01-01

A procedure to analyze a split-plot experimental design featuring two input factors, two levels of randomization, and two error structures in a low-speed wind tunnel investigation of a small-scale model of a fighter airplane configuration is described in this report. Standard commercially-available statistical software was used to analyze the test results obtained in a randomization-restricted environment often encountered in wind tunnel testing. The input factors were differential horizontal stabilizer incidence and the angle of attack. The response variables were the aerodynamic coefficients of lift, drag, and pitching moment. Using split-plot terminology, the whole plot, or difficult-to-change, factor was the differential horizontal stabilizer incidence, and the subplot, or easy-to-change, factor was the angle of attack. The whole plot and subplot factors were both tested at three levels. Degrees of freedom for the whole plot error were provided by replication in the form of three blocks, or replicates, which were intended to simulate three consecutive days of wind tunnel facility operation. The analysis was conducted in three stages, which yielded the estimated mean squares, multiple regression function coefficients, and corresponding tests of significance for all individual terms at the whole plot and subplot levels for the three aerodynamic response variables. The estimated regression functions included main effects and two-factor interaction for the lift coefficient, main effects, two-factor interaction, and quadratic effects for the drag coefficient, and only main effects for the pitching moment coefficient.

Promoting positive condom use experiences among young black MSM: a randomized controlled trial of a brief, clinic-based intervention.

PubMed

Crosby, Richard A; Mena, Leandro; Smith, Rachel Vickers

2018-06-01

The aim of this study is to determine, among young Black men who have sex with men (YBMSM), the 12-month efficacy of a single-session, clinic-based intervention promoting condom use to enhance sexual pleasure (purpose 1) and the use of condoms from the start-to-finish of anal sex (purpose 2). A pre-test, post-test randomized controlled trial was conducted, using a 12-month period of follow-up observation, in STI clinics. Data from 394 YBMSM completing baseline and 12-month follow-up assessments were analyzed. The experimental condition comprised a one-to-one, interactive program (Focus on the Future) designed for tailored delivery. Regarding study purpose 1, in an age-adjusted linear regression model for 277 HIV-uninfected men, there was a significant effect of the intervention (Beta=0.13, P =0.036) relative to more favorable sexual experiences when using condoms. Regarding study purpose 2, in an adjusted logistic regression model, for HIV-uninfected men, there was a significant effect of the intervention (AOR=0.54, P =0.048) relative to using condoms from start-to-finish of anal sex. Significant effects for HIV-infected men were not observed. A small, but non-significant, effect was observed relative to men's self-report of always using condoms. This single-session program may be a valuable counseling tool for use in conjunction with pre-exposure prophylaxis-related care for HIV-uninfected YBMSM.

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

This report presents the results of a fourth year effort of a research program, conducted for NASA-LeRC by the University of Texas at San Antonio (UTSA). The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subject to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation has been randomized and is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 has been analyzed using the developed methodology.

Computational simulation of probabilistic lifetime strength for aerospace materials subjected to high temperature, mechanical fatigue, creep, and thermal fatigue

NASA Technical Reports Server (NTRS)

Boyce, Lola; Bast, Callie C.; Trimble, Greg A.

1992-01-01

The results of a fourth year effort of a research program conducted for NASA-LeRC by The University of Texas at San Antonio (UTSA) are presented. The research included on-going development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic material strength degradation model, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects or primitive variables. These primitive variables may include high temperature, fatigue, or creep. In most cases, strength is reduced as a result of the action of a variable. This multifactor interaction strength degradation equation was randomized and is included in the computer program, PROMISC. Also included in the research is the development of methodology to calibrate the above-described constitutive equation using actual experimental materials data together with regression analysis of that data, thereby predicting values for the empirical material constants for each effect or primitive variable. This regression methodology is included in the computer program, PROMISC. Actual experimental materials data were obtained from industry and the open literature for materials typically for applications in aerospace propulsion system components. Material data for Inconel 718 was analyzed using the developed methodology.

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Publication bias in obesity treatment trials?

PubMed

Allison, D B; Faith, M S; Gorman, B S

1996-10-01

The present investigation examined the extent of publication bias (namely the tendency to publish significant findings and file away non-significant findings) within the obesity treatment literature. Quantitative literature synthesis of four published meta-analyses from the obesity treatment literature. Interventions in these studies included pharmacological, educational, child, and couples treatments. To assess publication bias, several regression procedures (for example weighted least-squares, random-effects multi-level modeling, and robust regression methods) were used to regress effect sizes onto their standard errors, or proxies thereof, within each of the four meta-analysis. A significant positive beta weight in these analyses signified publication bias. There was evidence for publication bias within two of the four published meta-analyses, such that reviews of published studies were likely to overestimate clinical efficacy. The lack of evidence for publication bias within the two other meta-analyses might have been due to insufficient statistical power rather than the absence of selection bias. As in other disciplines, publication bias appears to exist in the obesity treatment literature. Suggestions are offered for managing publication bias once identified or reducing its likelihood in the first place.

Measuring the impact of energy consumption and air quality indicators on climate change: evidence from the panel of UNFCC classified countries.

PubMed

Ozturk, Ilhan

2015-10-01

This study examines the relationship between energy consumption, air pollution, and climate change in the panel of six economically diversified countries classified by the United Nations Framework Convention on Climate Change (UNFCC) as industrialized countries and economies in transition nations by using the panel econometric techniques for the period of 1990-2012. The results of pooled least square regression show that both the energy consumption and air quality indicators have a positive and significant relationship with the climate change, i.e., 1 % increase in energy consumption increases greenhouse gas emissions by 0.124 %, carbon dioxide emissions increase by 0.652 %, methane emissions increase by 0.123 %, and nitrous oxide emissions increase greenhouse gas emissions by 0.105 % age points. The results of fixed-effect regression and random-effect regression confirmed the deteriorating impact of air quality indicators on climate change; however, the results failed to show any significant association between energy consumption and climate change when absorbing country-specific shocks and time-variant shocks during the study time period.

Covariate Selection for Multilevel Models with Missing Data

PubMed Central

Marino, Miguel; Buxton, Orfeu M.; Li, Yi

2017-01-01

Missing covariate data hampers variable selection in multilevel regression settings. Current variable selection techniques for multiply-imputed data commonly address missingness in the predictors through list-wise deletion and stepwise-selection methods which are problematic. Moreover, most variable selection methods are developed for independent linear regression models and do not accommodate multilevel mixed effects regression models with incomplete covariate data. We develop a novel methodology that is able to perform covariate selection across multiply-imputed data for multilevel random effects models when missing data is present. Specifically, we propose to stack the multiply-imputed data sets from a multiple imputation procedure and to apply a group variable selection procedure through group lasso regularization to assess the overall impact of each predictor on the outcome across the imputed data sets. Simulations confirm the advantageous performance of the proposed method compared with the competing methods. We applied the method to reanalyze the Healthy Directions-Small Business cancer prevention study, which evaluated a behavioral intervention program targeting multiple risk-related behaviors in a working-class, multi-ethnic population. PMID:28239457

Weighted SGD for ℓ p Regression with Randomized Preconditioning.

PubMed

Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W

2016-01-01

In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems-e.g., ℓ 2 and ℓ 1 regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system.By rewriting a deterministic ℓ p regression problem as a stochastic optimization problem, we connect pwSGD to several existing ℓ p solvers including RLA methods with algorithmic leveraging (RLA for short).We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Such SGD convergence rates are superior to other related SGD algorithm such as the weighted randomized Kaczmarz algorithm.Particularly, when solving ℓ 1 regression with size n by d , pwSGD returns an approximate solution with ε relative error in the objective value in (log n ·nnz( A )+poly( d )/ ε 2 ) time. This complexity is uniformly better than that of RLA methods in terms of both ε and d when the problem is unconstrained. In the presence of constraints, pwSGD only has to solve a sequence of much simpler and smaller optimization problem over the same constraints. In general this is more efficient than solving the constrained subproblem required in RLA.For ℓ 2 regression, pwSGD returns an approximate solution with ε relative error in the objective value and the solution vector measured in prediction norm in (log n ·nnz( A )+poly( d ) log(1/ ε )/ ε ) time. We show that for unconstrained ℓ 2 regression, this complexity is comparable to that of RLA and is asymptotically better over several state-of-the-art solvers in the regime where the desired accuracy ε , high dimension n and low dimension d satisfy d ≥ 1/ ε and n ≥ d 2 / ε . We also provide lower bounds on the coreset complexity for more general regression problems, indicating that still new ideas will be needed to extend similar RLA preconditioning ideas to weighted SGD algorithms for more general regression problems. Finally, the effectiveness of such algorithms is illustrated numerically on both synthetic and real datasets, and the results are consistent with our theoretical findings and demonstrate that pwSGD converges to a medium-precision solution, e.g., ε = 10 -3 , more quickly.

Weighted SGD for ℓp Regression with Randomized Preconditioning*

PubMed Central

Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W.

2018-01-01

In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems—e.g., ℓ2 and ℓ1 regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system.By rewriting a deterministic ℓp regression problem as a stochastic optimization problem, we connect pwSGD to several existing ℓp solvers including RLA methods with algorithmic leveraging (RLA for short).We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Such SGD convergence rates are superior to other related SGD algorithm such as the weighted randomized Kaczmarz algorithm.Particularly, when solving ℓ1 regression with size n by d, pwSGD returns an approximate solution with ε relative error in the objective value in 𝒪(log n·nnz(A)+poly(d)/ε2) time. This complexity is uniformly better than that of RLA methods in terms of both ε and d when the problem is unconstrained. In the presence of constraints, pwSGD only has to solve a sequence of much simpler and smaller optimization problem over the same constraints. In general this is more efficient than solving the constrained subproblem required in RLA.For ℓ2 regression, pwSGD returns an approximate solution with ε relative error in the objective value and the solution vector measured in prediction norm in 𝒪(log n·nnz(A)+poly(d) log(1/ε)/ε) time. We show that for unconstrained ℓ2 regression, this complexity is comparable to that of RLA and is asymptotically better over several state-of-the-art solvers in the regime where the desired accuracy ε, high dimension n and low dimension d satisfy d ≥ 1/ε and n ≥ d2/ε. We also provide lower bounds on the coreset complexity for more general regression problems, indicating that still new ideas will be needed to extend similar RLA preconditioning ideas to weighted SGD algorithms for more general regression problems. Finally, the effectiveness of such algorithms is illustrated numerically on both synthetic and real datasets, and the results are consistent with our theoretical findings and demonstrate that pwSGD converges to a medium-precision solution, e.g., ε = 10−3, more quickly. PMID:29782626

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.

PubMed

Kim, Dongchul; Kang, Mingon; Biswas, Ashis; Liu, Chunyu; Gao, Jean

2016-08-10

Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator. We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above. In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

Comparing spatial regression to random forests for large ...

EPA Pesticide Factsheets

Environmental data may be “large” due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates, whereas spatial regression, when using reduced rank methods, has a reputation for good predictive performance when using many records. In this study, we compare these two techniques using a data set containing the macroinvertebrate multimetric index (MMI) at 1859 stream sites with over 200 landscape covariates. Our primary goal is predicting MMI at over 1.1 million perennial stream reaches across the USA. For spatial regression modeling, we develop two new methods to accommodate large data: (1) a procedure that estimates optimal Box-Cox transformations to linearize covariate relationships; and (2) a computationally efficient covariate selection routine that takes into account spatial autocorrelation. We show that our new methods lead to cross-validated performance similar to random forests, but that there is an advantage for spatial regression when quantifying the uncertainty of the predictions. Simulations are used to clarify advantages for each method. This research investigates different approaches for modeling and mapping national stream condition. We use MMI data from the EPA's National Rivers and Streams Assessment and predictors from StreamCat (Hill et al., 2015). Previous studies have focused on modeling the MMI condition classes (i.e., good, fair, and po

Blood Glucose Reduction by Diabetic Drugs with Minimal Hypoglycemia Risk for Cardiovascular Outcomes: Evidence from Meta-regression Analysis of Randomized Controlled Trials.

PubMed

Huang, Chi-Jung; Wang, Wei-Ting; Sung, Shih-Hsien; Chen, Chen-Huan; Lip, Gregory Yh; Cheng, Hao-Min; Chiang, Chern-En

2018-05-02

To investigate the effects of blood glucose control with antihyperglycemic agents with minimal hypoglycemia risk on cardiovascular outcomes in patients with type 2 diabetes (T2D). Randomized controlled trials (RCTs) comparing the relative efficacy and safety of antidiabetic drugs with less hypoglycemia risk were comprehensively searched in MEDLINE, Embase, and the Cochrane Library up to January 27, 2018. Mixed-effects meta-regression analysis was conducted to explore the relationship between haemoglobin A1c (HbA1c) reduction and the risk of major adverse cardiovascular events (MACE), myocardial infarction, stroke, cardiovascular death, all-cause death, and hospitalization for heart failure. Ten RCTs comprising 92400 participants with T2D were included and provided information on 9773 MACE during a median follow-up of 2.6 years. The mean HbA1c concentration was 0.42% lower (median, 0.27-0.86%) for participants given antihyperglycemic agents than those given placebo. The meta-regression analysis demonstrated that HbA1c reduction was significantly associated with a decreased risk of MACE (β value, -0.39 to -0.55; P<0.02) even after adjusting for each of the following possible confounding factors including age, sex, baseline HbA1c, duration of follow-up, difference in achieved systolic blood pressure, difference in achieved body weight, or risk difference in hypoglycemia. Lowering HbA1c by 1% conferred a significant risk reduction of 30% (95% CI, 17-40%) for MACE. By contrast, the meta-regression analysis for trials using conventional agents failed to demonstrate a significant relationship between achieved HbA1c difference and MACE risk (P>0.74). Compared with placebo, newer T2D agents with less hypoglycemic hazard significantly reduced the risk of MACE. The MACE reduction seems to be associated with HbA1c reduction in a linear relationship. This article is protected by copyright. All rights reserved.

A Randomized Controlled Study of the Virginia Student Threat Assessment Guidelines in Kindergarten through Grade 12

ERIC Educational Resources Information Center

Cornell, Dewey G.; Allen, Korrie; Fan, Xitao

2012-01-01

This randomized controlled study examined disciplinary outcomes for 201 students who made threats of violence at school. The students attended 40 schools randomly assigned to use the Virginia Student Threat Assessment Guidelines or follow a business-as-usual disciplinary approach in a control group. Logistic regression analyses found, after…

Probabilistic Fiber Composite Micromechanics

NASA Technical Reports Server (NTRS)

Stock, Thomas A.

1996-01-01

Probabilistic composite micromechanics methods are developed that simulate expected uncertainties in unidirectional fiber composite properties. These methods are in the form of computational procedures using Monte Carlo simulation. The variables in which uncertainties are accounted for include constituent and void volume ratios, constituent elastic properties and strengths, and fiber misalignment. A graphite/epoxy unidirectional composite (ply) is studied to demonstrate fiber composite material property variations induced by random changes expected at the material micro level. Regression results are presented to show the relative correlation between predictor and response variables in the study. These computational procedures make possible a formal description of anticipated random processes at the intra-ply level, and the related effects of these on composite properties.

Examination of the Effect of Implantable Cardioverter-Defibrillators on Health-Related Quality of Life

PubMed Central

Noyes, Katia; Corona, Ethan; Veazie, Peter; Dick, Andrew W.; Zhao, Hongwei; Moss, Arthur J.

2015-01-01

Background While implantable cardioverter-defibrillators (ICDs) improve survival, their benefit in terms of health-related quality of life (HRQOL) is negligible. Objective To examine how shocks and congestive heart failure (CHF) mediate the effect of ICDs on HRQOL. Methods The US patients from the MADIT-II (Multicenter Automatic Defibrillator Trial-II) trial (n = 983) were randomized to receive an ICD or medical treatment only. HRQOL was assessed using the Health Utility Index 3 at baseline and 3, 12, 24, and 36 months following randomization. Logistic regressions were used to test for the effect of ICDs on the CHF indicator, and linear regressions were used to examine the effect of ICD shocks and CHF on HRQOL in living patients. We used a Monte Carlo simulation and a parametric Weibull distribution survival model to test for the effect of selective attrition. Observations were clustered by patients and robust standard errors (RSEs) were used to control for the non-independence of multiple observations provided by the same patient. Results Patients in the ICD arm had 41% higher odds of experiencing CHF since their last assessment compared with those in the control arm (RSE = 0.19, p = 0.01). Developing CHF reduced HRQOL at the subsequent visit by 0.07 (p < 0.01). Having ICD shocks reduced overall HRQOL by 0.04 (p = 0.04) at the subsequent assessment. The negative effect of ICD firing on HRQOL was an order of magnitude greater than the effect of CHF. Conclusions A higher prevalence of CHF and shocks among patients with ICDs and their negative effect on HRQOL may partially explain the lack of HRQOL benefit of ICD therapy. PMID:19929037

Nonparametric estimation and testing of fixed effects panel data models

PubMed Central

Henderson, Daniel J.; Carroll, Raymond J.; Li, Qi

2009-01-01

In this paper we consider the problem of estimating nonparametric panel data models with fixed effects. We introduce an iterative nonparametric kernel estimator. We also extend the estimation method to the case of a semiparametric partially linear fixed effects model. To determine whether a parametric, semiparametric or nonparametric model is appropriate, we propose test statistics to test between the three alternatives in practice. We further propose a test statistic for testing the null hypothesis of random effects against fixed effects in a nonparametric panel data regression model. Simulations are used to examine the finite sample performance of the proposed estimators and the test statistics. PMID:19444335

Effectiveness of a virtual intervention for primary healthcare professionals aimed at improving attitudes towards the empowerment of patients with chronic diseases: study protocol for a cluster randomized controlled trial (e-MPODERA project).

PubMed

González-González, Ana Isabel; Orrego, Carola; Perestelo-Perez, Lilisbeth; Bermejo-Caja, Carlos Jesús; Mora, Nuria; Koatz, Débora; Ballester, Marta; Del Pino, Tasmania; Pérez-Ramos, Jeannet; Toledo-Chavarri, Ana; Robles, Noemí; Pérez-Rivas, Francisco Javier; Ramírez-Puerta, Ana Belén; Canellas-Criado, Yolanda; Del Rey-Granado, Yolanda; Muñoz-Balsa, Marcos José; Becerril-Rojas, Beatriz; Rodríguez-Morales, David; Sánchez-Perruca, Luis; Vázquez, José Ramón; Aguirre, Armando

2017-10-30

Communities of practice are based on the idea that learning involves a group of people exchanging experiences and knowledge. The e-MPODERA project aims to assess the effectiveness of a virtual community of practice aimed at improving primary healthcare professional attitudes to the empowerment of patients with chronic diseases. This paper describes the protocol for a cluster randomized controlled trial. We will randomly assign 18 primary-care practices per participating region of Spain (Catalonia, Madrid and Canary Islands) to a virtual community of practice or to usual training. The primary-care practice will be the randomization unit and the primary healthcare professional will be the unit of analysis. We will need a sample of 270 primary healthcare professionals (general practitioners and nurses) and 1382 patients. We will perform randomization after professionals and patients are selected. We will ask the intervention group to participate for 12 months in a virtual community of practice based on a web 2.0 platform. We will measure the primary outcome using the Patient-Provider Orientation Scale questionnaire administered at baseline and after 12 months. Secondary outcomes will be the sociodemographic characteristics of health professionals, sociodemographic and clinical characteristics of patients, the Patient Activation Measure questionnaire for patient activation and outcomes regarding use of the virtual community of practice. We will calculate a linear mixed-effects regression to estimate the effect of participating in the virtual community of practice. This cluster randomized controlled trial will show whether a virtual intervention for primary healthcare professionals improves attitudes to the empowerment of patients with chronic diseases. ClicalTrials.gov, NCT02757781 . Registered on 25 April 2016. Protocol Version. PI15.01 22 January 2016.

Drug treatment rates with beta-blockers and ACE-inhibitors/angiotensin receptor blockers and recurrences in takotsubo cardiomyopathy: A meta-regression analysis.

PubMed

Brunetti, Natale Daniele; Santoro, Francesco; De Gennaro, Luisa; Correale, Michele; Gaglione, Antonio; Di Biase, Matteo

2016-07-01

In a recent paper Singh et al. analyzed the effect of drug treatment on recurrence of takotsubo cardiomyopathy (TTC) in a comprehensive meta-analysis. The study found that recurrence rates were independent of clinic utilization of BB prescription, but inversely correlated with ACEi/ARB prescription: authors therefore conclude that ACEi/ARB rather than BB may reduce risk of recurrence. We aimed to re-analyze data reported in the study, now weighted for populations' size, in a meta-regression analysis. After multiple meta-regression analysis, we found a significant regression between rates of prescription of ACEi and rates of recurrence of TTC; regression was not statistically significant for BBs. On the bases of our re-analysis, we confirm that rates of recurrence of TTC are lower in populations of patients with higher rates of treatment with ACEi/ARB. That could not necessarily imply that ACEi may prevent recurrence of TTC, but barely that, for example, rates of recurrence are lower in cohorts more compliant with therapy or more prescribed with ACEi because more carefully followed. Randomized prospective studies are surely warranted. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Assessing variation in life-history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists.

PubMed

Hamel, Sandra; Yoccoz, Nigel G; Gaillard, Jean-Michel

2017-05-01

Mixed models are now well-established methods in ecology and evolution because they allow accounting for and quantifying within- and between-individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi-modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life-history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life-history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long-term studies of large mammals to illustrate the potential of using mixture models for assessing within-population variation in life-history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life-history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users. © 2016 Cambridge Philosophical Society.

Curcumin downregulates human tumor necrosis factor-α levels: A systematic review and meta-analysis ofrandomized controlled trials.

PubMed

Sahebkar, Amirhossein; Cicero, Arrigo F G; Simental-Mendía, Luis E; Aggarwal, Bharat B; Gupta, Subash C

2016-05-01

Tumor necrosis factor-α (TNF-α) is a key inflammatory mediator and its reduction is a therapeutic target in several inflammatory diseases. Curcumin, a bioactive polyphenol from turmeric, has been shown in several preclinical studies to block TNF-α effectively. However, clinical evidence has not been fully conclusive. The aim of the present meta-analysis was to evaluate the efficacy of curcumin supplementation on circulating levels of TNF-α in randomized controlled trials (RCTs). The search included PubMed-Medline, Scopus, Web of Science and Google Scholar databases by up to September 21, 2015, to identify RCTs investigating the impact of curcumin on circulating TNF-α concentration. Quantitative data synthesis was performed using a random-effects model, with weighed mean difference (WMD) and 95% confidence interval (CI) as summary statistics. Meta-regression and leave-one-out sensitivity analyses were performed to assess the modifiers of treatment response. Eight RCTs comprising nine treatment arms were finally selected for the meta-analysis. There was a significant reduction of circulating TNF-α concentrations following curcumin supplementation (WMD: -4.69pg/mL, 95% CI: -7.10, -2.28, p<0.001). This effect size was robust in sensitivity analysis. Meta-regression did not suggest any significant association between the circulating TNF-α-lowering effects of curcumin with either dose or duration (slope: 0.197; 95% CI: -1.73, 2.12; p=0.841) of treatment. This meta-analysis of RCTs suggested a significant effect of curcumin in lowering circulating TNF-α concentration. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest

NASA Astrophysics Data System (ADS)

Sadler, J. M.; Goodall, J. L.; Morsy, M. M.; Spencer, K.

2018-04-01

Sea level rise has already caused more frequent and severe coastal flooding and this trend will likely continue. Flood prediction is an essential part of a coastal city's capacity to adapt to and mitigate this growing problem. Complex coastal urban hydrological systems however, do not always lend themselves easily to physically-based flood prediction approaches. This paper presents a method for using a data-driven approach to estimate flood severity in an urban coastal setting using crowd-sourced data, a non-traditional but growing data source, along with environmental observation data. Two data-driven models, Poisson regression and Random Forest regression, are trained to predict the number of flood reports per storm event as a proxy for flood severity, given extensive environmental data (i.e., rainfall, tide, groundwater table level, and wind conditions) as input. The method is demonstrated using data from Norfolk, Virginia USA from September 2010 to October 2016. Quality-controlled, crowd-sourced street flooding reports ranging from 1 to 159 per storm event for 45 storm events are used to train and evaluate the models. Random Forest performed better than Poisson regression at predicting the number of flood reports and had a lower false negative rate. From the Random Forest model, total cumulative rainfall was by far the most dominant input variable in predicting flood severity, followed by low tide and lower low tide. These methods serve as a first step toward using data-driven methods for spatially and temporally detailed coastal urban flood prediction.

Interventions for reducing fear of childbirth: A systematic review and meta-analysis of clinical trials.

PubMed

MoghaddamHosseini, Vahideh; Nazarzadeh, Milad; Jahanfar, Shayesteh

2017-11-07

Fear of childbirth is a problematic mental health issue during pregnancy. But, effective interventions to reduce this problem are not well understood. To examine effective interventions for reducing fear of childbirth. The Cochrane Central Register of Controlled Trials, PubMed, Embase and PsycINFO were searched since inception till September 2017 without any restriction. Randomised controlled trials and quasi-randomised controlled trials comparing interventions for treatment of fear of childbirth were included. The standardized mean differences were pooled using random and fixed effect models. The heterogeneity was determined using the Cochran's test and I 2 index and was further explored in meta-regression model and subgroup analyses. Ten studies inclusive of 3984 participants were included in the meta-analysis (2 quasi-randomized and 8 randomized clinical trials). Eight studies investigated education and two studies investigated hypnosis-based intervention. The pooled standardized mean differences of fear for the education intervention and hypnosis group in comparison with control group were -0.46 (95% CI -0.73 to -0.19) and -0.22 (95% CI -0.34 to -0.10), respectively. Both types of interventions were effective in reducing fear of childbirth; however our pooled results revealed that educational interventions may reduce fear with double the effect of hypnosis. Further large scale randomized clinical trials and individual patient data meta-analysis are warranted for assessing the association. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Random-effects meta-analysis: the number of studies matters.

PubMed

Guolo, Annamaria; Varin, Cristiano

2017-06-01

This paper investigates the impact of the number of studies on meta-analysis and meta-regression within the random-effects model framework. It is frequently neglected that inference in random-effects models requires a substantial number of studies included in meta-analysis to guarantee reliable conclusions. Several authors warn about the risk of inaccurate results of the traditional DerSimonian and Laird approach especially in the common case of meta-analysis involving a limited number of studies. This paper presents a selection of likelihood and non-likelihood methods for inference in meta-analysis proposed to overcome the limitations of the DerSimonian and Laird procedure, with a focus on the effect of the number of studies. The applicability and the performance of the methods are investigated in terms of Type I error rates and empirical power to detect effects, according to scenarios of practical interest. Simulation studies and applications to real meta-analyses highlight that it is not possible to identify an approach uniformly superior to alternatives. The overall recommendation is to avoid the DerSimonian and Laird method when the number of meta-analysis studies is modest and prefer a more comprehensive procedure that compares alternative inferential approaches. R code for meta-analysis according to all of the inferential methods examined in the paper is provided.

Triatoma dimidiata Infestation in Chagas Disease Endemic Regions of Guatemala: Comparison of Random and Targeted Cross-Sectional Surveys

PubMed Central

King, Raymond J.; Cordon-Rosales, Celia; Cox, Jonathan; Kitron, Uriel D.

2011-01-01

Background Guatemala is presently engaged in the Central America Initiative to interrupt Chagas disease transmission by reducing intradomiciliary prevalence of Triatoma dimidiata, using targeted cross-sectional surveys to direct control measures to villages exceeding the 5% control threshold. The use of targeted surveys to guide disease control programs has not been evaluated. Here, we compare the findings from the targeted surveys to concurrent random cross-sectional surveys in two primary foci of Chagas disease transmission in central and southeastern Guatemala. Methodology/Principal Findings Survey prevalences of T. dimidiata intradomiciliary infestation by village and region were compared. Univariate logistic regression was used to assess the use of risk factors to target surveys and to evaluate indicators associated with village level intradomiciliary prevalences >5% by survey and region. Multivariate logistic regression models were developed to assess the ability of random and targeted surveys to target villages with intradomiciliary prevalence exceeding the control threshold within each region. Regional prevalences did not vary by survey; however, village prevalences were significantly greater in random surveys in central (13.0% versus 8.7%) and southeastern (22.7% versus 6.9%) Guatemala. The number of significant risk factors detected did not vary by survey in central Guatemala but differed considerably in the southeast with a greater number of significant risk factors in the random survey (e.g. land surface temperature, relative humidity, cropland, grassland, tile flooring, and stick and mud and palm and straw walls). Differences in the direction of risk factor associations were observed between regions in both survey types. The overall discriminative capacity was significantly greater in the random surveys in central and southeastern Guatemala, with an area under the receiver-operator curve (AUC) of 0.84 in the random surveys and approximately 0.64 in the targeted surveys in both regions. Sensitivity did not differ between surveys, but the positive predictive value was significantly greater in the random surveys. Conclusions/Significance Surprisingly, targeted surveys were not more effective at determining T. dimidiata prevalence or at directing control to high risk villages in comparison to random surveys. We recommend that random surveys should be selected over targeted surveys whenever possible, particularly when the focus is on directing disease control and elimination and when risk factor association has not been evaluated for all regions under investigation. PMID:21532742

Bayesian Nonparametric Inference – Why and How

PubMed Central

Müller, Peter; Mitra, Riten

2013-01-01

We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP. PMID:24368932

Effect of an Early Dose of Measles Vaccine on Morbidity Between 18 Weeks and 9 Months of Age: A Randomized, Controlled Trial in Guinea-Bissau

PubMed Central

Do, Vu An; Biering-Sørensen, Sofie; Fisker, Ane Bærent; Balé, Carlito; Rasmussen, Stine Møller; Christensen, Lone Damkjær; Martins, Cesário; Aaby, Peter; Benn, Christine Stabell

2017-01-01

Abstract Background Children in Guinea-Bissau receive measles vaccine (MV) at 9 months of age, but studies have shown that an additional dose before 9 months of age might have beneficial nonspecific effects. Within a randomized trial designed to examine nonspecific effects of early MV receipt on mortality, we conducted a substudy to investigate the effect of early MV receipt on morbidity. Methods Children were randomly assigned at a ratio of 2:1 to receive 2 doses of MV at 18 weeks and age 9 months (intervention group) or 1 dose of MV at age 9 months, in accordance with current practice (control group). Children were visited weekly from enrollment to age 9 months; the mother reported morbidity, and the field assistants examined the children. Using Cox and binomial regression models, we compared the 2 randomization groups. Results Among the 1592 children, early measles vaccination was not associated with a higher risk of the well-known adverse events of fever, rash, and convulsions within the first 14 days. From 15 days after randomization to age 9 months, early measles vaccination was associated with reductions in maternally reported diarrhea (hazard ratio [HR], 0.89; 95% confidence interval [CI], .82–.97), vomiting (HR, 0.86; 95% CI, .75–.98), and fever (HR, 0.93; 95% CI, .87–1.00). Conclusion Early MV receipt was associated with reduced general morbidity in the following months, supporting that early MV receipt may improve the general health of children. PMID:28077730

A clustered randomized trial of the effects of feedback using academic detailing compared to postal bulletin on prescribing of preventative cardiovascular therapy.

PubMed

Naughton, Corina; Feely, John; Bennett, Kathleen

2007-10-01

Interventions to promote prescribing of preventive therapies in patients with cardiovascular disease (CVD) or diabetes have reported variable success. (i) To evaluate the effect of prescribing feedback on GP practice using academic detailing compared to postal bulletin on prescribing of CVD preventive therapies in patients with CVD or diabetes at 3 and 6 months post intervention and (ii) to evaluate the intervention from a GP's perspective. Volunteer GP practices (n = 98) were randomized to receive individualized prescribing feedback via academic detailing (postal bulletin plus outreach visit) (n = 48) or postal bulletin (n = 50). The proportion of CVD or diabetic patients on statins and antiplatelet agents/warfarin pre- and post-intervention was calculated for each GP practice. Multivariate regression with a random effects model was used to compare differences between the groups adjusting for GP clustering and confounding factors. beta-Coefficients and 95% confidence intervals (CIs) are presented. There was a 3% increase in statin prescribing in CVD patients at 6 months post-intervention for both randomized groups, but there was no statistical difference between the groups (beta = 0.004; 95% CI = -0.01 to 0.02). Statin and antiplatelet/warfarin prescribing also increased in the diabetic population; there was no significant differences between the groups. GPs participating in the project expressed a high level of satisfaction with both interventions. Prescribing of preventive therapies increased in both randomized groups over the study period. But academic detailing did not have an additional effect on changing prescribing over the postal bulletin alone.

Generated effect modifiers (GEM's) in randomized clinical trials.

PubMed

Petkova, Eva; Tarpey, Thaddeus; Su, Zhe; Ogden, R Todd

2017-01-01

In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A flexible Bayesian hierarchical model of preterm birth risk among US Hispanic subgroups in relation to maternal nativity and education

PubMed Central

2011-01-01

Background Previous research has documented heterogeneity in the effects of maternal education on adverse birth outcomes by nativity and Hispanic subgroup in the United States. In this article, we considered the risk of preterm birth (PTB) using 9 years of vital statistics birth data from New York City. We employed finer categorizations of exposure than used previously and estimated the risk dose-response across the range of education by nativity and ethnicity. Methods Using Bayesian random effects logistic regression models with restricted quadratic spline terms for years of completed maternal education, we calculated and plotted the estimated posterior probabilities of PTB (gestational age < 37 weeks) for each year of education by ethnic and nativity subgroups adjusted for only maternal age, as well as with more extensive covariate adjustments. We then estimated the posterior risk difference between native and foreign born mothers by ethnicity over the continuous range of education exposures. Results The risk of PTB varied substantially by education, nativity and ethnicity. Native born groups showed higher absolute risk of PTB and declining risk associated with higher levels of education beyond about 10 years, as did foreign-born Puerto Ricans. For most other foreign born groups, however, risk of PTB was flatter across the education range. For Mexicans, Central Americans, Dominicans, South Americans and "Others", the protective effect of foreign birth diminished progressively across the educational range. Only for Puerto Ricans was there no nativity advantage for the foreign born, although small numbers of foreign born Cubans limited precision of estimates for that group. Conclusions Using flexible Bayesian regression models with random effects allowed us to estimate absolute risks without strong modeling assumptions. Risk comparisons for any sub-groups at any exposure level were simple to calculate. Shrinkage of posterior estimates through the use of random effects allowed for finer categorization of exposures without restricting joint effects to follow a fixed parametric scale. Although foreign born Hispanic women with the least education appeared to generally have low risk, this seems likely to be a marker for unmeasured environmental and behavioral factors, rather than a causally protective effect of low education itself. PMID:21504612

A flexible Bayesian hierarchical model of preterm birth risk among US Hispanic subgroups in relation to maternal nativity and education.

PubMed

Kaufman, Jay S; MacLehose, Richard F; Torrone, Elizabeth A; Savitz, David A

2011-04-19

Previous research has documented heterogeneity in the effects of maternal education on adverse birth outcomes by nativity and Hispanic subgroup in the United States. In this article, we considered the risk of preterm birth (PTB) using 9 years of vital statistics birth data from New York City. We employed finer categorizations of exposure than used previously and estimated the risk dose-response across the range of education by nativity and ethnicity. Using Bayesian random effects logistic regression models with restricted quadratic spline terms for years of completed maternal education, we calculated and plotted the estimated posterior probabilities of PTB (gestational age < 37 weeks) for each year of education by ethnic and nativity subgroups adjusted for only maternal age, as well as with more extensive covariate adjustments. We then estimated the posterior risk difference between native and foreign born mothers by ethnicity over the continuous range of education exposures. The risk of PTB varied substantially by education, nativity and ethnicity. Native born groups showed higher absolute risk of PTB and declining risk associated with higher levels of education beyond about 10 years, as did foreign-born Puerto Ricans. For most other foreign born groups, however, risk of PTB was flatter across the education range. For Mexicans, Central Americans, Dominicans, South Americans and "Others", the protective effect of foreign birth diminished progressively across the educational range. Only for Puerto Ricans was there no nativity advantage for the foreign born, although small numbers of foreign born Cubans limited precision of estimates for that group. Using flexible Bayesian regression models with random effects allowed us to estimate absolute risks without strong modeling assumptions. Risk comparisons for any sub-groups at any exposure level were simple to calculate. Shrinkage of posterior estimates through the use of random effects allowed for finer categorization of exposures without restricting joint effects to follow a fixed parametric scale. Although foreign born Hispanic women with the least education appeared to generally have low risk, this seems likely to be a marker for unmeasured environmental and behavioral factors, rather than a causally protective effect of low education itself.

Cognitive-Based Interventions to Improve Mobility: A Systematic Review and Meta-analysis.

PubMed

Marusic, Uros; Verghese, Joe; Mahoney, Jeannette R

2018-06-01

A strong relation between cognition and mobility has been identified in aging, supporting a role for enhancement mobility through cognitive-based interventions. However, a critical evaluation of the consistency of treatment effects of cognitive-based interventions is currently lacking. The objective of this study was 2-fold: (1) to review the existing literature on cognitive-based interventions aimed at improving mobility in older adults and (2) to assess the clinical effectiveness of cognitive interventions on gait performance. A systematic review of randomized controlled trials (RCT) of cognitive training interventions for improving simple (normal walking) and complex (dual task walking) gait was conducted in February 2018. Older adults without major cognitive, psychiatric, neurologic, and/or sensory impairments were included. Random effect meta-analyses and a subsequent meta-regression were performed to generate overall cognitive intervention effects on single- and dual-task walking conditions. Ten RCTs met inclusion criteria, with a total of 351 participants included in this meta-analysis. Cognitive training interventions revealed a small effect of intervention on complex gait [effect size (ES) = 0.47, 95% confidence interval (CI) 0.13 to 0.81, P = .007, I 2  = 15.85%], but not simple gait (ES = 0.35, 95% CI -0.01 to 0.71, P = .057, I 2  = 57.32%). Moreover, a meta-regression analysis revealed that intervention duration, training frequency, total number of sessions, and total minutes spent in intervention were not significant predictors of improvement in dual-task walking speed, though there was a suggestive trend toward a negative association between dual-task walking speed improvements and individual training session duration (P = .067). This meta-analysis provides support for the fact that cognitive training interventions can improve mobility-related outcomes, especially during challenging walking conditions requiring higher-order executive functions. Additional evidence from well-designed large-scale randomized clinical trials is warranted to confirm the observed effects. Copyright © 2018 AMDA – The Society for Post-Acute and Long-Term Care Medicine. All rights reserved.

Homogenization Issues in the Combustion of Heterogeneous Solid Propellants

NASA Technical Reports Server (NTRS)

Chen, M.; Buckmaster, J.; Jackson, T. L.; Massa, L.

2002-01-01

We examine random packs of discs or spheres, models for ammonium-perchlorate-in-binder propellants, and discuss their average properties. An analytical strategy is described for calculating the mean or effective heat conduction coefficient in terms of the heat conduction coefficients of the individual components, and the results are verified by comparison with those of direct numerical simulations (dns) for both 2-D (disc) and 3-D (sphere) packs across which a temperature difference is applied. Similarly, when the surface regression speed of each component is related to the surface temperature via a simple Arrhenius law, an analytical strategy is developed for calculating an effective Arrhenius law for the combination, and these results are verified using dns in which a uniform heat flux is applied to the pack surface, causing it to regress. These results are needed for homogenization strategies necessary for fully integrated 2-D or 3-D simulations of heterogeneous propellant combustion.

An Overview of Longitudinal Data Analysis Methods for Neurological Research

PubMed Central

Locascio, Joseph J.; Atri, Alireza

2011-01-01

The purpose of this article is to provide a concise, broad and readily accessible overview of longitudinal data analysis methods, aimed to be a practical guide for clinical investigators in neurology. In general, we advise that older, traditional methods, including (1) simple regression of the dependent variable on a time measure, (2) analyzing a single summary subject level number that indexes changes for each subject and (3) a general linear model approach with a fixed-subject effect, should be reserved for quick, simple or preliminary analyses. We advocate the general use of mixed-random and fixed-effect regression models for analyses of most longitudinal clinical studies. Under restrictive situations or to provide validation, we recommend: (1) repeated-measure analysis of covariance (ANCOVA), (2) ANCOVA for two time points, (3) generalized estimating equations and (4) latent growth curve/structural equation models. PMID:22203825

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

[Establishment of multiple regression model for virulence factors of Saccharomyces albicans by random amplified polymorphic DNA bands].

PubMed

Liu, Qi; Wu, Youcong; Yuan, Youhua; Bai, Li; Niu, Kun

2011-12-01

To research the relationship between the virulence factors of Saccharomyces albicans (S. albicans) and the random amplified polymorphic DNA (RAPD) bands of them, and establish the regression model by multiple regression analysis. Extracellular phospholipase, secreted proteinase, ability to generate germ tubes and adhere to oral mucosal cells of 92 strains of S. albicans were measured in vitro; RAPD-polymerase chain reaction (RAPD-PCR) was used to get their bands. Multiple regression for virulence factors of S. albicans and RAPD-PCR bands was established. The extracellular phospholipase activity was associated with 4 RAPD bands: 350, 450, 650 and 1 300 bp (P < 0.05); secreted proteinase activity of S. albicans was associated with 2 bands: 350 and 1 200 bp (P < 0.05); the ability of germ tube produce was associated with 2 bands: 400 and 550 bp (P < 0.05). Some RAPD bands will reflect the virulence factors of S. albicans indirectly. These bands would contain some important messages for regulation of S. albicans virulence factors.

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

NASA Astrophysics Data System (ADS)

Shamsoddini, A.; Aboodi, M. R.; Karami, J.

2017-09-01

Air pollution as one of the most serious forms of environmental pollutions poses huge threat to human life. Air pollution leads to environmental instability, and has harmful and undesirable effects on the environment. Modern prediction methods of the pollutant concentration are able to improve decision making and provide appropriate solutions. This study examines the performance of the Random Forest feature selection in combination with multiple-linear regression and Multilayer Perceptron Artificial Neural Networks methods, in order to achieve an efficient model to estimate carbon monoxide and nitrogen dioxide, sulfur dioxide and PM2.5 contents in the air. The results indicated that Artificial Neural Networks fed by the attributes selected by Random Forest feature selection method performed more accurate than other models for the modeling of all pollutants. The estimation accuracy of sulfur dioxide emissions was lower than the other air contaminants whereas the nitrogen dioxide was predicted more accurate than the other pollutants.

Multilevel Modeling in Psychosomatic Medicine Research

PubMed Central

Myers, Nicholas D.; Brincks, Ahnalee M.; Ames, Allison J.; Prado, Guillermo J.; Penedo, Frank J.; Benedict, Catherine

2012-01-01

The primary purpose of this manuscript is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The manuscript begins with a general introduction to multilevel modeling. Multilevel regression modeling at two-levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated datasets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including: communication of model specification, parameter interpretation, sample size and power, and missing data. Input and key output files from Mplus and SAS are provided. A cluster randomized trial with repeated measures (i.e., three-level regression model) is then briefly presented with simulated data based on some core ideas from a cognitive behavioral stress management intervention in prostate cancer. PMID:23107843

Complementary nonparametric analysis of covariance for logistic regression in a randomized clinical trial setting.

PubMed

Tangen, C M; Koch, G G

1999-03-01

In the randomized clinical trial setting, controlling for covariates is expected to produce variance reduction for the treatment parameter estimate and to adjust for random imbalances of covariates between the treatment groups. However, for the logistic regression model, variance reduction is not obviously obtained. This can lead to concerns about the assumptions of the logistic model. We introduce a complementary nonparametric method for covariate adjustment. It provides results that are usually compatible with expectations for analysis of covariance. The only assumptions required are based on randomization and sampling arguments. The resulting treatment parameter is a (unconditional) population average log-odds ratio that has been adjusted for random imbalance of covariates. Data from a randomized clinical trial are used to compare results from the traditional maximum likelihood logistic method with those from the nonparametric logistic method. We examine treatment parameter estimates, corresponding standard errors, and significance levels in models with and without covariate adjustment. In addition, we discuss differences between unconditional population average treatment parameters and conditional subpopulation average treatment parameters. Additional features of the nonparametric method, including stratified (multicenter) and multivariate (multivisit) analyses, are illustrated. Extensions of this methodology to the proportional odds model are also made.

Endovascular Therapy Is Effective and Safe for Patients With Severe Ischemic Stroke: Pooled Analysis of Interventional Management of Stroke III and Multicenter Randomized Clinical Trial of Endovascular Therapy for Acute Ischemic Stroke in the Netherlands Data.

PubMed

Broderick, Joseph P; Berkhemer, Olvert A; Palesch, Yuko Y; Dippel, Diederik W J; Foster, Lydia D; Roos, Yvo B W E M; van der Lugt, Aad; Tomsick, Thomas A; Majoie, Charles B L M; van Zwam, Wim H; Demchuk, Andrew M; van Oostenbrugge, Robert J; Khatri, Pooja; Lingsma, Hester F; Hill, Michael D; Roozenbeek, Bob; Jauch, Edward C; Jovin, Tudor G; Yan, Bernard; von Kummer, Rüdiger; Molina, Carlos A; Goyal, Mayank; Schonewille, Wouter J; Mazighi, Mikael; Engelter, Stefan T; Anderson, Craig S; Spilker, Judith; Carrozzella, Janice; Ryckborst, Karla J; Janis, L Scott; Simpson, Kit N

2015-12-01

We assessed the effect of endovascular treatment in acute ischemic stroke patients with severe neurological deficit (National Institutes of Health Stroke Scale score, ≥20) after a prespecified analysis plan. The pooled analysis of the Interventional Management of Stroke III (IMS III) and Multicenter Randomized Clinical Trial of Endovascular Therapy for Acute Ischemic Stroke in the Netherlands (MR CLEAN) trials included participants with an National Institutes of Health Stroke Scale score of ≥20 before intravenous tissue-type plasminogen activator (tPA) treatment (IMS III) or randomization (MR CLEAN) who were treated with intravenous tPA ≤3 hours of stroke onset. Our hypothesis was that participants with severe stroke randomized to endovascular therapy after intravenous tPA would have improved 90-day outcome (distribution of modified Rankin Scale scores), when compared with those who received intravenous tPA alone. Among 342 participants in the pooled analysis (194 from IMS III and 148 from MR CLEAN), an ordinal logistic regression model showed that the endovascular group had superior 90-day outcome compared with the intravenous tPA group (adjusted odds ratio, 1.78; 95% confidence interval, 1.20-2.66). In the logistic regression model of the dichotomous outcome (modified Rankin Scale score, 0-2, or functional independence), the endovascular group had superior outcomes (adjusted odds ratio, 1.97; 95% confidence interval, 1.09-3.56). Functional independence (modified Rankin Scale score, ≤2) at 90 days was 25% in the endovascular group when compared with 14% in the intravenous tPA group. Endovascular therapy after intravenous tPA within 3 hours of symptom onset improves functional outcome at 90 days after severe ischemic stroke. URL: http://www.clinicaltrials.gov. Unique identifier: NCT00359424 (IMS III) and ISRCTN10888758 (MR CLEAN). © 2015 American Heart Association, Inc.

How Does Physical Activity Intervention Improve Self-Esteem and Self-Concept in Children and Adolescents? Evidence from a Meta-Analysis

PubMed Central

Liu, Mingli; Wu, Lang; Ming, Qingsen

2015-01-01

Objective To perform a systematic review and meta-analysis for the effects of physical activity intervention on self-esteem and self-concept in children and adolescents, and to identify moderator variables by meta-regression. Design A meta-analysis and meta-regression. Method Relevant studies were identified through a comprehensive search of electronic databases. Study inclusion criteria were: (1) intervention should be supervised physical activity, (2) reported sufficient data to estimate pooled effect sizes of physical activity intervention on self-esteem or self-concept, (3) participants’ ages ranged from 3 to 20 years, and (4) a control or comparison group was included. For each study, study design, intervention design and participant characteristics were extracted. R software (version 3.1.3) and Stata (version 12.0) were used to synthesize effect sizes and perform moderation analyses for determining moderators. Results Twenty-five randomized controlled trial (RCT) studies and 13 non-randomized controlled trial (non-RCT) studies including a total of 2991 cases were identified. Significant positive effects were found in RCTs for intervention of physical activity alone on general self outcomes (Hedges’ g = 0.29, 95% confidence interval [CI]: 0.14 to 0.45; p = 0.001), self-concept (Hedges’ g = 0.49, 95%CI: 0.10 to 0.88, p = 0.014) and self-worth (Hedges’ g = 0.31, 95%CI: 0.13 to 0.49, p = 0.005). There was no significant effect of intervention of physical activity alone on any outcomes in non-RCTs, as well as in studies with intervention of physical activity combined with other strategies. Meta-regression analysis revealed that higher treatment effects were associated with setting of intervention in RCTs (β = 0.31, 95%CI: 0.07 to 0.55, p = 0.013). Conclusion Intervention of physical activity alone is associated with increased self-concept and self-worth in children and adolescents. And there is a stronger association with school-based and gymnasium-based intervention compared with other settings. PMID:26241879

Atomoxetine-Related Change in Sluggish Cognitive Tempo Is Partially Independent of Change in Attention-Deficit/Hyperactivity Disorder Inattentive Symptoms.

PubMed

McBurnett, Keith; Clemow, David; Williams, David; Villodas, Miguel; Wietecha, Linda; Barkley, Russell

2017-02-01

To evaluate effects of atomoxetine versus placebo on sluggish cognitive tempo (SCT) and determine factors affecting improvement of SCT in children with attention-deficit/hyperactivity disorder (ADHD) with dyslexia (ADHD+D) or dyslexia only. This is a post hoc analysis of a 16-week placebo-controlled, double-blind randomized phase of a previously reported atomoxetine study in children aged 10-16 years with ADHD+D, Dyslexia-only, or ADHD-only (no placebo arm). Least squares mean changes from baseline to endpoint for atomoxetine versus placebo on the Kiddie-Sluggish Cognitive Tempo Interview (K-SCT) (Parent, Teacher, and Youth) were analyzed using analysis of covariance and multiple regression (partial R 2 ) analyses to test contributions of ADHD and dyslexia to improvements in K-SCT scores. Results were examined for the three informants within the three diagnostic groups (nine outcomes). Atomoxetine treatment was associated with significant reductions from baseline in seven of the nine outcomes using the p = 0.05 significance level, appropriate for exploratory analysis. When change in ADHD symptom severity was controlled, all of the seven SCT outcomes remained significant; changes in effect sizes were minimal. Regression analyses using SCT change as the criterion found a significant contribution by inattention change only for parent report, whereas, baseline SCT severity was a significant predictor in the randomized groups with the exception of teacher report in the Dyslexia-only group. Given that controlling for change in ADHD symptoms had little effect on change in SCT scores, findings suggest that change in SCT is substantially independent of change in ADHD. By inference, SCT and its response to treatment is a partially distinct phenomenon from ADHD response. Regression analyses did not reveal global effects of inattention change on SCT change; instead, baseline SCT severity was the strongest predictor of placebo-controlled treatment effect on SCT. Atomoxetine effects on SCT appear to be best predicted by how much room for improvement exists for SCT rather than by severity or improvement in inattention. NCT00607919, www.clinicaltrials.gov.

Accounting for Heterogeneity in Relative Treatment Effects for Use in Cost-Effectiveness Models and Value-of-Information Analyses

PubMed Central

Soares, Marta O.; Palmer, Stephen; Ades, Anthony E.; Harrison, David; Shankar-Hari, Manu; Rowan, Kathy M.

2015-01-01

Cost-effectiveness analysis (CEA) models are routinely used to inform health care policy. Key model inputs include relative effectiveness of competing treatments, typically informed by meta-analysis. Heterogeneity is ubiquitous in meta-analysis, and random effects models are usually used when there is variability in effects across studies. In the absence of observed treatment effect modifiers, various summaries from the random effects distribution (random effects mean, predictive distribution, random effects distribution, or study-specific estimate [shrunken or independent of other studies]) can be used depending on the relationship between the setting for the decision (population characteristics, treatment definitions, and other contextual factors) and the included studies. If covariates have been measured that could potentially explain the heterogeneity, then these can be included in a meta-regression model. We describe how covariates can be included in a network meta-analysis model and how the output from such an analysis can be used in a CEA model. We outline a model selection procedure to help choose between competing models and stress the importance of clinical input. We illustrate the approach with a health technology assessment of intravenous immunoglobulin for the management of adult patients with severe sepsis in an intensive care setting, which exemplifies how risk of bias information can be incorporated into CEA models. We show that the results of the CEA and value-of-information analyses are sensitive to the model and highlight the importance of sensitivity analyses when conducting CEA in the presence of heterogeneity. The methods presented extend naturally to heterogeneity in other model inputs, such as baseline risk. PMID:25712447

Accounting for Heterogeneity in Relative Treatment Effects for Use in Cost-Effectiveness Models and Value-of-Information Analyses.

PubMed

Welton, Nicky J; Soares, Marta O; Palmer, Stephen; Ades, Anthony E; Harrison, David; Shankar-Hari, Manu; Rowan, Kathy M

2015-07-01

Cost-effectiveness analysis (CEA) models are routinely used to inform health care policy. Key model inputs include relative effectiveness of competing treatments, typically informed by meta-analysis. Heterogeneity is ubiquitous in meta-analysis, and random effects models are usually used when there is variability in effects across studies. In the absence of observed treatment effect modifiers, various summaries from the random effects distribution (random effects mean, predictive distribution, random effects distribution, or study-specific estimate [shrunken or independent of other studies]) can be used depending on the relationship between the setting for the decision (population characteristics, treatment definitions, and other contextual factors) and the included studies. If covariates have been measured that could potentially explain the heterogeneity, then these can be included in a meta-regression model. We describe how covariates can be included in a network meta-analysis model and how the output from such an analysis can be used in a CEA model. We outline a model selection procedure to help choose between competing models and stress the importance of clinical input. We illustrate the approach with a health technology assessment of intravenous immunoglobulin for the management of adult patients with severe sepsis in an intensive care setting, which exemplifies how risk of bias information can be incorporated into CEA models. We show that the results of the CEA and value-of-information analyses are sensitive to the model and highlight the importance of sensitivity analyses when conducting CEA in the presence of heterogeneity. The methods presented extend naturally to heterogeneity in other model inputs, such as baseline risk. © The Author(s) 2015.

Divergence instability of pipes conveying fluid with uncertain flow velocity

NASA Astrophysics Data System (ADS)

Rahmati, Mehdi; Mirdamadi, Hamid Reza; Goli, Sareh

2018-02-01

This article deals with investigation of probabilistic stability of pipes conveying fluid with stochastic flow velocity in time domain. As a matter of fact, this study has focused on the randomness effects of flow velocity on stability of pipes conveying fluid while most of research efforts have only focused on the influences of deterministic parameters on the system stability. The Euler-Bernoulli beam and plug flow theory are employed to model pipe structure and internal flow, respectively. In addition, flow velocity is considered as a stationary random process with Gaussian distribution. Afterwards, the stochastic averaging method and Routh's stability criterion are used so as to investigate the stability conditions of system. Consequently, the effects of boundary conditions, viscoelastic damping, mass ratio, and elastic foundation on the stability regions are discussed. Results delineate that the critical mean flow velocity decreases by increasing power spectral density (PSD) of the random velocity. Moreover, by increasing PSD from zero, the type effects of boundary condition and presence of elastic foundation are diminished, while the influences of viscoelastic damping and mass ratio could increase. Finally, to have a more applicable study, regression analysis is utilized to develop design equations and facilitate further analyses for design purposes.

The hemodynamic effects of intravenous paracetamol (acetaminophen) vs normal saline in cardiac surgery patients: A single center placebo controlled randomized study

PubMed Central

Churilov, Leonid

2018-01-01

The hemodynamic effects of intravenous (IV) paracetamol in patients undergoing cardiac surgery are unknown. We performed a prospective single center placebo controlled randomized study with parallel group design in adult patients undergoing elective cardiac surgery. Participants received paracetamol (1 gram) IV or placebo (an equal volume of 0.9% saline) preoperatively followed by two postoperative doses 6 hours apart. The primary endpoint was the absolute change in systolic (SBP) 30 minutes after the preoperative infusion, analysed using an ANCOVA model. Secondary endpoints included absolute changes in mean arterial pressure (MAP) and diastolic blood pressure (DPB), and other key hemodynamic variables after each infusion. All other endpoints were analysed using random-effect generalized least squares regression modelling with individual patients treated as random effects. Fifty participants were randomly assigned to receive paracetamol (n = 25) or placebo (n = 25). Post preoperative infusion, paracetamol decreased SBP by a mean (SD) of 13 (18) mmHg, p = 0.02, compared to a mean (SD) of 1 (11) mmHg with saline. Paracetamol decreased MAP and DBP by a mean (SD) of 9 (12) mmHg and 8 (9) mmHg (p = 0.01 and 0.02), respectively, compared to a mean (SD) of 1 (8) mmHg and 0 (6) mmHg with placebo. Postoperatively, there were no significant differences in pressure or flow based hemodynamic parameters in both groups. This study provides high quality evidence that the administration of IV paracetamol in patients undergoing cardiac surgery causes a transient decrease in preoperative blood pressure when administered before surgery but no adverse hemodynamic effects when administered in the postoperative setting. PMID:29659631

The hemodynamic effects of intravenous paracetamol (acetaminophen) vs normal saline in cardiac surgery patients: A single center placebo controlled randomized study.

PubMed

Chiam, Elizabeth; Bellomo, Rinaldo; Churilov, Leonid; Weinberg, Laurence

2018-01-01

The hemodynamic effects of intravenous (IV) paracetamol in patients undergoing cardiac surgery are unknown. We performed a prospective single center placebo controlled randomized study with parallel group design in adult patients undergoing elective cardiac surgery. Participants received paracetamol (1 gram) IV or placebo (an equal volume of 0.9% saline) preoperatively followed by two postoperative doses 6 hours apart. The primary endpoint was the absolute change in systolic (SBP) 30 minutes after the preoperative infusion, analysed using an ANCOVA model. Secondary endpoints included absolute changes in mean arterial pressure (MAP) and diastolic blood pressure (DPB), and other key hemodynamic variables after each infusion. All other endpoints were analysed using random-effect generalized least squares regression modelling with individual patients treated as random effects. Fifty participants were randomly assigned to receive paracetamol (n = 25) or placebo (n = 25). Post preoperative infusion, paracetamol decreased SBP by a mean (SD) of 13 (18) mmHg, p = 0.02, compared to a mean (SD) of 1 (11) mmHg with saline. Paracetamol decreased MAP and DBP by a mean (SD) of 9 (12) mmHg and 8 (9) mmHg (p = 0.01 and 0.02), respectively, compared to a mean (SD) of 1 (8) mmHg and 0 (6) mmHg with placebo. Postoperatively, there were no significant differences in pressure or flow based hemodynamic parameters in both groups. This study provides high quality evidence that the administration of IV paracetamol in patients undergoing cardiac surgery causes a transient decrease in preoperative blood pressure when administered before surgery but no adverse hemodynamic effects when administered in the postoperative setting.

The value of a statistical life: a meta-analysis with a mixed effects regression model.

PubMed

Bellavance, François; Dionne, Georges; Lebeau, Martin

2009-03-01

The value of a statistical life (VSL) is a very controversial topic, but one which is essential to the optimization of governmental decisions. We see a great variability in the values obtained from different studies. The source of this variability needs to be understood, in order to offer public decision-makers better guidance in choosing a value and to set clearer guidelines for future research on the topic. This article presents a meta-analysis based on 39 observations obtained from 37 studies (from nine different countries) which all use a hedonic wage method to calculate the VSL. Our meta-analysis is innovative in that it is the first to use the mixed effects regression model [Raudenbush, S.W., 1994. Random effects models. In: Cooper, H., Hedges, L.V. (Eds.), The Handbook of Research Synthesis. Russel Sage Foundation, New York] to analyze studies on the value of a statistical life. We conclude that the variability found in the values studied stems in large part from differences in methodologies.

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

A statistical methodology for estimating transport parameters: Theory and applications to one-dimensional advectivec-dispersive systems

USGS Publications Warehouse

Wagner, Brian J.; Gorelick, Steven M.

1986-01-01

A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference contaminant transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2–3 times more reliable than estimates based on temporal data for all parameters except velocity. Comparison of estimated linear and nonlinear confidence intervals based upon Monte Carlo analysis showed that the linear approximation is poor for dispersion coefficient and zero-order production coefficient when data are collected over time. In addition, examples demonstrate transport parameter estimation for two real one-dimensional systems. First, the longitudinal dispersivity and effective porosity of an unsaturated soil are estimated using laboratory column data. We compare the reliability of estimates based upon data from individual laboratory experiments versus estimates based upon pooled data from several experiments. Second, the simulation nonlinear regression procedure is extended to include an additional governing equation that describes delayed storage during contaminant transport. The model is applied to analyze the trends, variability, and interrelationship of parameters in a mourtain stream in northern California.

Acupuncture for musculoskeletal pain: A meta-analysis and meta-regression of sham-controlled randomized clinical trials

PubMed Central

Yuan, Qi-ling; Wang, Peng; Liu, Liang; Sun, Fu; Cai, Yong-song; Wu, Wen-tao; Ye, Mao-lin; Ma, Jiang-tao; Xu, Bang-bang; Zhang, Yin-gang

2016-01-01

The aims of this systematic review were to study the analgesic effect of real acupuncture and to explore whether sham acupuncture (SA) type is related to the estimated effect of real acupuncture for musculoskeletal pain. Five databases were searched. The outcome was pain or disability immediately (≤1 week) following an intervention. Standardized mean differences (SMDs) with 95% confidence intervals were calculated. Meta-regression was used to explore possible sources of heterogeneity. Sixty-three studies (6382 individuals) were included. Eight condition types were included. The pooled effect size was moderate for pain relief (59 trials, 4980 individuals, SMD −0.61, 95% CI −0.76 to −0.47; P < 0.001) and large for disability improvement (31 trials, 4876 individuals, −0.77, −1.05 to −0.49; P < 0.001). In a univariate meta-regression model, sham needle location and/or depth could explain most or all heterogeneities for some conditions (e.g., shoulder pain, low back pain, osteoarthritis, myofascial pain, and fibromyalgia); however, the interactions between subgroups via these covariates were not significant (P < 0.05). Our review provided low-quality evidence that real acupuncture has a moderate effect (approximate 12-point reduction on the 100-mm visual analogue scale) on musculoskeletal pain. SA type did not appear to be related to the estimated effect of real acupuncture. PMID:27471137

Effects of Long-Term Vitamin D Supplementation on Regression and Metabolic Status of Cervical Intraepithelial Neoplasia: a Randomized, Double-Blind, Placebo-Controlled Trial.

PubMed

Vahedpoor, Zahra; Jamilian, Mehri; Bahmani, Fereshteh; Aghadavod, Esmat; Karamali, Maryam; Kashanian, Maryam; Asemi, Zatollah

2017-02-01

We are not aware of any study examining the effects of long term vitamin D administration on regression and metabolic status of patients with cervical intraepithelial neoplasia grade 1 (CIN1). This study was performed to evaluate the effects of long-term vitamin D administration on regression and metabolic status of patients with CIN1. This randomized, double-blind, placebo-controlled trial was performed among 58 women diagnosed with CIN1. CIN1 diagnosis was performed based on specific diagnostic procedures of biopsy, pathological diagnosis, and colposcopy. Patients were randomly allocated into two groups to take 50,000 IU vitamin D3 supplements (n = 29) or placebo (n = 29) every 2 weeks for 6 months. Fasting blood samples were taken at the beginning of the study and end-of-trial to measure related markers. After 6 months of vitamin D administration, greater percentage of women in the vitamin D group had regressed CIN1 (84.6 vs. 53.8%, P = 0.01) than those in the placebo group. Long-term vitamin D supplementation increased serum-25(OH) vitamin D levels in the intervention group compared to the placebo group (+12.3 ± 11.4 vs. -0.1 ± 3.7 ng/mL, P < 0.001). In addition, vitamin D intake led to significant decreases in serum insulin levels (-5.3 ± 7.3 vs. +2.4 ± 5.9 μIU/mL, P < 0.001), homeostasis model of assessment-insulin resistance (-1.2 ± 1.6 vs. +0.5 ± 1.2, P < 0.001), homeostatic model assessment-Beta cell function (P = 0.005) and a significant elevation in quantitative insulin sensitivity check index (+0.03 ± 0.04 vs. -0.007 ± 0.02, P < 0.001) compared with the placebo group. Additionally, significant increases in plasma nitric oxide (NO) (+15.5 ± 10.3 vs. +4.0 ± 13.4 μmol/L, P = 0.001), total antioxidant capacity (TAC) (P = 0.04), total glutathione (GSH) (+11.8 ± 153.5 vs. -294.2 ± 595.1 μmol/L, P = 0.01) and a significant reduction in plasma malondialdehyde (MDA) levels (-0.8 ± 1.0 vs. -0.03 ± 1.4 μmol/L, P = 0.03) were observed following the administration of vitamin D supplements compared with the placebo group. In conclusion, vitamin D3 administration for 6 months among women with CIN1 resulted in its regression and had beneficial effects on markers of insulin metabolism, plasma NO, TAC, GSH and MDA levels. Clinical trial registration number www.irct.ir : IRCT201412065623N30.

The effect of 2 adhesive products on skin integrity used for fixation of hip and knee surgical dressings: a randomized controlled trial.

PubMed

Ter, Nergiz; Yavuz, Meryem; Aydoğdu, Semih; Kaya Biçer, Elcil

2015-01-01

The aim of this study was to compare effects of 2 adhesive products, a nonwoven porous adhesive bandage (NPAB) and transparent film adhesive bandage (TFAB), on skin integrity for fixation of hip and knee surgical dressings. A prospective, randomized study was conducted on 300 patients who underwent hip and knee surgery (arthroplasty, fixation of fractures, tumor operations, etc). Participants were randomized into 2 equal groups according to the applied surgical dressing fixation product (NPAB or TFAB). Skin changes (edema, erythema, blister, peeling of blister, mechanical peeling, and maceration), drying of incision, serous discharge, and early infection symptoms were evaluated. The skin integrity was found to be impaired in 4.0% (n = 6) of the 150 NPAB patients and in 10.7% (n = 16) of the 150 TFAB patients (P = .02). Logistic regression analysis showed that the risk of impaired skin integrity increased 2.5-fold when TFAB was applied (P = .03). The NPAB was associated with a reduced likelihood of impaired skin integrity following hip and knee surgery.

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Fitting parametric random effects models in very large data sets with application to VHA national data

PubMed Central

2012-01-01

Background With the current focus on personalized medicine, patient/subject level inference is often of key interest in translational research. As a result, random effects models (REM) are becoming popular for patient level inference. However, for very large data sets that are characterized by large sample size, it can be difficult to fit REM using commonly available statistical software such as SAS since they require inordinate amounts of computer time and memory allocations beyond what are available preventing model convergence. For example, in a retrospective cohort study of over 800,000 Veterans with type 2 diabetes with longitudinal data over 5 years, fitting REM via generalized linear mixed modeling using currently available standard procedures in SAS (e.g. PROC GLIMMIX) was very difficult and same problems exist in Stata’s gllamm or R’s lme packages. Thus, this study proposes and assesses the performance of a meta regression approach and makes comparison with methods based on sampling of the full data. Data We use both simulated and real data from a national cohort of Veterans with type 2 diabetes (n=890,394) which was created by linking multiple patient and administrative files resulting in a cohort with longitudinal data collected over 5 years. Methods and results The outcome of interest was mean annual HbA1c measured over a 5 years period. Using this outcome, we compared parameter estimates from the proposed random effects meta regression (REMR) with estimates based on simple random sampling and VISN (Veterans Integrated Service Networks) based stratified sampling of the full data. Our results indicate that REMR provides parameter estimates that are less likely to be biased with tighter confidence intervals when the VISN level estimates are homogenous. Conclusion When the interest is to fit REM in repeated measures data with very large sample size, REMR can be used as a good alternative. It leads to reasonable inference for both Gaussian and non-Gaussian responses if parameter estimates are homogeneous across VISNs. PMID:23095325

Interaction between the FTO gene, body mass index and depression: meta-analysis of 13701 individuals†

PubMed Central

Rivera, Margarita; Locke, Adam E.; Corre, Tanguy; Czamara, Darina; Wolf, Christiane; Ching-Lopez, Ana; Milaneschi, Yuri; Kloiber, Stefan; Cohen-Woods, Sara; Rucker, James; Aitchison, Katherine J.; Bergmann, Sven; Boomsma, Dorret I.; Craddock, Nick; Gill, Michael; Holsboer, Florian; Hottenga, Jouke-Jan; Korszun, Ania; Kutalik, Zoltan; Lucae, Susanne; Maier, Wolfgang; Mors, Ole; Müller-Myhsok, Bertram; Owen, Michael J.; Penninx, Brenda W. J. H.; Preisig, Martin; Rice, John; Rietschel, Marcella; Tozzi, Federica; Uher, Rudolf; Vollenweider, Peter; Waeber, Gerard; Willemsen, Gonneke; Craig, Ian W.; Farmer, Anne E.; Lewis, Cathryn M.; Breen, Gerome; McGuffin, Peter

2017-01-01

Background Depression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the FTO gene, suggesting its implication in the association between depression and obesity. Aims To confirm these findings by investigating the FTO polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis. Method The sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT. Results In the replication cohorts, we observed a significant interaction between FTO, BMI and depression with fixed effects meta-analysis (β = 0.12, P = 2.7 × 10−4) and with the Han/Eskin random effects method (P = 1.4 × 10−7) but not with traditional random effects (β = 0.1, P = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, P = 0.027) being highly significant based on the Han/Eskin model (P = 6.9 × 10−8). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of FTO. Conclusions This meta-analysis provides additional support for a significant interaction between FTO, depression and BMI, indicating that depression increases the effect of FTO on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression. PMID:28642257

The effect of albendazole treatment on seizure outcomes in patients with symptomatic neurocysticercosis.

PubMed

Romo, Matthew L; Wyka, Katarzyna; Carpio, Arturo; Leslie, Denise; Andrews, Howard; Bagiella, Emilia; Hauser, W Allen; Kelvin, Elizabeth A

2015-11-01

Randomized controlled trials have found an inconsistent effect of anthelmintic treatment on long-term seizure outcomes in neurocysticercosis. The objective of this study was to further explore the effect of albendazole treatment on long-term seizure outcomes and to determine if there is evidence for a differential effect by seizure type. In this trial, 178 patients with active or transitional neurocysticercosis cysts and new-onset symptoms were randomized to 8 days of treatment with albendazole (n=88) or placebo (n=90), both with prednisone, and followed for 24 months. We used negative binomial regression and logistic regression models to determine the effect of albendazole on the number of seizures and probability of recurrent or new-onset seizures, respectively, over follow-up. Treatment with albendazole was associated with a reduction in the number of seizures during 24 months of follow-up, but this was only significant for generalized seizures during months 1-12 (unadjusted rate ratio [RR] 0.19; 95% CI: 0.04-0.91) and months 1-24 (unadjusted RR 0.06; 95% CI: 0.01-0.57). We did not detect a significant effect of albendazole on reducing the number of focal seizures or on the probability of having a seizure, regardless of seizure type or time period. Albendazole treatment may be associated with some symptomatic improvement; however, this association seems to be specific to generalized seizures. Future research is needed to identify strategies to better reduce long-term seizure burden in patients with neurocysticercosis. © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Comparison of low-dose spinal anesthesia and single-shot femoral block combination with conventional dose spinal anesthesia in outpatient arthroscopic meniscus repair.

PubMed

Turhan, K S Cakar; Akmese, R; Ozkan, F; Okten, F F

2015-04-01

In the current prospective, randomized study, we aimed to compare the effects of low dose selective spinal anesthesia with 5 mg of hyperbaric bupivacaine and single-shot femoral nerve block combination with conventional dose selective spinal anesthesia in terms of intraoperative anesthesia characteristics, block recovery characteristics, and postoperative analgesic consumption. After obtaining institutional Ethics Committee approval, 52 ASA I-II patients aged 25-65, undergoing arthroscopic meniscus repair were randomly assigned to Group S (conventional dose selective spinal anesthesia with 10 mg bupivacaine) and Group FS (low-dose selective spinal anesthesia with 5mg bupivacaine +single-shot femoral block with 0.25% bupivacaine). Primary endpoints were time to reach T12 sensory block level, L2 regression, and complete motor block regression. Secondary endpoints were maximum sensory block level (MSBL); time to reach MSBL, time to first urination, time to first analgesic consumption and pain severity at the time of first mobilization. Demographic characteristics were similar in both groups (p > 0.05). MSBL and time to reach T12 sensory level were similar in both groups (p > 0.05). Time to reach L2 regression, complete motor block regression, and time to first micturition were significantly shorter; time to first analgesic consumption was significantly longer; and total analgesic consumption and severity of pain at time of first mobilization were significantly lower in Group FS (p < 0.05). The findings of the current study suggest that addition of single-shot femoral block to low dose spinal anesthesia could be an alternative to conventional dose spinal anesthesia in outpatient arthroscopic meniscus repair. NCT02322372.

Evaluating Disease Severity in Chronic Pain Patients with and without Fibromyalgia: A Comparison of the Symptom Impact Questionnaire and the Polysymptomatic Distress Scale.

PubMed

Friend, Ronald; Bennett, Robert M

2015-12-01

To compare the relative effectiveness of the Polysymptomatic Distress Scale (PSD) with the Symptom Impact Questionnaire (SIQR), the disease-neutral revision of the updated Fibromyalgia Impact Questionnaire (FIQR), in their ability to assess disease activity in patients with rheumatic disorders both with and without fibromyalgia (FM). The study included 321 patients from 8 clinical practices with some 16 different chronic pain disorders. Disease severity was assessed by the Medical Outcomes Study Short Form-36 (SF-36). Univariate analyses were used to assess the magnitude of PSD and SIQR correlations with SF-36 subscales. Hierarchical stepwise regression was used to evaluate the unique contribution of the PSD and SIQR to the SF-36. Random forest regression probed the relative importance of the SIQR and PSD components as predictors of SF-36. The correlations with the SF-36 subscales were significantly higher for the SIQR (0.48 to 0.78) than the PSD (0.29 to 0.56; p < 0.001). Stepwise regression revealed that the SIQR was contributing additional unique variance on SF-36 subscales, which was not the case for the PSD. Random forest regression showed SIQR Function, Symptoms, and Global Impact subscales were more important predictors of SF-36 than the PSD. The single SIQR pain item contributed 55% of SF-36 pain variance compared to 23% with the 19-point WPI (the Widespread Pain Index component of PSD). The SIQR, the disease-neutral revision of the updated FIQ, has several important advantages over the PSD in the evaluation of disease severity in chronic pain disorders.

Predicted effect size of lisdexamfetamine treatment of attention deficit/hyperactivity disorder (ADHD) in European adults: Estimates based on indirect analysis using a systematic review and meta-regression analysis.

PubMed

Fridman, M; Hodgkins, P S; Kahle, J S; Erder, M H

2015-06-01

There are few approved therapies for adults with attention-deficit/hyperactivity disorder (ADHD) in Europe. Lisdexamfetamine (LDX) is an effective treatment for ADHD; however, no clinical trials examining the efficacy of LDX specifically in European adults have been conducted. Therefore, to estimate the efficacy of LDX in European adults we performed a meta-regression of existing clinical data. A systematic review identified US- and Europe-based randomized efficacy trials of LDX, atomoxetine (ATX), or osmotic-release oral system methylphenidate (OROS-MPH) in children/adolescents and adults. A meta-regression model was then fitted to the published/calculated effect sizes (Cohen's d) using medication, geographical location, and age group as predictors. The LDX effect size in European adults was extrapolated from the fitted model. Sensitivity analyses performed included using adult-only studies and adding studies with placebo designs other than a standard pill-placebo design. Twenty-two of 2832 identified articles met inclusion criteria. The model-estimated effect size of LDX for European adults was 1.070 (95% confidence interval: 0.738, 1.401), larger than the 0.8 threshold for large effect sizes. The overall model fit was adequate (80%) and stable in the sensitivity analyses. This model predicts that LDX may have a large treatment effect size in European adults with ADHD. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

Meta-analysis of vaccine effectiveness of mumps-containing vaccine under different immunization strategies in China.

PubMed

Wang, Huaqing; Hu, Yongmei; Zhang, Guomin; Zheng, Jingshan; Li, Li; An, Zhijie

2014-08-20

To evaluate vaccine effectiveness (VE) of mumps-containing vaccine (MuV) under different immunization strategies. We conducted Medline, Embase, China National Knowledge Internet (CNKI), and Wan Fang Database (WF) searches for Chinese and English language articles describing studies of mumps VE in a Chinese population. Evaluated articles were scored on quality using the Newcastle-Ottawa Scale. Meta-analysis was conducted using random effects models. Sensitivity analysis, subgroup analysis and meta-regression were conducted to explore heterogeneity. A total of 32 studies in 19 papers were included; 14 were case-control studies, and 18 were cohort studies. Half of the studies were of high quality; 41% were of moderate quality. The overall VE for mumps containing vaccine (either one dose or two doses) was 85% (95% CI 76-90%) for cohort studies and 88% (95% CI 82-92%) for case-control studies. Using random effects meta-regression we found significant differences in some study covariates; for instance, VE varied by population (VE=88% in day care versus 69% in pupil, p=0.008) and emergency versus routine immunization (VE=80% for routine immunization versus 95% for emergency immunization, p=0.041). However, these results must be interpreted with caution due to the low number of studies in subgroups, with the permutation test giving non-significant results that indicated that the results may be due to chance. MuV provides good protection from mumps infection. Further studies of mumps VE with larger sample sizes enabling subgroup analyses will be needed to confirm our findings. Copyright © 2014 Elsevier Ltd. All rights reserved.

Impact of social and technological distraction on pedestrian crossing behaviour: an observational study

PubMed Central

Thompson, Leah L; Rivara, Frederick P; Ayyagari, Rajiv C; Ebel, Beth E

2013-01-01

Objectives The objective of the present work was to study the impact of technological and social distraction on cautionary behaviours and crossing times in pedestrians. Methods Pedestrians were observed at 20 high-risk intersections during 1 of 3 randomly assigned time windows in 2012. Observers recorded demographic and behavioural information, including use of a mobile device (talking on the phone, text messaging, or listening to music). We examined the association between distraction and crossing behaviours, adjusting for age and gender. All multivariate analyses were conducted with random effect logistic regression (binary outcomes) and random effect linear regression (continuous outcomes), accounting for clustering by site. Results Observers recorded crossing behaviours for 1102 pedestrians. Nearly one-third (29.8%) of all pedestrians performed a distracting activity while crossing. Distractions included listening to music (11.2%), text messaging (7.3%) and using a handheld phone (6.2%). Text messaging, mobile phone use and talking with a companion increased crossing time. Texting pedestrians took 1.87 additional seconds (18.0%) to cross the average intersection (3.4 lanes), compared to undistracted pedestrians. Texting pedestrians were 3.9 times more likely than undistracted pedestrians to display at least 1 unsafe crossing behaviour (disobeying the lights, crossing mid-intersection, or failing to look both ways). Pedestrians listening to music walked more than half a second (0.54) faster across the average intersection than undistracted pedestrians. Conclusions Distracting activity is common among pedestrians, even while crossing intersections. Technological and social distractions increase crossing times, with text messaging associated with the highest risk. Our findings suggest the need for intervention studies to reduce risk of pedestrian injury. PMID:23243104

Effect of etomidate versus thiopental on major depressive disorder in electroconvulsive therapy, a randomized double-blind controlled clinical trial.

PubMed

Abdollahi, Mohammad Hassan; Izadi, Amir; Hajiesmaeili, Mohammad Reza; Ghanizadeh, Ahmad; Dastjerdi, Ghasem; Hosseini, Habib Allah; Ghiamat, Mohammad Mehdi; Abbasi, Hamid Reza

2012-03-01

Although the therapeutic effect of electroconvulsive therapy (ECT) on major depressive disorder is widely investigated, there is a gap in literature regarding the possible effects of the medications used for induction of anesthesia in ECT. To the best of the authors' knowledge, this study is the first randomized double-blind clinical trial comparing the effect of etomidate and sodium thiopental on the depression symptoms in patients who have received ECT. The participants of this study are 60 adult patients with major depressive disorder who were referred for ECT. They were randomly allocated into 1 of the 2 groups. One group received etomidate, and the other group received sodium thiopental, as medication for induction of anesthesia. All the patients received bilateral ECT. The outcomes measures included the Beck Depression Inventory score, seizure duration, and recovery duration after induction of anesthesia. The sex ratio and mean age were not different between the 2 groups. Linear regression analysis showed that etomidate decreased the depression score more than did sodium thiopental. Seizure duration in all of the sessions in the etomidate group was significantly higher than that of sodium thiopental group. In conclusion, etomidate may improve major depressive disorder more than sodium thiopental in patients who are receiving ECT.

Differences in effectiveness of the active living every day program for older adults with arthritis.

PubMed

Sperber, Nina R; Allen, Kelli D; Devellis, Brenda M; Devellis, Robert F; Lewis, Megan A; Callahan, Leigh F

2013-10-01

The authors explored whether demographic and psychosocial variables predicted differences in physical activity for participants with arthritis in a trial of Active Living Every Day (ALED). Participants (N = 280) from 17 community sites were randomized into ALED or usual care. The authors assessed participant demographic characteristics, self-efficacy, outcome expectations, pain, fatigue, and depressive symptoms at baseline and physical activity frequency at 20-wk follow-up. They conducted linear regression with interaction terms (Baseline Characteristic × Randomization Group). Being female (p ≤ .05), less depressed (p ≤ .05), or younger (p ≤ .10) was associated with more frequent posttest physical activity for ALED participants than for those with usual care. Higher education was associated with more physical activity for both ALED and usual-care groups. ALED was particularly effective for female, younger, and less depressed participants. Further research should determine whether modifications could produce better outcomes in other subgroups.

Randomized, double-blind, placebo-controlled trial of fluoxetine treatment for elderly patients with dysthymic disorder.

PubMed

Devanand, D P; Nobler, Mitchell S; Cheng, Jocelyn; Turret, Nancy; Pelton, Gregory H; Roose, Steven P; Sackeim, Harold A

2005-01-01

The authors compared the efficacy and side effects of fluoxetine and placebo in elderly outpatients with dysthymic disorder. Patients were randomly assigned to fluoxetine (20 mg-60 mg/day) or placebo for 12 weeks in a double-blind trial. Of 90 randomized patients, 71 completed the trial. In the intent-to-treat sample, random regression analyses of the Hamilton Rating Scale for Depression (Ham-D; 24-item) and Cornell Dysthymia Rating Scale (CDRS) scores at each visit produced significant time x treatment group interactions favoring the fluoxetine group. Analysis of percentage change in Ham-D scores yielded no effect for treatment group, but a similar analysis of percentage change in CDRS scores yielded a main effect for treatment group, favoring fluoxetine over placebo. In the intent-to-treat sample, response rates were 27.3% for fluoxetine and 19.6% for placebo. In the completer sample, response rates were 37.5% for fluoxetine and 23.1% for placebo. Fluoxetine had limited efficacy in elderly dysthymic patients. The clinical features of elderly dysthymic patients are typically distinct from those of dysthymic disorder in young adults, and the findings suggest that treatments effective for young adult dysthymic patients may not be as useful in elderly dysthymic patients. Further research is needed to identify efficacious treatments for elderly patients with dysthymic disorder, and investigative tools such as electronic/computerized brain scans and neuropsychological testing may help identify the factors that moderate antidepressant treatment response and resistance.

Effect of Whey Supplementation on Circulating C-Reactive Protein: A Meta-Analysis of Randomized Controlled Trials

PubMed Central

Zhou, Ling-Mei; Xu, Jia-Ying; Rao, Chun-Ping; Han, Shufen; Wan, Zhongxiao; Qin, Li-Qiang

2015-01-01

Whey supplementation is beneficial for human health, possibly by reducing the circulating C-reactive protein (CRP) level, a sensitive marker of inflammation. Thus, a meta-analysis of randomized controlled trials was conducted to evaluate their relationship. A systematic literature search was conducted in July, 2014, to identify eligible studies. Either a fixed-effects model or a random-effects model was used to calculate pooled effects. The meta-analysis results of nine trials showed a slight, but no significant, reduction of 0.42 mg/L (95% CI −0.96, 0.13) in CRP level with the supplementation of whey protein and its derivates. Relatively high heterogeneity across studies was observed. Subgroup analyses showed that whey significantly lowered CRP by 0.72 mg/L (95% CI −0.97, −0.47) among trials with a daily whey dose ≥20 g/day and by 0.67 mg/L (95% CI −1.21, −0.14) among trials with baseline CRP ≥3 mg/L. Meta-regression analysis revealed that the baseline CRP level was a potential effect modifier of whey supplementation in reducing CRP. In conclusion, our meta-analysis did not find sufficient evidence that whey and its derivates elicited a beneficial effect in reducing circulating CRP. However, they may significantly reduce CRP among participants with highly supplemental doses or increased baseline CRP levels. PMID:25671415

Effects of self-management health information technology on glycaemic control for patients with diabetes: a meta-analysis of randomized controlled trials.

PubMed

Tao, Da; Or, Calvin Kl

2013-04-01

We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) which had evaluated self-management health information technology (SMHIT) for glycaemic control in patients with diabetes. A total of 43 RCTs was identified, which reported on 52 control-intervention comparisons. The glycosylated haemoglobin (HbA 1c ) data were pooled using a random effects meta-analysis method, followed by a meta-regression and subgroup analyses to examine the effects of a set of moderators. The meta-analysis showed that use of SMHITs was associated with a significant reduction in HbA 1c compared to usual care, with a pooled standardized mean difference of -0.30% (95% CI -0.39 to -0.21, P < 0.001). Sample size, age, study setting, type of application and method of data entry significantly moderated the effects of SMHIT use. The review supports the use of SMHITs as a self-management approach to improve glycaemic control. The effect of SMHIT use is significantly greater when the technology is a web-based application, when a mechanism for patients' health data entry is provided (manual or automatic) and when the technology is operated in the home or without location restrictions. Integrating these variables into the design of SMHITs may augment the effectiveness of the interventions. © SAGE Publications Ltd, 2013.

Comparative study of outcome measures and analysis methods for traumatic brain injury trials.

PubMed

Alali, Aziz S; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B; Temkin, Nancy R

2015-04-15

Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a "super" dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data.

Comparative Study of Outcome Measures and Analysis Methods for Traumatic Brain Injury Trials

PubMed Central

Alali, Aziz S.; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B.

2015-01-01

Abstract Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a “super” dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data. PMID:25317951

MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data.

PubMed

Yavorska, Olena O; Burgess, Stephen

2017-12-01

MendelianRandomization is a software package for the R open-source software environment that performs Mendelian randomization analyses using summarized data. The core functionality is to implement the inverse-variance weighted, MR-Egger and weighted median methods for multiple genetic variants. Several options are available to the user, such as the use of robust regression, fixed- or random-effects models and the penalization of weights for genetic variants with heterogeneous causal estimates. Extensions to these methods, such as allowing for variants to be correlated, can be chosen if appropriate. Graphical commands allow summarized data to be displayed in an interactive graph, or the plotting of causal estimates from multiple methods, for comparison. Although the main method of data entry is directly by the user, there is also an option for allowing summarized data to be incorporated from the PhenoScanner database of genotype-phenotype associations. We hope to develop this feature in future versions of the package. The R software environment is available for download from [https://www.r-project.org/]. The MendelianRandomization package can be downloaded from the Comprehensive R Archive Network (CRAN) within R, or directly from [https://cran.r-project.org/web/packages/MendelianRandomization/]. Both R and the MendelianRandomization package are released under GNU General Public Licenses (GPL-2|GPL-3). © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association.

Motivational Interviewing delivered by existing prison staff: a randomized controlled study of effectiveness on substance use after release.

PubMed

Forsberg, Lars Georg; Ernst, Denise; Sundqvist, Kristina; Farbring, Carl Åke

2011-01-01

A sample of 296 drug-using inmates in 14 Swedish prisons was randomized during 2004-2006 into three intervention groups; Motivational Interviewing delivered by counselors with workshop-only training, or by counselors with workshop training followed by peer group supervision, and controls. Drug and alcohol use was measured by the Addiction Severity Index (ASI) at intake and at 10 months after release. Complete data from 114 clients were analyzed by a stepwise regression analysis. All three groups reduced alcohol and drug use. Limitations in the study are discussed and future research is suggested. The study is financed by grants from the Research Committee of the National Prison and Probation Administration.

Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP.

PubMed

Lazar, Ann A; Bonetti, Marco; Cole, Bernard F; Yip, Wai-Ki; Gelber, Richard D

2016-04-01

Investigators conducting randomized clinical trials often explore treatment effect heterogeneity to assess whether treatment efficacy varies according to patient characteristics. Identifying heterogeneity is central to making informed personalized healthcare decisions. Treatment effect heterogeneity can be investigated using subpopulation treatment effect pattern plot (STEPP), a non-parametric graphical approach that constructs overlapping patient subpopulations with varying values of a characteristic. Procedures for statistical testing using subpopulation treatment effect pattern plot when the endpoint of interest is survival remain an area of active investigation. A STEPP analysis was used to explore patterns of absolute and relative treatment effects for varying levels of a breast cancer biomarker, Ki-67, in the phase III Breast International Group 1-98 randomized clinical trial, comparing letrozole to tamoxifen as adjuvant therapy for postmenopausal women with hormone receptor-positive breast cancer. Absolute treatment effects were measured by differences in 4-year cumulative incidence of breast cancer recurrence, while relative effects were measured by the subdistribution hazard ratio in the presence of competing risks using O-E (observed-minus-expected) methodology, an intuitive non-parametric method. While estimation of hazard ratio values based on O-E methodology has been shown, a similar development for the subdistribution hazard ratio has not. Furthermore, we observed that the subpopulation treatment effect pattern plot analysis may not produce results, even with 100 patients within each subpopulation. After further investigation through simulation studies, we observed inflation of the type I error rate of the traditional test statistic and sometimes singular variance-covariance matrix estimates that may lead to results not being produced. This is due to the lack of sufficient number of events within the subpopulations, which we refer to as instability of the subpopulation treatment effect pattern plot analysis. We introduce methodology designed to improve stability of the subpopulation treatment effect pattern plot analysis and generalize O-E methodology to the competing risks setting. Simulation studies were designed to assess the type I error rate of the tests for a variety of treatment effect measures, including subdistribution hazard ratio based on O-E estimation. This subpopulation treatment effect pattern plot methodology and standard regression modeling were used to evaluate heterogeneity of Ki-67 in the Breast International Group 1-98 randomized clinical trial. We introduce methodology that generalizes O-E methodology to the competing risks setting and that improves stability of the STEPP analysis by pre-specifying the number of events across subpopulations while controlling the type I error rate. The subpopulation treatment effect pattern plot analysis of the Breast International Group 1-98 randomized clinical trial showed that patients with high Ki-67 percentages may benefit most from letrozole, while heterogeneity was not detected using standard regression modeling. The STEPP methodology can be used to study complex patterns of treatment effect heterogeneity, as illustrated in the Breast International Group 1-98 randomized clinical trial. For the subpopulation treatment effect pattern plot analysis, we recommend a minimum of 20 events within each subpopulation. © The Author(s) 2015.

Simple to complex modeling of breathing volume using a motion sensor.

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

To compare simple and complex modeling techniques to estimate categories of low, medium, and high ventilation (VE) from ActiGraph™ activity counts. Vertical axis ActiGraph™ GT1M activity counts, oxygen consumption and VE were measured during treadmill walking and running, sports, household chores and labor-intensive employment activities. Categories of low (<19.3 l/min), medium (19.3 to 35.4 l/min) and high (>35.4 l/min) VEs were derived from activity intensity classifications (light <2.9 METs, moderate 3.0 to 5.9 METs and vigorous >6.0 METs). We examined the accuracy of two simple techniques (multiple regression and activity count cut-point analyses) and one complex (random forest technique) modeling technique in predicting VE from activity counts. Prediction accuracy of the complex random forest technique was marginally better than the simple multiple regression method. Both techniques accurately predicted VE categories almost 80% of the time. The multiple regression and random forest techniques were more accurate (85 to 88%) in predicting medium VE. Both techniques predicted the high VE (70 to 73%) with greater accuracy than low VE (57 to 60%). Actigraph™ cut-points for light, medium and high VEs were <1381, 1381 to 3660 and >3660 cpm. There were minor differences in prediction accuracy between the multiple regression and the random forest technique. This study provides methods to objectively estimate VE categories using activity monitors that can easily be deployed in the field. Objective estimates of VE should provide a better understanding of the dose-response relationship between internal exposure to pollutants and disease. Copyright © 2013 Elsevier B.V. All rights reserved.

In vitro chemo-sensitivity assay guided chemotherapy is associated with prolonged overall survival in cancer patients.

PubMed

Udelnow, Andrej; Schönfęlder, Manfred; Würl, Peter; Halloul, Zuhir; Meyer, Frank; Lippert, Hans; Mroczkowski, Paweł

2013-06-01

The overall survival (OS) of patients suffering From various tumour entities was correlated with the results of in vitro-chemosensitivity assay (CSA) of the in vivo applied drugs. Tumour specimen (n=611) were dissected in 514 patients and incubated for primary tumour cell culture. The histocytological regression assay was performed 5 days after adding chemotherapeutic substances to the cell cultures. n=329 patients undergoing chemotherapy were included in the in vitro/in vivo associations. OS was assessed and in vitro response groups compared using survival analysis. Furthermore Cox-regression analysis was performed on OS including CSA, age, TNM classification and treatment course. The growth rate of the primary was 73-96% depending on tumour entity. The in-vitro response rate varied with histology and drugs (e.g. 8-18% for methotrexate and 33-83% for epirubicine). OS was significantly prolonged for patients treated with in vitro effective drugs compared to empiric therapy (log-rank-test, p=0.0435). Cox-regression revealed that application of in vitro effective drugs, residual tumour and postoperative radiotherapy determined the death risk independently. When patients were treated with drugs effective in our CSA, OS was significantly prolonged compared to empiric therapy. CSA guided chemotherapy should be compared to empiric treatment by a prospective randomized trial.

Economic and Health Predictors of National Postpartum Depression Prevalence: A Systematic Review, Meta-analysis, and Meta-Regression of 291 Studies from 56 Countries.

PubMed

Hahn-Holbrook, Jennifer; Cornwell-Hinrichs, Taylor; Anaya, Itzel

2017-01-01

Postpartum depression (PPD) poses a major global public health challenge. PPD is the most common complication associated with childbirth and exerts harmful effects on children. Although hundreds of PPD studies have been published, we lack accurate global or national PPD prevalence estimates and have no clear account of why PPD appears to vary so dramatically between nations. Accordingly, we conducted a meta-analysis to estimate the global and national prevalence of PPD and a meta-regression to identify economic, health, social, or policy factors associated with national PPD prevalence. We conducted a systematic review of all papers reporting PPD prevalence using the Edinburgh Postnatal Depression Scale. PPD prevalence and methods were extracted from each study. Random effects meta-analysis was used to estimate global and national PPD prevalence. To test for country level predictors, we drew on data from UNICEF, WHO, and the World Bank. Random effects meta-regression was used to test national predictors of PPD prevalence. 291 studies of 296284 women from 56 countries were identified. The global pooled prevalence of PPD was 17.7% (95% confidence interval: 16.6-18.8%), with significant heterogeneity across nations ( Q  = 16,823, p  = 0.000, I 2  = 98%), ranging from 3% (2-5%) in Singapore to 38% (35-41%) in Chile. Nations with significantly higher rates of income inequality ( R 2  = 41%), maternal mortality ( R 2  = 19%), infant mortality ( R 2  = 16%), or women of childbearing age working ≥40 h a week ( R 2  = 31%) have higher rates of PPD. Together, these factors explain 73% of the national variation in PPD prevalence. The global prevalence of PPD is greater than previously thought and varies dramatically by nation. Disparities in wealth inequality and maternal-child-health factors explain much of the national variation in PPD prevalence.

Differences in liquor prices between control state-operated and license-state retail outlets in the United States.

PubMed

Siegel, Michael; DeJong, William; Albers, Alison B; Naimi, Timothy S; Jernigan, David H

2013-02-01

This study aims to compare the average price of liquor in the United States between retail alcohol outlets in states that have a monopoly ('control' states) with those that do not ('licence' states). A cross-sectional study of brand-specific alcohol prices in the United States. We determined the average prices in February 2012 of 74 brands of liquor among the 13 control states that maintain a monopoly on liquor sales at the retail level and among a sample of 50 license-state liquor stores, using their online-available prices. We calculated average prices for 74 brands of liquor by control versus license state. We used a random-effects regression model to estimate differences between control and license state prices-overall and by alcoholic beverage type. We also compared prices between the 13 control states. The overall mean price for the 74 brands was $27.79 in the license states [95% confidence interval (CI): $25.26-30.32] and $29.82 in the control states (95% CI: $26.98-32.66). Based on the random-effects linear regression model, the average liquor price was approximately $2 lower (6.9% lower) in license states. In the United States monopoly of alcohol retail outlets appears to be associated with slightly higher liquor prices. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

Possibility of modifying the growth trajectory in Raeini Cashmere goat.

PubMed

Ghiasi, Heydar; Mokhtari, M S

2018-03-27

The objective of this study was to investigate the possibility of modifying the growth trajectory in Raeini Cashmere goat breed. In total, 13,193 records on live body weight collected from 4788 Raeini Cashmere goats were used. According to Akanke's information criterion (AIC), the sing-trait random regression model included fourth-order Legendre polynomial for direct and maternal genetic effect; maternal and individual permanent environmental effect was the best model for estimating (co)variance components. The matrices of eigenvectors for (co)variances between random regression coefficients of direct additive genetic were used to calculate eigenfunctions, and different eigenvector indices were also constructed. The obtained results showed that the first eigenvalue explained 79.90% of total genetic variance. Therefore, changing the body weights applying the first eigenfunction will be obtained rapidly. Selection based on the first eigenvector will cause favorable positive genetic gains for all body weight considered from birth to 12 months of age. For modifying the growth trajectory in Raeini Cashmere goat, the selection should be based on the second eigenfunction. The second eigenvalue accounted for 14.41% of total genetic variance for body weights that is low in comparison with genetic variance explained by the first eigenvalue. The complex patterns of genetic change in growth trajectory observed under the third and fourth eigenfunction and low amount of genetic variance explained by the third and fourth eigenvalues.

Differences in liquor prices between control state-operated and license-state retail outlets in the U.S.

PubMed Central

Siegel, Michael; DeJong, William; Albers, Alison B.; Naimi, Timothy S.; Jernigan, David H.

2012-01-01

Aims This study aims to compare the average price of liquor in the United States between retail alcohol outlets in states that have a monopoly ('control' states) with those that do not ('licence' states). Design A cross-sectional study of brand-specific alcohol prices in the United States. Setting We determined the average prices in February 2012 of 74 brands of liquor among the 13 control states that maintain a monopoly on liquor sales at the retail level and among a sample of 50 license-state liquor stores, using their online-available prices. Measurements We calculated average prices for 74 brands of liquor by control vs. license state. We used a random effects regression model to estimate differences between control and license state prices – overall and by alcoholic beverage type. We also compared prices between the 13 control states. Findings The overall mean price for the 74 brands was $27.79 in the license states (95% confidence interval [CI], $25.26–$30.32) and $29.82 in the control states (95% CI, $26.98–$32.66). Based on the random effects linear regression model, the average liquor price was approximately two dollars lower (6.9% lower) in license states. Conclusions In the United States monopoly of alcohol retail outlets appears to be associated with slightly higher liquor prices. PMID:22934914

Measures of Residual Risk with Connections to Regression, Risk Tracking, Surrogate Models, and Ambiguity

DTIC Science & Technology

2015-01-07

vector that helps to manage , predict, and mitigate the risk in the original variable. Residual risk can be exemplified as a quantification of the improved... the random variable of interest is viewed in concert with a related random vector that helps to manage , predict, and mitigate the risk in the original...measures of risk. They view a random variable of interest in concert with an auxiliary random vector that helps to manage , predict and mitigate the risk

Fathers’ Participation in Parenting and Maternal Parenting Stress: Variation by Relationship Status

PubMed Central

Nomaguchi, Kei; Brown, Susan L.; Leyman, Tanya M.

2015-01-01

The growing diversity in mother-father relationship status has led to a debate over the role of fathers in parenting. Little is known, however, about how fathers’ participation in parenting is linked to maternal well-being across different mother-father relationship statuses. Using data from the Fragile Families and Child Wellbeing Study (N = 2,062), fixed-effects as well as random-effects regression models show that overall fathers’ engagement with children and sharing in child-related chores are negatively related to maternal parenting stress. Fathers’ cooperative coparenting is negatively related to maternal parenting stress only in the random-effects model, suggesting that the association is driven by selection factors. There is little variation in these associations by mother-father relationship status, once selection factors are controlled for. These findings extend support for the current cultural emphasis on benefits of fathers’ active participation in parenting for mothers and children even after the mother-father relationship dissolved. PMID:28479649

Motivational and mindfulness intervention for young adult female marijuana users

PubMed Central

de Dios, Marcel A.; Herman, Debra S.; Britton, Willoughby B.; Hagerty, Claire E.; Anderson, Bradley J.; Stein, Michael D.

2011-01-01

This pilot study tested the efficacy of a brief intervention using motivational interviewing (MI) plus mindfulness meditation (MM) to reduce marijuana use among young adult female. Thirty-four female marijuana users between the ages of 18–29 were randomized to either the intervention group (n = 22), consisting of 2 sessions of MI-MM or an assessment-only control group (n = 12). Participants’ marijuana use was assessed at baseline, 1, 2, and 3 months post-treatment. Fixed-effects regression modeling was used to analyze treatment effects. Participants randomized to the intervention group were found to use marijuana on 6.15 (z = −2.42, p=.015), 7.81 (z = −2.78, p=.005), and 6.83 (z = −2.23, p=.026) fewer days at months 1, 2, and 3, respectively, than controls. Findings from this pilot study provide preliminary evidence for the feasibility and effectiveness of a brief MI-MM for young adult female marijuana users. PMID:21940136

A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials

PubMed Central

Dai, James Y.; Hughes, James P.

2012-01-01

The meta-analytic approach to evaluating surrogate end points assesses the predictiveness of treatment effect on the surrogate toward treatment effect on the clinical end point based on multiple clinical trials. Definition and estimation of the correlation of treatment effects were developed in linear mixed models and later extended to binary or failure time outcomes on a case-by-case basis. In a general regression setting that covers nonnormal outcomes, we discuss in this paper several metrics that are useful in the meta-analytic evaluation of surrogacy. We propose a unified 3-step procedure to assess these metrics in settings with binary end points, time-to-event outcomes, or repeated measures. First, the joint distribution of estimated treatment effects is ascertained by an estimating equation approach; second, the restricted maximum likelihood method is used to estimate the means and the variance components of the random treatment effects; finally, confidence intervals are constructed by a parametric bootstrap procedure. The proposed method is evaluated by simulations and applications to 2 clinical trials. PMID:22394448

Solving a mixture of many random linear equations by tensor decomposition and alternating minimization.

DOT National Transportation Integrated Search

2016-09-01

We consider the problem of solving mixed random linear equations with k components. This is the noiseless setting of mixed linear regression. The goal is to estimate multiple linear models from mixed samples in the case where the labels (which sample...

Exploring unobserved heterogeneity in bicyclists' red-light running behaviors at different crossing facilities.

PubMed

Guo, Yanyong; Li, Zhibin; Wu, Yao; Xu, Chengcheng

2018-06-01

Bicyclists running the red light at crossing facilities increase the potential of colliding with motor vehicles. Exploring the contributing factors could improve the prediction of running red-light probability and develop countermeasures to reduce such behaviors. However, individuals could have unobserved heterogeneities in running a red light, which make the accurate prediction more challenging. Traditional models assume that factor parameters are fixed and cannot capture the varying impacts on red-light running behaviors. In this study, we employed the full Bayesian random parameters logistic regression approach to account for the unobserved heterogeneous effects. Two types of crossing facilities were considered which were the signalized intersection crosswalks and the road segment crosswalks. Electric and conventional bikes were distinguished in the modeling. Data were collected from 16 crosswalks in urban area of Nanjing, China. Factors such as individual characteristics, road geometric design, environmental features, and traffic variables were examined. Model comparison indicates that the full Bayesian random parameters logistic regression approach is statistically superior to the standard logistic regression model. More red-light runners are predicted at signalized intersection crosswalks than at road segment crosswalks. Factors affecting red-light running behaviors are gender, age, bike type, road width, presence of raised median, separation width, signal type, green ratio, bike and vehicle volume, and average vehicle speed. Factors associated with the unobserved heterogeneity are gender, bike type, signal type, separation width, and bike volume. Copyright © 2018 Elsevier Ltd. All rights reserved.

Topical Application of Arnica and Mucopolysaccharide Polysulfate Attenuates Periorbital Edema and Ecchymosis in Open Rhinoplasty: A Randomized Controlled Clinical Study.

PubMed

Simsek, Gokce; Sari, Elif; Kilic, Rahmi; Bayar Muluk, Nuray

2016-03-01

The purpose of this study was to investigate the effects of local arnica and mucopolysaccharide polysulfate treatment on the regression of postoperative edema and ecchymosis in patients who have undergone open technique rhinoplasty. One hundred eight patients were included in the study. Participants were randomized into three groups, all of whom had undergone rhinoplasty. Group 1 (n = 36) received postoperative arnica cream treatment, and group 2 (n = 36) received postoperative mucopolysaccharide polysulfate cream treatment. Group 3 (n = 36, control group) consisted of patients who received no postoperative local treatments. Patients were evaluated for 24 hours on days 2, 5, 7, and 10 after the operation. For the evaluation of postoperative edema and ecchymosis, a scale ranging from 0 to 4 was used, and the groups were compared. In groups 1 and 2, postoperative ecchymosis was significantly less than in the control group during postoperative days 1, 5, and 7 (p < 0.005). The regression of the edema was also more rapid in groups 1 and 2 than in the control group during evaluations on postoperative days 1, 5, and 7 (p < 0.005). Neither edema nor ecchymosis was significantly different between groups 1 and 2 (p > 0.005). The authors' results suggest that a rapid regression of edema and ecchymosis may be achieved by local treatments of arnica and mucopolysaccharide polysulfate cream. In addition, there are no significant differences between these two treatment regimens. Therapeutic, II.

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

Predicting athletic success motivation using mental skin and emotional intelligence and its components in male athletes.

PubMed

Kajbafnezhad, H; Ahadi, H; Heidarie, A; Askari, P; Enayati, M

2012-10-01

The aim of this study was to predict athletic success motivation by mental skills, emotional intelligence and its components. The research sample consisted of 153 male athletes who were selected through random multistage sampling. The subjects completed the Mental Skills Questionnaire, Bar-On Emotional Intelligence questionnaire and the perception of sport success questionnaire. Data were analyzed using Pearson correlation coefficient and multiple regressions. Regression analysis shows that between the two variables of mental skill and emotional intelligence, mental skill is the best predictor for athletic success motivation and has a better ability to predict the success rate of the participants. Regression analysis results showed that among all the components of emotional intelligence, self-respect had a significantly higher ability to predict athletic success motivation. The use of psychological skills and emotional intelligence as an mediating and regulating factor and organizer cause leads to improved performance and can not only can to help athletes in making suitable and effective decisions for reaching a desired goal.

Association between social contact frequency and negative symptoms, psychosocial functioning and quality of life in patients with schizophrenia.

PubMed

Siegrist, Karin; Millier, Aurelie; Amri, Ikbal; Aballéa, Samuel; Toumi, Mondher

2015-12-30

The lack of social contacts may be an important element in the presumed vicious circle aggravating, or at least stabilising negative symptoms in patients with schizophrenia. A European 2-year cohort study collected negative symptom scores, psychosocial functioning scores, objective social contact frequency scores and quality of life scores every 6 months. Bivariate analyses, correlation analyses, multivariate regressions and random effects regressions were conducted to describe relations between social contact and outcomes of interest and to gain a better understanding of this relation over time. Using data from 1208 patients with schizophrenia, a link between social contact frequency and negative symptom scores, functioning and quality of life at baseline was established. Regression models confirmed the significant association between social contact and negative symptoms as well as psychosocial functioning. This study aimed at demonstrating the importance of social contact for deficient behavioural aspects of schizophrenia. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Dynamic prediction in functional concurrent regression with an application to child growth.

PubMed

Leroux, Andrew; Xiao, Luo; Crainiceanu, Ciprian; Checkley, William

2018-04-15

In many studies, it is of interest to predict the future trajectory of subjects based on their historical data, referred to as dynamic prediction. Mixed effects models have traditionally been used for dynamic prediction. However, the commonly used random intercept and slope model is often not sufficiently flexible for modeling subject-specific trajectories. In addition, there may be useful exposures/predictors of interest that are measured concurrently with the outcome, complicating dynamic prediction. To address these problems, we propose a dynamic functional concurrent regression model to handle the case where both the functional response and the functional predictors are irregularly measured. Currently, such a model cannot be fit by existing software. We apply the model to dynamically predict children's length conditional on prior length, weight, and baseline covariates. Inference on model parameters and subject-specific trajectories is conducted using the mixed effects representation of the proposed model. An extensive simulation study shows that the dynamic functional regression model provides more accurate estimation and inference than existing methods. Methods are supported by fast, flexible, open source software that uses heavily tested smoothing techniques. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

A comparative study of entrepreneurs and managers: stress, burnout, locus of control, and social support.

PubMed

Rahim, M A

1995-01-01

This study investigated the relationships of job stress to job burnout, of locus of control and social support to stress and burnout, and the moderating effects of locus of control and social support on the stress-burnout relationship. These relationships were tested with questionnaire data collected from a random sample of entrepreneurs (N = 238) and managers (N = 288). Results showed that entrepreneurs reported that they had higher internal locus of control, received less social support, and had less job burnout than managers. Hierarchical regression analyses found support for eight of the ten main effects and one of the four moderating effects.

Threshold regression to accommodate a censored covariate.

PubMed

Qian, Jing; Chiou, Sy Han; Maye, Jacqueline E; Atem, Folefac; Johnson, Keith A; Betensky, Rebecca A

2018-06-22

In several common study designs, regression modeling is complicated by the presence of censored covariates. Examples of such covariates include maternal age of onset of dementia that may be right censored in an Alzheimer's amyloid imaging study of healthy subjects, metabolite measurements that are subject to limit of detection censoring in a case-control study of cardiovascular disease, and progressive biomarkers whose baseline values are of interest, but are measured post-baseline in longitudinal neuropsychological studies of Alzheimer's disease. We propose threshold regression approaches for linear regression models with a covariate that is subject to random censoring. Threshold regression methods allow for immediate testing of the significance of the effect of a censored covariate. In addition, they provide for unbiased estimation of the regression coefficient of the censored covariate. We derive the asymptotic properties of the resulting estimators under mild regularity conditions. Simulations demonstrate that the proposed estimators have good finite-sample performance, and often offer improved efficiency over existing methods. We also derive a principled method for selection of the threshold. We illustrate the approach in application to an Alzheimer's disease study that investigated brain amyloid levels in older individuals, as measured through positron emission tomography scans, as a function of maternal age of dementia onset, with adjustment for other covariates. We have developed an R package, censCov, for implementation of our method, available at CRAN. © 2018, The International Biometric Society.

Evaluating disease management programme effectiveness: an introduction to the regression discontinuity design.

PubMed

Linden, Ariel; Adams, John L; Roberts, Nancy

2006-04-01

Although disease management (DM) has been in existence for over a decade, there is still much uncertainty as to its effectiveness in improving health status and reducing medical cost. The main reason is that most programme evaluations typically follow weak observational study designs that are subject to bias, most notably selection bias and regression to the mean. The regression discontinuity (RD) design may be the best alternative to randomized studies for evaluating DM programme effectiveness. The most crucial element of the RD design is its use of a 'cut-off' score on a pre-test measure to determine assignment to intervention or control. A valuable feature of this technique is that the pre-test measure does not have to be the same as the outcome measure, thus maximizing the programme's ability to use research-based practice guidelines, survey instruments and other tools to identify those individuals in greatest need of the programme intervention. Similarly, the cut-off score can be based on clinical understanding of the disease process, empirically derived, or resource-based. In the RD design, programme effectiveness is determined by a change in the pre-post relationship at the cut-off point. While the RD design is uniquely suitable for DM programme evaluation, its success will depend, in large part, on fundamental changes being made in the way DM programmes identify and assign individuals to the programme intervention.

A randomized controlled trial on the benefits and respiratory adverse effects of morphine for refractory dyspnea in patients with COPD: Protocol of the MORDYC study.

PubMed

Verberkt, C A; van den Beuken-van Everdingen, M H J; Franssen, F M E; Dirksen, C D; Schols, J M G A; Wouters, E F M; Janssen, D J A

2016-03-01

Dyspnea is one of the most reported symptoms of patients with advanced Chronic Obstructive Pulmonary Disease (COPD) and is often undertreated. Morphine has proven to be an effective treatment for dyspnea and is recommended in clinical practice guidelines, but questions concerning benefits and respiratory adverse effects remain. This study primarily evaluates the impact of oral sustained release morphine (morphine SR) on health-related quality of life and respiratory adverse effects in patients with COPD. Secondary objectives include the impact on exercise capacity, the relationship between description and severity of dyspnea and the presence of a clinically relevant response to morphine, and cost-effectiveness. A single-center, randomized, double blind, placebo controlled intervention study will be performed in 124 patients with COPD who recently completed a comprehensive pulmonary rehabilitation program. Participants will receive 20-30 mg/24h morphine SR or placebo for four weeks. After the intervention, participants will be followed for twelve weeks. Outcomes include: the COPD Assessment Test, six minute walking test, Multidimensional Dyspnea Scale and a cost diary. Furthermore, lung function and arterial blood gasses will be measured. These measures will be assessed during a baseline and outcome assessment, two home visits, two phone calls, and three follow-up assessments. The intervention and control group will be compared using uni- and multivariate regression analysis and logistic regression analysis. Finally, an economic evaluation will be performed from a societal and healthcare perspective. The current manuscript describes the rationale and methods of this study and provides an outline of the possible strengths, weaknesses and clinical consequences. Copyright © 2016 Elsevier Inc. All rights reserved.

Value of Computed Tomographic Perfusion-Based Patient Selection for Intra-Arterial Acute Ischemic Stroke Treatment.

PubMed

Borst, Jordi; Berkhemer, Olvert A; Roos, Yvo B W E M; van Bavel, Ed; van Zwam, Wim H; van Oostenbrugge, Robert J; van Walderveen, Marianne A A; Lingsma, Hester F; van der Lugt, Aad; Dippel, Diederik W J; Yoo, Albert J; Marquering, Henk A; Majoie, Charles B L M

2015-12-01

The utility of computed tomographic perfusion (CTP)-based patient selection for intra-arterial treatment of acute ischemic stroke has not been proven in randomized trials and requires further study in a cohort that was not selected based on CTP. Our objective was to study the relationship between CTP-derived parameters and outcome and treatment effect in patients with acute ischemic stroke because of a proximal intracranial arterial occlusion. We included 175 patients who underwent CTP in the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in The Netherlands (MR CLEAN). Association of CTP-derived parameters (ischemic-core volume, penumbra volume, and percentage ischemic core) with outcome was estimated with multivariable ordinal logistic regression as an adjusted odds ratio for a shift in the direction of a better outcome on the modified Rankin Scale. Interaction between CTP-derived parameters and treatment effect was determined using multivariable ordinal logistic regression. Interaction with treatment effect was also tested for mismatch (core <70 mL; penumbra core >1.2; penumbra core >10 mL). The adjusted odds ratio for improved functional outcome for ischemic core, percentage ischemic core, and penumbra were 0.79 per 10 mL (95% confidence interval: 0.71-0.89; P<0.001), 0.82 per 10% (95% confidence interval: 0.66-0.90; P=0.002), and 0.97 per 10 mL (96% confidence interval: 0.92-1.01; P=0.15), respectively. No significant interaction between any of the CTP-derived parameters and treatment effect was observed. We observed no significant interaction between mismatch and treatment effect. CTP seems useful for predicting functional outcome, but cannot reliably identify patients who will not benefit from intra-arterial therapy. © 2015 American Heart Association, Inc.

Particulate air pollution and panel studies in children: a systematic review

PubMed Central

Ward, D; Ayres, J

2004-01-01

Aims: To systematically review the results of such studies in children, estimate summary measures of effect, and investigate potential sources of heterogeneity. Methods: Studies were identified by searching electronic databases to June 2002, including those where outcomes and particulate level measurements were made at least daily for ⩾8 weeks, and analysed using an appropriate regression model. Study results were compared using forest plots, and fixed and random effects summary effect estimates obtained. Publication bias was considered using a funnel plot. Results: Twenty two studies were identified, all except two reporting PM10 (24 hour mean) >50 µg.m-3. Reported effects of PM10 on PEF were widely spread and smaller than those for PM2.5 (fixed effects summary: -0.012 v -0.063 l.min-1 per µg.m-3 rise). A similar pattern was evident for symptoms. Random effects models produced larger estimates. Overall, in between-study comparisons, panels of children with diagnosed asthma or pre-existing respiratory symptoms appeared less affected by PM10 levels than those without, and effect estimates were larger where studies were conducted in higher ozone conditions. Larger PM10 effect estimates were obtained from studies using generalised estimating equations to model autocorrelation and where results were derived by pooling subject specific regression coefficients. A funnel plot of PM10 results for PEF was markedly asymmetrical. Conclusions: The majority of identified studies indicate an adverse effect of particulate air pollution that is greater for PM2.5 than PM10. However, results show considerable heterogeneity and there is evidence consistent with publication bias, so limited confidence may be placed on summary estimates of effect. The possibility of interaction between particle and ozone effects merits further investigation, as does variability due to analytical differences that alter the interpretation of final estimates. PMID:15031404

Vitamin D and calcium supplementation reduces cancer risk: results of a randomized trial.

PubMed

Lappe, Joan M; Travers-Gustafson, Dianne; Davies, K Michael; Recker, Robert R; Heaney, Robert P

2007-06-01

Numerous observational studies have found supplemental calcium and vitamin D to be associated with reduced risk of common cancers. However, interventional studies to test this effect are lacking. The purpose of this analysis was to determine the efficacy of calcium alone and calcium plus vitamin D in reducing incident cancer risk of all types. This was a 4-y, population-based, double-blind, randomized placebo-controlled trial. The primary outcome was fracture incidence, and the principal secondary outcome was cancer incidence. The subjects were 1179 community-dwelling women randomly selected from the population of healthy postmenopausal women aged >55 y in a 9-county rural area of Nebraska centered at latitude 41.4 degrees N. Subjects were randomly assigned to receive 1400-1500 mg supplemental calcium/d alone (Ca-only), supplemental calcium plus 1100 IU vitamin D3/d (Ca + D), or placebo. When analyzed by intention to treat, cancer incidence was lower in the Ca + D women than in the placebo control subjects (P < 0.03). With the use of logistic regression, the unadjusted relative risks (RR) of incident cancer in the Ca + D and Ca-only groups were 0.402 (P = 0.01) and 0.532 (P = 0.06), respectively. When analysis was confined to cancers diagnosed after the first 12 mo, RR for the Ca + D group fell to 0.232 (CI: 0.09, 0.60; P < 0.005) but did not change significantly for the Ca-only group. In multiple logistic regression models, both treatment and serum 25-hydroxyvitamin D concentrations were significant, independent predictors of cancer risk. Improving calcium and vitamin D nutritional status substantially reduces all-cancer risk in postmenopausal women. This trial was registered at clinicaltrials.gov as NCT00352170.

A behavioral intervention for war-affected youth in Sierra Leone: a randomized controlled trial.

PubMed

Betancourt, Theresa S; McBain, Ryan; Newnham, Elizabeth A; Akinsulure-Smith, Adeyinka M; Brennan, Robert T; Weisz, John R; Hansen, Nathan B

2014-12-01

Youth in war-affected regions are at risk for poor psychological, social, and educational outcomes. Effective interventions are needed to improve mental health, social behavior, and school functioning. This randomized controlled trial tested the effectiveness of a 10-session cognitive-behavioral therapy (CBT)-based group mental health intervention for multisymptomatic war-affected youth (aged 15-24 years) in Sierra Leone. War-affected youth identified by elevated distress and impairment via community screening were randomized (stratified by sex and age) to the Youth Readiness Intervention (YRI) (n = 222) or to a control condition (n = 214). After treatment, youth were again randomized and offered an education subsidy immediately (n = 220) or waitlisted (n = 216). Emotion regulation, psychological distress, prosocial attitudes/behaviors, social support, functional impairment, and posttraumatic stress disorder (PTSD) symptoms were assessed at pre- and postintervention and at 6-month follow-up. For youth in school, enrollment, attendance, and classroom performance were assessed after 8 months. Linear mixed-effects regressions evaluated outcomes. The YRI showed significant postintervention effects on emotion regulation, prosocial attitudes/behaviors, social support, and reduced functional impairment, and significant follow-up effects on school enrollment, school attendance, and classroom behavior. In contrast, education subsidy was associated with better attendance but had no effect on mental health or functioning, school retention, or classroom behavior. Interactions between education subsidy and YRI were not significant. YRI produced acute improvements in mental health and functioning as well as longer-term effects on school engagement and behavior, suggesting potential to prepare war-affected youth for educational and other opportunities. Clinical trial registration information-Trial of the Youth Readiness Intervention (YRI); http://clinicaltrials.gov; NCT01684488. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

A Behavioral Intervention for War-Affected Youth in Sierra Leone: A Randomized Controlled Trial

PubMed Central

Betancourt, Theresa S.; McBain, Ryan; Newnham, Elizabeth A.; Akinsulure-Smith, Adeyinka M.; Brennan, Robert T.; Weisz, John R.; Hansen, Nathan B.

2016-01-01

Objective Youth in war-affected regions are at risk for poor psychological, social, and educational outcomes. Effective interventions are needed to improve mental health, social behavior, and school functioning. This randomized controlled trial tested the effectiveness of a 10-session cognitive-behavioral therapy (CBT)–based group mental health intervention for multisymptomatic war-affected youth (aged 15–24 years) in Sierra Leone. Method War-affected youth identified by elevated distress and impairment via community screening were randomized (stratified by sex and age) to the Youth Readiness Intervention (YRI) (n = 222) or to a control condition (n = 214). After treatment, youth were again randomized and offered an education subsidy immediately (n = 220) or waitlisted (n = 216). Emotion regulation, psychological distress, prosocial attitudes/behaviors, social support, functional impairment, and posttraumatic stress disorder (PTSD) symptoms were assessed at pre- and postintervention and at 6-month follow-up. For youth in school, enrollment, attendance, and classroom performance were assessed after 8 months. Linear mixed-effects regressions evaluated outcomes. Results The YRI showed significant postintervention effects on emotion regulation, prosocial attitudes/behaviors, social support, and reduced functional impairment, and significant follow-up effects on school enrollment, school attendance, and classroom behavior. In contrast, education subsidy was associated with better attendance but had no effect on mental health or functioning, school retention, or classroom behavior. Interactions between education subsidy and YRI were not significant. Conclusion YRI produced acute improvements in mental health and functioning as well as longer-term effects on school engagement and behavior, suggesting potential to prepare war-affected youth for educational and other opportunities. Clinical trial registration information-Trial of the Youth Readiness Intervention (YRI); http://clinicaltrials.gov; NCT01684488. PMID:25457927

The effect of platelet-rich fibrin matrix on rotator cuff tendon healing: a prospective, randomized clinical study.

PubMed

Rodeo, Scott A; Delos, Demetris; Williams, Riley J; Adler, Ronald S; Pearle, Andrew; Warren, Russell F

2012-06-01

There is a strong need for methods to improve the biological potential of rotator cuff tendon healing. Platelet-rich fibrin matrix (PRFM) allows delivery of autologous cytokines to healing tissue, and limited evidence suggests a positive effect of platelet-rich plasma on tendon biology. To evaluate the effect of platelet-rich fibrin matrix on rotator cuff tendon healing. Randomized controlled trial; Level of evidence, 2. Seventy-nine patients undergoing arthroscopic rotator cuff tendon repair were randomized intraoperatively to either receive PRFM at the tendon-bone interface (n = 40) or standard repair with no PRFM (n = 39). Standardized repair techniques were used for all patients. The postoperative rehabilitation protocol was the same in both groups. The primary outcome was tendon healing evaluated by ultrasound (intact vs defect at repair site) at 6 and 12 weeks. Power Doppler ultrasound was also used to evaluate vascularity in the peribursal, peritendinous, and musculotendinous and insertion site areas of the tendon and bone anchor site. Secondary outcomes included standardized shoulder outcome scales (American Shoulder and Elbow Surgeons [ASES] and L'Insalata) and strength measurements using a handheld dynamometer. Patients and the evaluator were blinded to treatment group. All patients were evaluated at minimum 1-year follow-up. A logistic regression model was used to predict outcome (healed vs defect) based on tear severity, repair type, treatment type (PRFM or control), and platelet count. Overall, there were no differences in tendon-to-bone healing between the PRFM and control groups. Complete tendon-to-bone healing (intact repair) was found in 24 of 36 (67%) in the PRFM group and 25 of 31 (81%) in the control group (P = .20). There were no significant differences in healing by ultrasound between 6 and 12 weeks. There were gradual increases in ASES and L'Insalata scores over time in both groups, but there were no differences in scores between the groups. We also found no difference in vascularity in the peribursal, peritendinous, and musculotendinous areas of the tendon between groups. There were no differences in strength between groups. Platelet count had no effect on healing. Logistic regression analysis demonstrated that PRFM was a significant predictor (P = .037) for a tendon defect at 12 weeks, with an odds ratio of 5.8. Platelet-rich fibrin matrix applied to the tendon-bone interface at the time of rotator cuff repair had no demonstrable effect on tendon healing, tendon vascularity, manual muscle strength, or clinical rating scales. In fact, the regression analysis suggests that PRFM may have a negative effect on healing. Further study is required to evaluate the role of PRFM in rotator cuff repair.

Renin-angiotensin system inhibitors prevent the recurrence of atrial fibrillation: a meta-analysis of randomized controlled trials.

PubMed

Han, Min; Zhang, Yong; Sun, Shujuan; Wang, Zhongsu; Wang, Jiangrong; Xie, Xinxing; Gao, Mei; Yin, Xiangcui; Hou, Yinglong

2013-10-01

This study was designed to assess whether angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) could prevent the recurrence of atrial fibrillation (AF). A systemic literature search of PubMed, EMBASE, and Cochrane Controlled Trials Register till 2012 was performed to identify randomized controlled trials involving the prevention of recurrence of AF with renin-angiotensin system blockade therapy. Subgroup analysis and meta-regression were performed. Publication bias was checked through funnel plot and Egger's test. Twenty-one randomized controlled trials including 13,184 patients with AF were identified. Overall, the recurrence of AF was significantly reduced in patients using ACEI/ARBs [odds ratio (OR), 0.43; 95% confidence interval (CI), 0.32-0.56; P < 0.00001], especially both in irbesartan subgroup (OR, 0.38; 95% CI, 0.21-0.68; P = 0.001) and in patients receiving antiarrhythmic drug (AAD) (OR, 0.37; 95% CI, 0.29-0.48; P < 0.00001), and there was no significant difference between ACEIs and ARBs (ACEIs: OR, 0.42; 95% CI, 0.31-0.57 and ARBs: OR, 0.42; 95% CI, 0.31-0.57). Moreover, it was found that the benefits of ACEI/ARBs revealed positive correlation to systolic blood pressure (regression coefficient: -0.0700257, P = 0.000) in no-AAD users. ACEI/ARBs are effective on the secondary prevention of AF, especially in patients receiving AAD and suffering from hypertension.

Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases.

PubMed

Masías, Víctor Hugo; Valle, Mauricio; Morselli, Carlo; Crespo, Fernando; Vargas, Augusto; Laengle, Sigifredo

2016-01-01

Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers-Logistic Regression, Naïve Bayes and Random Forest-with a range of social network measures and the necessary databases to model the verdicts in two real-world cases: the U.S. Watergate Conspiracy of the 1970's and the now-defunct Canada-based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures.

Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research

ERIC Educational Resources Information Center

He, Lingjun; Levine, Richard A.; Fan, Juanjuan; Beemer, Joshua; Stronach, Jeanne

2018-01-01

In institutional research, modern data mining approaches are seldom considered to address predictive analytics problems. The goal of this paper is to highlight the advantages of tree-based machine learning algorithms over classic (logistic) regression methods for data-informed decision making in higher education problems, and stress the success of…

Replicating Experimental Impact Estimates Using a Regression Discontinuity Approach. NCEE 2012-4025

ERIC Educational Resources Information Center

Gleason, Philip M.; Resch, Alexandra M.; Berk, Jillian A.

2012-01-01

This NCEE Technical Methods Paper compares the estimated impacts of an educational intervention using experimental and regression discontinuity (RD) study designs. The analysis used data from two large-scale randomized controlled trials--the Education Technology Evaluation and the Teach for America Study--to provide evidence on the performance of…

On the null distribution of Bayes factors in linear regression

USDA-ARS?s Scientific Manuscript database

We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...

Development of a hybrid proximal sensing method for rapid identification of petroleum contaminated soils.

PubMed

Chakraborty, Somsubhra; Weindorf, David C; Li, Bin; Ali Aldabaa, Abdalsamad Abdalsatar; Ghosh, Rakesh Kumar; Paul, Sathi; Nasim Ali, Md

2015-05-01

Using 108 petroleum contaminated soil samples, this pilot study proposed a new analytical approach of combining visible near-infrared diffuse reflectance spectroscopy (VisNIR DRS) and portable X-ray fluorescence spectrometry (PXRF) for rapid and improved quantification of soil petroleum contamination. Results indicated that an advanced fused model where VisNIR DRS spectra-based penalized spline regression (PSR) was used to predict total petroleum hydrocarbon followed by PXRF elemental data-based random forest regression was used to model the PSR residuals, it outperformed (R(2)=0.78, residual prediction deviation (RPD)=2.19) all other models tested, even producing better generalization than using VisNIR DRS alone (RPD's of 1.64, 1.86, and 1.96 for random forest, penalized spline regression, and partial least squares regression, respectively). Additionally, unsupervised principal component analysis using the PXRF+VisNIR DRS system qualitatively separated contaminated soils from control samples. Fusion of PXRF elemental data and VisNIR derivative spectra produced an optimized model for total petroleum hydrocarbon quantification in soils. Copyright © 2015 Elsevier B.V. All rights reserved.

Ethnic density and depressive symptoms among African Americans: threshold and differential effects across social and demographic subgroups.

PubMed

Bécares, Laia; Nazroo, James; Jackson, James

2014-12-01

We examined the association between Black ethnic density and depressive symptoms among African Americans. We sought to ascertain whether a threshold exists in the association between Black ethnic density and an important mental health outcome, and to identify differential effects of this association across social, economic, and demographic subpopulations. We analyzed the African American sample (n = 3570) from the National Survey of American Life, which we geocoded to the 2000 US Census. We determined the threshold with a multivariable regression spline model. We examined differential effects of ethnic density with random-effects multilevel linear regressions stratified by sociodemographic characteristics. The protective association between Black ethnic density and depressive symptoms changed direction, becoming a detrimental effect, when ethnic density reached 85%. Black ethnic density was protective for lower socioeconomic positions and detrimental for the better-off categories. The masking effects of area deprivation were stronger in the highest levels of Black ethnic density. Addressing racism, racial discrimination, economic deprivation, and poor services-the main drivers differentiating ethnic density from residential segregation-will help to ensure that the racial/ethnic composition of a neighborhood is not a risk factor for poor mental health.

Ethnic Density and Depressive Symptoms Among African Americans: Threshold and Differential Effects Across Social and Demographic Subgroups

PubMed Central

Nazroo, James; Jackson, James

2014-01-01

Objectives. We examined the association between Black ethnic density and depressive symptoms among African Americans. We sought to ascertain whether a threshold exists in the association between Black ethnic density and an important mental health outcome, and to identify differential effects of this association across social, economic, and demographic subpopulations. Methods. We analyzed the African American sample (n = 3570) from the National Survey of American Life, which we geocoded to the 2000 US Census. We determined the threshold with a multivariable regression spline model. We examined differential effects of ethnic density with random-effects multilevel linear regressions stratified by sociodemographic characteristics. Results. The protective association between Black ethnic density and depressive symptoms changed direction, becoming a detrimental effect, when ethnic density reached 85%. Black ethnic density was protective for lower socioeconomic positions and detrimental for the better-off categories. The masking effects of area deprivation were stronger in the highest levels of Black ethnic density. Conclusions. Addressing racism, racial discrimination, economic deprivation, and poor services—the main drivers differentiating ethnic density from residential segregation—will help to ensure that the racial/ethnic composition of a neighborhood is not a risk factor for poor mental health. PMID:25322307

Association of Sulindac and Erlotinib vs Placebo With Colorectal Neoplasia in Familial Adenomatous Polyposis: Secondary Analysis of a Randomized Clinical Trial.

PubMed

Samadder, N Jewel; Kuwada, Scott K; Boucher, Kenneth M; Byrne, Kathryn; Kanth, Priyanka; Samowitz, Wade; Jones, David; Tavtigian, Sean V; Westover, Michelle; Berry, Therese; Jasperson, Kory; Pappas, Lisa; Smith, Laurel; Sample, Danielle; Burt, Randall W; Neklason, Deborah W

2018-02-08

Patients with familial adenomatous polyposis (FAP) are at markedly increased risk for colorectal polyps and cancer. A combination of sulindac and erlotinib led to a 71% reduction in duodenal polyp burden in a phase 2 trial. To evaluate effect of sulindac and erlotinib on colorectal adenoma regression in patients with FAP. Prespecified secondary analysis for colorectal adenoma regression was carried out using data from a double-blind, randomized, placebo-controlled trial, enrolling 92 patients with FAP, conducted from July 2010 to June 2014 in Salt Lake City, Utah. Patients were randomized to sulindac, 150 mg twice daily, and erlotinib, 75 mg daily (n = 46), vs placebo (n = 46) for 6 months. The total number of polyps in the intact colorectum, ileal pouch anal anastomosis, or ileo-rectum were recorded at baseline and 6 months. The primary outcomes were change in total colorectal polyp count and percentage change in colorectal polyps, following 6 months of treatment. Eighty-two randomized patients (mean [SD] age, 40 [13] years; 49 [60%] women) had colorectal polyp count data available for this secondary analysis: 22 with intact colon, 44 with ileal pouch anal anastomosis and 16 with ileo-rectal anastomosis; 41 patients received sulindac/erlotinib and 41 placebo. The total colorectal polyp count was significantly different between the placebo and sulindac-erlotinib group at 6 months in patients with net percentage change of 69.4% in those with an intact colorectum compared with placebo (95% CI, 28.8%-109.2%; P = .009). In this double-blind, placebo-controlled, randomized trial we showed that combination treatment with sulindac and erlotinib compared with placebo resulted in significantly lower colorectal polyp burden after 6 months of treatment. There was a reduction in polyp burden in both those with an entire colorectum and those with only a rectal pouch or rectum. clinicaltrials.gov Identifier: NCT01187901.

Comparison of modeling methods to predict the spatial distribution of deep-sea coral and sponge in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Rooper, Christopher N.; Zimmermann, Mark; Prescott, Megan M.

2017-08-01

Deep-sea coral and sponge ecosystems are widespread throughout most of Alaska's marine waters, and are associated with many different species of fishes and invertebrates. These ecosystems are vulnerable to the effects of commercial fishing activities and climate change. We compared four commonly used species distribution models (general linear models, generalized additive models, boosted regression trees and random forest models) and an ensemble model to predict the presence or absence and abundance of six groups of benthic invertebrate taxa in the Gulf of Alaska. All four model types performed adequately on training data for predicting presence and absence, with regression forest models having the best overall performance measured by the area under the receiver-operating-curve (AUC). The models also performed well on the test data for presence and absence with average AUCs ranging from 0.66 to 0.82. For the test data, ensemble models performed the best. For abundance data, there was an obvious demarcation in performance between the two regression-based methods (general linear models and generalized additive models), and the tree-based models. The boosted regression tree and random forest models out-performed the other models by a wide margin on both the training and testing data. However, there was a significant drop-off in performance for all models of invertebrate abundance ( 50%) when moving from the training data to the testing data. Ensemble model performance was between the tree-based and regression-based methods. The maps of predictions from the models for both presence and abundance agreed very well across model types, with an increase in variability in predictions for the abundance data. We conclude that where data conforms well to the modeled distribution (such as the presence-absence data and binomial distribution in this study), the four types of models will provide similar results, although the regression-type models may be more consistent with biological theory. For data with highly zero-inflated distributions and non-normal distributions such as the abundance data from this study, the tree-based methods performed better. Ensemble models that averaged predictions across the four model types, performed better than the GLM or GAM models but slightly poorer than the tree-based methods, suggesting ensemble models might be more robust to overfitting than tree methods, while mitigating some of the disadvantages in predictive performance of regression methods.

Metformin for the prevention of hypertensive disorders of pregnancy in women with gestational diabetes and obesity: a systematic review and meta-analysis.

PubMed

Kalafat, Erkan; Sukur, Yavuz Emre; Abdi, Abdulkadir; Thilaganathan, Basky; Khalil, Asma

2018-05-10

Metformin has been reported to reduce the risk of preeclampsia. It is also known to influence soluble fms-like tyrosine kinase-1 (sFlt-1) levels, which correlate significantly with the gestation of onset and severity of preeclampsia. The main aim of this systematic review and meta-analysis was to determine whether metformin use is associated with the incidence of hypertensive disorders of pregnancy (HDP). MEDLINE (1947 - September 2017), Scopus (1970 - September 2017) and the Cochrane Library (since inception - September 2017) were searched for relevant citations in English language. Randomized controlled trials on metformin use, reporting the incidence of preeclampsia or pregnancy induced hypertension were included. Studies on populations with a high probability of metformin use prior to randomization (type II diabetes or polycystic ovary syndrome) were excluded. Random-effects models with Mantel-Haenszel were used for subgroup analyses. Moreover, a Bayesian random-effects meta-regression was used to synthesize the evidence. In total, 3337 citations matched the search criteria. After evaluating the abstracts and full text review, 15 studies were included in the review. Metformin use was associated with a reduced risk of pregnancy induced hypertension when compared to insulin (RR: 0.56, 95% CI: 0.37-0.85, I^ 2 =0, 1260 women) and a non-significantly reduced risk of preeclampsia (RR: 0.83, 95% CI: 0.60-1.14, I^ 2 =0%, 1724 women). When compared to placebo, metformin use was associated with a non-significant reduction of preeclampsia (RR: 0.74, 95% CI: 0.09-6.28, I^ 2 =86%, 840 women). Metformin use was also associated with a non-significant reduction of any HDP (RR: 0.71, 95% CI: 0.41-1.25, I^ 2 =0, 556 women) when compared to glyburide. When studies were combined with Bayesian random-effects meta-regression using treatment type as a covariate, the posterior probabilities of metformin having a beneficial effect for the prevention of preeclampsia, pregnancy induced hypertension and any HDP were 92.7%, 92.8% and 99.2%, respectively when compared to any other treatment or placebo. There is a high probability that metformin use is associated with a reduced HDP incidence when compared to other treatments and placebo. The small number of studies included in the analysis, the low quality of evidence and the clinical heterogeneity preclude the generalization of these results to broader populations. Given the clinical importance of this topic and the magnitude of effect observed in this meta-analysis, further prospective trials are urgently needed. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods

NASA Astrophysics Data System (ADS)

Erener, Arzu; Sivas, A. Abdullah; Selcuk-Kestel, A. Sevtap; Düzgün, H. Sebnem

2017-07-01

All of the quantitative landslide susceptibility mapping (QLSM) methods requires two basic data types, namely, landslide inventory and factors that influence landslide occurrence (landslide influencing factors, LIF). Depending on type of landslides, nature of triggers and LIF, accuracy of the QLSM methods differs. Moreover, how to balance the number of 0 (nonoccurrence) and 1 (occurrence) in the training set obtained from the landslide inventory and how to select which one of the 1's and 0's to be included in QLSM models play critical role in the accuracy of the QLSM. Although performance of various QLSM methods is largely investigated in the literature, the challenge of training set construction is not adequately investigated for the QLSM methods. In order to tackle this challenge, in this study three different training set selection strategies along with the original data set is used for testing the performance of three different regression methods namely Logistic Regression (LR), Bayesian Logistic Regression (BLR) and Fuzzy Logistic Regression (FLR). The first sampling strategy is proportional random sampling (PRS), which takes into account a weighted selection of landslide occurrences in the sample set. The second method, namely non-selective nearby sampling (NNS), includes randomly selected sites and their surrounding neighboring points at certain preselected distances to include the impact of clustering. Selective nearby sampling (SNS) is the third method, which concentrates on the group of 1's and their surrounding neighborhood. A randomly selected group of landslide sites and their neighborhood are considered in the analyses similar to NNS parameters. It is found that LR-PRS, FLR-PRS and BLR-Whole Data set-ups, with order, yield the best fits among the other alternatives. The results indicate that in QLSM based on regression models, avoidance of spatial correlation in the data set is critical for the model's performance.

Design of Probabilistic Random Forests with Applications to Anticancer Drug Sensitivity Prediction

PubMed Central

Rahman, Raziur; Haider, Saad; Ghosh, Souparno; Pal, Ranadip

2015-01-01

Random forests consisting of an ensemble of regression trees with equal weights are frequently used for design of predictive models. In this article, we consider an extension of the methodology by representing the regression trees in the form of probabilistic trees and analyzing the nature of heteroscedasticity. The probabilistic tree representation allows for analytical computation of confidence intervals (CIs), and the tree weight optimization is expected to provide stricter CIs with comparable performance in mean error. We approached the ensemble of probabilistic trees’ prediction from the perspectives of a mixture distribution and as a weighted sum of correlated random variables. We applied our methodology to the drug sensitivity prediction problem on synthetic and cancer cell line encyclopedia dataset and illustrated that tree weights can be selected to reduce the average length of the CI without increase in mean error. PMID:27081304

Olfactory-visual congruence effects stable across ages: yellow is warmer when it is pleasantly lemony.

PubMed

Guerdoux, Estelle; Trouillet, Raphaël; Brouillet, Denis

2014-07-01

This study aimed to examine the age-related differences in the olfactory-visual cross-correspondences and the extent to which they are moderated by the odors pleasantness. Sixty participants aged from 20- to 75- years (young, middle-aged and older adults) performed a priming task to explore the influence of six olfactory primes (lemon, orange, rose, thyme, mint and fish) on the categorization (cool vs. warm) of six subsequent color targets (yellow, orange, pink, malachite green, grass-green, and blue-gray). We tested mixed effects models. Response times were regressed on covariates models using both fixed effects (Groups of age, olfactory Pleasantness and multimodal Condition) and cross-random effects (Subject, Color and Odor). The random effects coding for Odor (p < .001) and Color (p = .001) were significant. There was a significant interaction effect ( p= .004) between Condition × Pleasantness, but not with Groups of age. The compatibility effect (i.e., when odors and colors were congruent, the targets processing were facilitated) was as much enhanced as the olfactory primes were pleasant. Cross-correspondences between olfaction and vision may be robust in aging. They should be considered alongside spatiotemporal but also emotional congruency.

Moderators of fluoxetine treatment response for children and adolescents with comorbid depression and substance use disorders.

PubMed

Hirschtritt, Matthew E; Pagano, Maria E; Christian, Kelly M; McNamara, Nora K; Stansbrey, Robert J; Lingler, Jacqui; Faber, Jon E; Demeter, Christine A; Bedoya, Denise; Findling, Robert L

2012-06-01

Our recent 8-week, randomized, placebo-controlled trial of fluoxetine in adolescents (ages 12-17 years) with comorbid depression and substance use disorder (SUD) did not detect a significant antidepressant treatment effect. The purpose of this secondary analysis was to explore moderators of the effect of fluoxetine in this sample. Static moderators measured at baseline were depression chronicity and hopelessness severity; time-varying moderators measured at baseline and weekly during the 8-week trial period were alcohol and marijuana use severity. Treatment effects on depression outcomes were examined among moderating subgroups in random effects regression models. Subjects assigned to fluoxetine treatment with chronic depression at baseline (p = .04) or no more than moderate alcohol use during the trial (p = .04) showed significantly greater decline in depression symptoms in comparison to placebo-assigned subgroups. The current analysis suggests that youth with chronic depression and no more than moderate alcohol consumption are likely to respond better to treatment with fluoxetine compared with placebo than youth with transient depression and heavy alcohol use. Copyright © 2012 Elsevier Inc. All rights reserved.

Moderators of fluoxetine treatment response for children and adolescents with comorbid depression and substance use disorders

PubMed Central

Hirschtritt, Matthew E.; Pagano, Maria E.; Christian, Kelly M.; McNamara, Nora K.; Stansbrey, Robert J.; Lingler, Jacqui; Faber, Jon E.; Demeter, Christine A.; Bedoya, Denise; Findling, Robert L.

2012-01-01

Our recent 8-week, randomized, placebo-controlled trial of fluoxetine in adolescents (ages 12–17 years) with comorbid depression and substance use disorder (SUD) did not detect a significant antidepressant treatment effect. The purpose of this secondary analysis was to explore moderators of the effect of fluoxetine in this sample. Static moderators measured at baseline were depression chronicity and hopelessness severity; time-varying moderators measured at baseline and weekly during the 8-week trial period were alcohol and marijuana use severity. Treatment effects on depression outcomes were examined among moderating subgroups in random effects regression models. Subjects assigned to fluoxetine treatment with chronic depression at baseline (p = .04) or no more than moderate alcohol use during the trial (p = .04) showed significantly greater decline in depression symptoms in comparison to placebo-assigned subgroups. The current analysis suggests that youth with chronic depression and no more than moderate alcohol consumption are likely to respond better to treatment with fluoxetine compared with placebo than youth with transient depression and heavy alcohol use. PMID:22116008

Peer effects on risk behaviour: the importance of group identity.

PubMed

Gioia, Francesca

2017-01-01

This paper investigates whether and to what extent group identity plays a role in peer effects on risk behaviour. We run a laboratory experiment in which different levels of group identity are induced through different matching protocols (random or based on individual painting preferences) and the possibility to interact with group members via an online chat in a group task. Risk behaviour is measured by using the Bomb Risk Elicitation Task and peer influence is introduced by giving subjects feedback regarding group members' previous decisions. We find that subjects are affected by their peers when taking decisions and that group identity influences the magnitude of peer effects: painting preferences matching significantly reduces the heterogeneity in risk behaviour compared with random matching. On the other hand, introducing a group task has no significant effect on behaviour, possibly because interaction does not always contribute to enhancing group identity. Finally, relative riskiness within the group matters and individuals whose peers are riskier than they are take on average riskier decisions, even when controlling for regression to the mean.

Mixed models, linear dependency, and identification in age-period-cohort models.

PubMed

O'Brien, Robert M

2017-07-20

This paper examines the identification problem in age-period-cohort models that use either linear or categorically coded ages, periods, and cohorts or combinations of these parameterizations. These models are not identified using the traditional fixed effect regression model approach because of a linear dependency between the ages, periods, and cohorts. However, these models can be identified if the researcher introduces a single just identifying constraint on the model coefficients. The problem with such constraints is that the results can differ substantially depending on the constraint chosen. Somewhat surprisingly, age-period-cohort models that specify one or more of ages and/or periods and/or cohorts as random effects are identified. This is the case without introducing an additional constraint. I label this identification as statistical model identification and show how statistical model identification comes about in mixed models and why which effects are treated as fixed and which are treated as random can substantially change the estimates of the age, period, and cohort effects. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Forecasting Daily Patient Outflow From a Ward Having No Real-Time Clinical Data

PubMed Central

Tran, Truyen; Luo, Wei; Phung, Dinh; Venkatesh, Svetha

2016-01-01

Background: Modeling patient flow is crucial in understanding resource demand and prioritization. We study patient outflow from an open ward in an Australian hospital, where currently bed allocation is carried out by a manager relying on past experiences and looking at demand. Automatic methods that provide a reasonable estimate of total next-day discharges can aid in efficient bed management. The challenges in building such methods lie in dealing with large amounts of discharge noise introduced by the nonlinear nature of hospital procedures, and the nonavailability of real-time clinical information in wards. Objective Our study investigates different models to forecast the total number of next-day discharges from an open ward having no real-time clinical data. Methods We compared 5 popular regression algorithms to model total next-day discharges: (1) autoregressive integrated moving average (ARIMA), (2) the autoregressive moving average with exogenous variables (ARMAX), (3) k-nearest neighbor regression, (4) random forest regression, and (5) support vector regression. Although the autoregressive integrated moving average model relied on past 3-month discharges, nearest neighbor forecasting used median of similar discharges in the past in estimating next-day discharge. In addition, the ARMAX model used the day of the week and number of patients currently in ward as exogenous variables. For the random forest and support vector regression models, we designed a predictor set of 20 patient features and 88 ward-level features. Results Our data consisted of 12,141 patient visits over 1826 days. Forecasting quality was measured using mean forecast error, mean absolute error, symmetric mean absolute percentage error, and root mean square error. When compared with a moving average prediction model, all 5 models demonstrated superior performance with the random forests achieving 22.7% improvement in mean absolute error, for all days in the year 2014. Conclusions In the absence of clinical information, our study recommends using patient-level and ward-level data in predicting next-day discharges. Random forest and support vector regression models are able to use all available features from such data, resulting in superior performance over traditional autoregressive methods. An intelligent estimate of available beds in wards plays a crucial role in relieving access block in emergency departments. PMID:27444059

Effect of magnesium supplementation on lipid profile: a systematic review and meta-analysis of randomized controlled trials.

PubMed

Simental-Mendía, Luis E; Simental-Mendía, Mario; Sahebkar, Amirhossein; Rodríguez-Morán, Martha; Guerrero-Romero, Fernando

2017-05-01

We performed a meta-analysis of randomized controlled trials (RCTs) in order to evaluate the effect of oral magnesium supplementation on lipid profile of both diabetic and non-diabetic individuals. PubMed-Medline, SCOPUS, Web of Science, and Google Scholar databases were searched (from inception to February 23, 2016) to identify RCTs evaluating the effect of magnesium on lipid concentrations. A random-effects model and generic inverse variance method were used for quantitative data synthesis. Sensitivity analysis was conducted using the leave-one-out method. A weighted random-effects meta-regression was performed to evaluate the impact of potential confounders on lipid concentrations. Magnesium treatment was not found to significantly affect plasma concentrations of any of the lipid indices including total cholesterol (WMD 0.03 mmol/L, 95% CI -0.11, 0.16, p = 0.671), LDL-C (WMD -0.01 mmol/L, 95% CI -0.13, 0.11, p = 0.903), HDL-C (WMD 0.03 mmol/L, 95% CI -0.003, 0.06, p = 0.076), and triglycerides concentrations (WMD -0.10 mmol/L, 95% CI -0.25, 0.04, p = 0.149). In a subgroup analysis comparing studies with and without diabetes, no difference was observed between subgroups in terms of changes in plasma total cholesterol (p = 0.924), LDL-C (p = 0.161), HDL-C (p = 0.822), and triglyceride (p = 0.162) concentrations. Results of the present meta-analysis indicated that magnesium supplementation showed no significant effects on the lipid profile of either diabetic or non-diabetic individuals.

The causal effect of red blood cell folate on genome-wide methylation in cord blood: a Mendelian randomization approach.

PubMed

Binder, Alexandra M; Michels, Karin B

2013-12-04

Investigation of the biological mechanism by which folate acts to affect fetal development can inform appraisal of expected benefits and risk management. This research is ethically imperative given the ubiquity of folic acid fortified products in the US. Considering that folate is an essential component in the one-carbon metabolism pathway that provides methyl groups for DNA methylation, epigenetic modifications provide a putative molecular mechanism mediating the effect of folic acid supplementation on neonatal and pediatric outcomes. In this study we use a Mendelian Randomization Unnecessary approach to assess the effect of red blood cell (RBC) folate on genome-wide DNA methylation in cord blood. Site-specific CpG methylation within the proximal promoter regions of approximately 14,500 genes was analyzed using the Illumina Infinium Human Methylation27 Bead Chip for 50 infants from the Epigenetic Birth Cohort at Brigham and Women's Hospital in Boston. Using methylenetetrahydrofolate reductase genotype as the instrument, the Mendelian Randomization approach identified 7 CpG loci with a significant (mostly positive) association between RBC folate and methylation level. Among the genes in closest proximity to this significant subset of CpG loci, several enriched biologic processes were involved in nucleic acid transport and metabolic processing. Compared to the standard ordinary least squares regression method, our estimates were demonstrated to be more robust to unmeasured confounding. To the authors' knowledge, this is the largest genome-wide analysis of the effects of folate on methylation pattern, and the first to employ Mendelian Randomization to assess the effects of an exposure on epigenetic modifications. These results can help guide future analyses of the causal effects of periconceptional folate levels on candidate pathways.

Effect of Conjugated Linoleic Acid on Leptin Level: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

PubMed

Mohammadi-Sartang, Mohsen; Sohrabi, Zahra; Esmaeilinezhad, Zahra; Aqaeinezhad R, Seyed Mohammad; Jalilpiran, Yahya

2018-02-01

The results of human clinical trials examining the effects of conjugated linoleic acid (CLA) on leptin concentration are inconsistent. Our objective was to elucidate the role of conjugated linoleic acid supplementation on leptin through a systematic review and a meta-analysis of available randomized placebo-controlled trials (RCTs). We searched the PubMed, SCOPUS, and ISI web of science up to February2017, in English, to identify RCTs investigating the effect of CLA supplements on plasma leptin concentrations. Weighted mean differences (WMDs) and their respective 95% confidence intervals (CIs) were calculated to assess the efficacy of CLA on leptin concentration by using random effects. Statistical heterogeneity, study quality, meta-regression and publication bias were used based on standard methods. Nineteen RCTs (comprising 26 treatment arms) with 1045 subjects were included in this meta-analysis. Random-effect meta-analysis found a slight but not significant reduction in plasma leptin concentrations (WMD: -0.38 ng/ml, 95% CI: -1.08, 0.32, p=0.286); I 2 =53.24%, p=0.001), following CLA supplementation. The pooled effect size was robust and remained non-significant in the leave-one-out sensitivity analysis. Subgroup analysis based on BMI status showed that the CLA supplementation significantly reduces leptin when used for obese subjects (WMD: -1.47 ng/ml, 95% CI: -2.15, -0.79, p<0.001) and in the subset of trials lasting<24 weeks of duration (WMD: -0.76 ng/ml, 95% CI: -1.40, -0.12, p=0.019). CLA supplementation might moderately decrease circulatory leptin levels only among obese adults for shorter than 24 weeks. Additional high-quality studies are needed to replicate our results. © Georg Thieme Verlag KG Stuttgart · New York.

Omega-3 and -6 fatty acid supplementation and sensory processing in toddlers with ASD symptomology born preterm: A randomized controlled trial.

PubMed

Boone, Kelly M; Gracious, Barbara; Klebanoff, Mark A; Rogers, Lynette K; Rausch, Joseph; Coury, Daniel L; Keim, Sarah A

2017-12-01

Despite advances in the health and long-term survival of infants born preterm, they continue to face developmental challenges including higher risk for autism spectrum disorder (ASD) and atypical sensory processing patterns. This secondary analysis aimed to describe sensory profiles and explore effects of combined dietary docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and gamma-linolenic acid (GLA) supplementation on parent-reported sensory processing in toddlers born preterm who were exhibiting ASD symptoms. 90-day randomized, double blinded, placebo-controlled trial. 31 children aged 18-38months who were born at ≤29weeks' gestation. Mixed effects regression analyses followed intent to treat and explored effects on parent-reported sensory processing measured by the Infant/Toddler Sensory Profile (ITSP). Baseline ITSP scores reflected atypical sensory processing, with the majority of atypical scores falling below the mean. Sensory processing sections: auditory (above=0%, below=65%), vestibular (above=13%, below=48%), tactile (above=3%, below=35%), oral sensory (above=10%; below=26%), visual (above=10%, below=16%); sensory processing quadrants: low registration (above=3%; below=71%), sensation avoiding (above=3%; below=39%), sensory sensitivity (above=3%; below=35%), and sensation seeking (above=10%; below=19%). Twenty-eight of 31 children randomized had complete outcome data. Although not statistically significant (p=0.13), the magnitude of the effect for reduction in behaviors associated with sensory sensitivity was medium to large (effect size=0.57). No other scales reflected a similar magnitude of effect size (range: 0.10 to 0.32). The findings provide support for larger randomized trials of omega fatty acid supplementation for children at risk of sensory processing difficulties, especially those born preterm. Copyright © 2017 Elsevier B.V. All rights reserved.

Effectiveness of Healthy Relationships Video-Group—A Videoconferencing Group Intervention for Women Living with HIV: Preliminary Findings from a Randomized Controlled Trial

PubMed Central

Buhi, Eric R.; Baldwin, Julie; Chen, Henian; Johnson, Ayesha; Lynn, Vickie; Glueckauf, Robert

2014-01-01

Abstract Introduction: Expanded access to efficacious interventions is needed for women living with human immunodeficiency virus (WLH) in the United States. Availability of “prevention with (human immunodeficiency virus [HIV)] positives” interventions in rural/remote and low HIV prevalence areas remains limited, leaving WLH in these communities few options for receiving effective behavioral interventions such as Healthy Relationships (HR). Offering such programs via videoconferencing groups (VGs) may expand access. This analysis tests the effectiveness of HR-VG (versus wait-list control) for reducing sexual risk behavior among WLH and explores intervention satisfaction. Subjects and Methods: In this randomized controlled trial unprotected vaginal/anal sex occasions over the prior 3 months reported at the 6-month follow-up were compared across randomization groups through zero-inflated Poisson regression modeling, controlling for unprotected sex at baseline. Seventy-one WLH were randomized and completed the baseline assessment (n=36 intervention and n=35 control); 59 (83% in each group) had follow-up data. Results: Among those who engaged in unprotected sex at 6-month follow-up, intervention participants had approximately seven fewer unprotected occasions than control participants (95% confidence interval 5.43–7.43). Intervention participants reported high levels of satisfaction with HR-VG; 84% reported being “very satisfied” overall. Conclusions: This study found promising evidence for effective dissemination of HIV risk reduction interventions via VGs. Important next steps will be to determine whether VGs are effective with other subpopulations of people living with HIV (i.e., men and non-English speakers) and to assess cost-effectiveness. Possibilities for using VGs to expand access to other psychosocial and behavioral interventions and reduce stigma are discussed. PMID:24237482

Modeling spatial effects of PM{sub 2.5} on term low birth weight in Los Angeles County

DOE Office of Scientific and Technical Information (OSTI.GOV)

Coker, Eric, E-mail: cokerer@onid.orst.edu; Ghosh, Jokay; Jerrett, Michael

Air pollution epidemiological studies suggest that elevated exposure to fine particulate matter (PM{sub 2.5}) is associated with higher prevalence of term low birth weight (TLBW). Previous studies have generally assumed the exposure–response of PM{sub 2.5} on TLBW to be the same throughout a large geographical area. Health effects related to PM{sub 2.5} exposures, however, may not be uniformly distributed spatially, creating a need for studies that explicitly investigate the spatial distribution of the exposure–response relationship between individual-level exposure to PM{sub 2.5} and TLBW. Here, we examine the overall and spatially varying exposure–response relationship between PM{sub 2.5} and TLBW throughout urbanmore » Los Angeles (LA) County, California. We estimated PM{sub 2.5} from a combination of land use regression (LUR), aerosol optical depth from remote sensing, and atmospheric modeling techniques. Exposures were assigned to LA County individual pregnancies identified from electronic birth certificates between the years 1995-2006 (N=1,359,284) provided by the California Department of Public Health. We used a single pollutant multivariate logistic regression model, with multilevel spatially structured and unstructured random effects set in a Bayesian framework to estimate global and spatially varying pollutant effects on TLBW at the census tract level. Overall, increased PM{sub 2.5} level was associated with higher prevalence of TLBW county-wide. The spatial random effects model, however, demonstrated that the exposure–response for PM{sub 2.5} and TLBW was not uniform across urban LA County. Rather, the magnitude and certainty of the exposure–response estimates for PM{sub 2.5} on log odds of TLBW were greatest in the urban core of Central and Southern LA County census tracts. These results suggest that the effects may be spatially patterned, and that simply estimating global pollutant effects obscures disparities suggested by spatial patterns of effects. Studies that incorporate spatial multilevel modeling with random coefficients allow us to identify areas where air pollutant effects on adverse birth outcomes may be most severe and policies to further reduce air pollution might be most effective. - Highlights: • We model the spatial dependency of PM{sub 2.5} effects on term low birth weight (TLBW). • PM{sub 2.5} effects on TLBW are shown to vary spatially across urban LA County. • Modeling spatial dependency of PM{sub 2.5} health effects may identify effect 'hotspots'. • Birth outcomes studies should consider the spatial dependency of PM{sub 2.5} effects.« less

A multilevel model to estimate the within- and the between-center components of the exposure/disease association in the EPIC study.

PubMed

Sera, Francesco; Ferrari, Pietro

2015-01-01

In a multicenter study, the overall relationship between exposure and the risk of cancer can be broken down into a within-center component, which reflects the individual level association, and a between-center relationship, which captures the association at the aggregate level. A piecewise exponential proportional hazards model with random effects was used to evaluate the association between dietary fiber intake and colorectal cancer (CRC) risk in the EPIC study. During an average follow-up of 11.0 years, 4,517 CRC events occurred among study participants recruited in 28 centers from ten European countries. Models were adjusted by relevant confounding factors. Heterogeneity among centers was modelled with random effects. Linear regression calibration was used to account for errors in dietary questionnaire (DQ) measurements. Risk ratio estimates for a 10 g/day increment in dietary fiber were equal to 0.90 (95%CI: 0.85, 0.96) and 0.85 (0.64, 1.14), at the individual and aggregate levels, respectively, while calibrated estimates were 0.85 (0.76, 0.94), and 0.87 (0.65, 1.15), respectively. In multicenter studies, over a straightforward ecological analysis, random effects models allow information at the individual and ecologic levels to be captured, while controlling for confounding at both levels of evidence.

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

PubMed Central

Ali, S. M.; Mehmood, C. A; Khan, B.; Jawad, M.; Farid, U; Jadoon, J. K.; Ali, M.; Tareen, N. K.; Usman, S.; Majid, M.; Anwar, S. M.

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion. PMID:27314229

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

PubMed

Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

Utility-Weighted Modified Rankin Scale as Primary Outcome in Stroke Trials

PubMed Central

Voormolen, Daphne C.; Venema, Esmee; Roozenbeek, Bob; Polinder, Suzanne; Haagsma, Juanita A.; Nieboer, Daan; Chalos, Vicky; Yoo, Albert J.; Schreuders, Jennifer; van der Lugt, Aad; Majoie, Charles B.L.M.; Roos, Yvo B.W.E.M.; van Zwam, Wim H.; van Oostenbrugge, Robert J.; Steyerberg, Ewout W.; Dippel, Diederik W.J.; Lingsma, Hester F.

2018-01-01

Background and Purpose— The utility-weighted modified Rankin Scale (UW-mRS) has been proposed as a new patient-centered primary outcome in stroke trials. We aimed to describe utility weights for the mRS health states and to evaluate the statistical efficiency of the UW-mRS to detect treatment effects in stroke intervention trials. Methods— We used data of the 500 patients enrolled in the MR CLEAN (Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands). Utility values were elicited from the EuroQol Group 5-Dimension Self-Report Questionnaire assessed at 90 days after inclusion, simultaneously with the mRS. Utility weights were determined by averaging the utilities of all patients within each mRS category. We performed simulations to evaluate statistical efficiency. The simulated treatment effect was an odds ratio of 1.65 in favor of the treatment arm, similar for all mRS cutoffs. This treatment effect was analyzed using 3 approaches: linear regression with the UW-mRS as outcome, binary logistic regression with a dichotomized mRS (0–1/2–6, 0–2/3–6, and 0–4/5–6), and proportional odds logistic regression with the ordinal mRS. The statistical power of the 3 approaches was expressed as the proportion of 10 000 simulations that resulted in a statistically significant treatment effect (P≤0.05). Results— The mean utility values (SD) for mRS categories 0 to 6 were: 0.95 (0.08), 0.93 (0.13), 0.83 (0.21), 0.62 (0.27), 0.42 (0.28), 0.11 (0.28), and 0 (0), respectively, but varied substantially between individual patients within each category. The UW-mRS approach was more efficient than the dichotomous approach (power 85% versus 71%) but less efficient than the ordinal approach (power 85% versus 87%). Conclusions— The UW-mRS as primary outcome does not capture individual variation in utility values and may reduce the statistical power of a randomized trial. PMID:29535271

Heart failure, chronic diuretic use, and increase in mortality and hospitalization: an observational study using propensity score methods

PubMed Central

Ahmed, Ali; Husain, Ahsan; Love, Thomas E.; Gambassi, Giovanni; Dell'Italia, Louis J.; Francis, Gary S.; Gheorghiade, Mihai; Allman, Richard M.; Meleth, Sreelatha; Bourge, Robert C.

2008-01-01

Aims Non-potassium-sparing diuretics are commonly used in heart failure (HF). They activate the neurohormonal system, and are potentially harmful. Yet, the long-term effects of chronic diuretic use in HF are largely unknown. We retrospectively analysed the Digitalis Investigation Group (DIG) data to determine the effects of diuretics on HF outcomes. Methods and results Propensity scores for diuretic use were calculated for each of the 7788 DIG participants using a non-parsimonious multivariable logistic regression model, and were used to match 1391 (81%) no-diuretic patients with 1391 diuretic patients. Effects of diuretics on mortality and hospitalization at 40 months of median follow-up were assessed using matched Cox regression models. All-cause mortality was 21% for no-diuretic patients and 29% for diuretic patients [hazard ratio (HR) 1.31; 95% confidence interval (CI) 1.11−1.55; P = 0.002]. HF hospitalizations occurred in 18% of no-diuretic patients and 23% of diuretic patients (HR 1.37; 95% CI 1.13−1.65; P = 0.001). Conclusion Chronic diuretic use was associated with increased long-term mortality and hospitalizations in a wide spectrum of ambulatory chronic systolic and diastolic HF patients. The findings of the current study challenge the wisdom of routine chronic use of diuretics in HF patients who are asymptomatic or minimally symptomatic without fluid retention, and are on complete neurohormonal blockade. These findings, based on a non-randomized design, need to be further studied in randomized trials. PMID:16709595

Placebo and nocebo reactions in randomized trials of pharmacological treatments for persistent depressive disorder. A meta-regression analysis.

PubMed

Meister, Ramona; Jansen, Alessa; Härter, Martin; Nestoriuc, Yvonne; Kriston, Levente

2017-06-01

We aimed to investigate placebo and nocebo reactions in randomized controlled trials (RCT) of pharmacological treatments for persistent depressive disorder (PDD). We conducted a systematic electronic search and included RCTs investigating antidepressants for the treatment of PDD. Outcomes were the number of patients experiencing response and remission in placebo arms (=placebo reaction). Additional outcomes were the incidence of patients experiencing adverse events and related discontinuations in placebo arms (=nocebo reaction). A priori defined effect modifiers were analyzed using a series of meta-regression analyses. Twenty-three trials were included in the analyses. We found a pooled placebo response rate of 31% and a placebo remission rate of 22%. The pooled adverse event rate and related discontinuations were 57% and 4%, respectively. All placebo arm outcomes were positively associated with the corresponding medication arm outcomes. Placebo response rate was associated with a greater proportion of patients with early onset depression, a smaller chance to receive placebo and a larger sample size. The adverse event rate in placebo arms was associated with a greater proportion of patients with early onset depression, a smaller proportion of females and a more recent publication. Pooled placebo and nocebo reaction rates in PDD were comparable to those in episodic depression. The identified effect modifiers should be considered to assess unbiased effects in RCTs, to influence placebo and nocebo reactions in practice. Limitations result from the methodology applied, the fact that we conducted only univariate analyses, and the number and quality of included trials. Copyright © 2017 Elsevier B.V. All rights reserved.

Effects of Various Cryotherapy Applications on Postoperative Pain in Molar Teeth with Symptomatic Apical Periodontitis: A Preliminary Randomized Prospective Clinical Trial.

PubMed

Gundogdu, Eyup Candas; Arslan, Hakan

2018-03-01

The purpose of the study was to evaluate the effects of intracanal, intraoral, and extraoral cryotherapy on postoperative pain in molar teeth with symptomatic apical periodontitis. A total of 100 patients were randomly distributed into 4 groups: control (without cryotherapy application), intracanal cryotherapy application, intraoral cryotherapy application, and extraoral cryotherapy application. The postoperative pain of the patients was recorded at the first, third, fifth, and seventh days. The data were statistically analyzed by using linear regression, χ 2 , one-way analysis of variance, Tukey post hoc, and Kruskal-Wallis H tests (P = .05). There were no statistically significant differences among the groups in terms of demographic data (P > .05). The preoperative pain levels and preoperative visual analogue scale (VAS) scores of pain on percussion were similar among the groups (P > .05). The linear regression analysis demonstrated that group variable had the most significant effect on postoperative pain at day 1 (P < .001) among the other variables (group, age, gender, tooth number, preoperative pain levels, and VAS scores of pain on percussion). When compared with the control group, all the cryotherapy groups exhibited less percussion pain and less postoperative pain at the first, third, fifth, and seventh days (P < .05). Within the study limitations, all the cryotherapy applications (intracanal, intraoral, and extraoral) resulted in lower postoperative pain levels and lower VAS scores of pain on percussion versus those of the control group. Copyright © 2017 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

High-Dose Chemotherapy With Autologous Hematopoietic Stem-Cell Transplantation in Metastatic Breast Cancer: Overview of Six Randomized Trials

PubMed Central

Berry, Donald A.; Ueno, Naoto T.; Johnson, Marcella M.; Lei, Xiudong; Caputo, Jean; Smith, Dori A.; Yancey, Linda J.; Crump, Michael; Stadtmauer, Edward A.; Biron, Pierre; Crown, John P.; Schmid, Peter; Lotz, Jean-Pierre; Rosti, Giovanni; Bregni, Marco; Demirer, Taner

2011-01-01

Purpose High doses of effective chemotherapy are compelling if they can be delivered safely. Substantial interest in supporting high-dose chemotherapy with bone marrow or autologous hematopoietic stem-cell transplantation in the 1980s and 1990s led to the initiation of randomized trials to evaluate its effect in the treatment of metastatic breast cancer. Methods We identified six randomized trials in metastatic breast cancer that evaluated high doses of chemotherapy with transplant support versus a control regimen without stem-cell support. We assembled a single database containing individual patient information from these trials. The primary analysis of overall survival was a log-rank test comparing high dose versus control. We also used Cox proportional hazards regression, adjusting for known covariates. We addressed potential treatment differences within subsets of patients. Results The effect of high-dose chemotherapy on overall survival was not statistically different (median, 2.16 v 2.02 years; P = .08). A statistically significant advantage in progression-free survival (median, 0.91 v 0.69 years) did not translate into survival benefit. Subset analyses found little evidence that there are groups of patients who might benefit from high-dose chemotherapy with hematopoietic support. Conclusion Overall survival of patients with metastatic breast cancer in the six randomized trials was not significantly improved by high-dose chemotherapy; any benefit from high doses was small. No identifiable subset of patients seems to benefit from high-dose chemotherapy. PMID:21768454

Representation of limb kinematics in Purkinje cell simple spike discharge is conserved across multiple tasks

PubMed Central

Hewitt, Angela L.; Popa, Laurentiu S.; Pasalar, Siavash; Hendrix, Claudia M.

2011-01-01

Encoding of movement kinematics in Purkinje cell simple spike discharge has important implications for hypotheses of cerebellar cortical function. Several outstanding questions remain regarding representation of these kinematic signals. It is uncertain whether kinematic encoding occurs in unpredictable, feedback-dependent tasks or kinematic signals are conserved across tasks. Additionally, there is a need to understand the signals encoded in the instantaneous discharge of single cells without averaging across trials or time. To address these questions, this study recorded Purkinje cell firing in monkeys trained to perform a manual random tracking task in addition to circular tracking and center-out reach. Random tracking provides for extensive coverage of kinematic workspaces. Direction and speed errors are significantly greater during random than circular tracking. Cross-correlation analyses comparing hand and target velocity profiles show that hand velocity lags target velocity during random tracking. Correlations between simple spike firing from 120 Purkinje cells and hand position, velocity, and speed were evaluated with linear regression models including a time constant, τ, as a measure of the firing lead/lag relative to the kinematic parameters. Across the population, velocity accounts for the majority of simple spike firing variability (63 ± 30% of Radj2), followed by position (28 ± 24% of Radj2) and speed (11 ± 19% of Radj2). Simple spike firing often leads hand kinematics. Comparison of regression models based on averaged vs. nonaveraged firing and kinematics reveals lower Radj2 values for nonaveraged data; however, regression coefficients and τ values are highly similar. Finally, for most cells, model coefficients generated from random tracking accurately estimate simple spike firing in either circular tracking or center-out reach. These findings imply that the cerebellum controls movement kinematics, consistent with a forward internal model that predicts upcoming limb kinematics. PMID:21795616

Computer-delivered interventions for reducing alcohol consumption: meta-analysis and meta-regression using behaviour change techniques and theory.

PubMed

Black, Nicola; Mullan, Barbara; Sharpe, Louise

2016-09-01

The current aim was to examine the effectiveness of behaviour change techniques (BCTs), theory and other characteristics in increasing the effectiveness of computer-delivered interventions (CDIs) to reduce alcohol consumption. Included were randomised studies with a primary aim of reducing alcohol consumption, which compared self-directed CDIs to assessment-only control groups. CDIs were coded for the use of 42 BCTs from an alcohol-specific taxonomy, the use of theory according to a theory coding scheme and general characteristics such as length of the CDI. Effectiveness of CDIs was assessed using random-effects meta-analysis and the association between the moderators and effect size was assessed using univariate and multivariate meta-regression. Ninety-three CDIs were included in at least one analysis and produced small, significant effects on five outcomes (d+ = 0.07-0.15). Larger effects occurred with some personal contact, provision of normative information or feedback on performance, prompting commitment or goal review, the social norms approach and in samples with more women. Smaller effects occurred when information on the consequences of alcohol consumption was provided. These findings can be used to inform both intervention- and theory-development. Intervention developers should focus on, including specific, effective techniques, rather than many techniques or more-elaborate approaches.

Systematic Review and Meta-Analysis: Dose-Response Relationship of Selective Serotonin Reuptake Inhibitors in Major Depressive Disorder.

PubMed

Jakubovski, Ewgeni; Varigonda, Anjali L; Freemantle, Nicholas; Taylor, Matthew J; Bloch, Michael H

2016-02-01

Previous studies suggested that the treatment response to selective serotonin reuptake inhibitors (SSRIs) in major depressive disorder follows a flat response curve within the therapeutic dose range. The present study was designed to clarify the relationship between dosage and treatment response in major depressive disorder. The authors searched PubMed for randomized placebo-controlled trials examining the efficacy of SSRIs for treating adults with major depressive disorder. Trials were also required to assess improvement in depression severity at multiple time points. Additional data were collected on treatment response and all-cause and side effect-related discontinuation. All medication doses were transformed into imipramine-equivalent doses. The longitudinal data were analyzed with a mixed-regression model. Endpoint and tolerability analyses were analyzed using meta-regression and stratified subgroup analysis by predefined SSRI dose categories in order to assess the effect of SSRI dosing on the efficacy and tolerability of SSRIs for major depressive disorder. Forty studies involving 10,039 participants were included. Longitudinal modeling (dose-by-time interaction=0.0007, 95% CI=0.0001-0.0013) and endpoint analysis (meta-regression: β=0.00053, 95% CI=0.00018-0.00088, z=2.98) demonstrated a small but statistically significant positive association between SSRI dose and efficacy. Higher doses of SSRIs were associated with an increased likelihood of dropouts due to side effects (meta-regression: β=0.00207, 95% CI=0.00071-0.00342, z=2.98) and decreased likelihood of all-cause dropout (meta-regression: β=-0.00093, 95% CI=-0.00165 to -0.00021, z=-2.54). Higher doses of SSRIs appear slightly more effective in major depressive disorder. This benefit appears to plateau at around 250 mg of imipramine equivalents (50 mg of fluoxetine). The slightly increased benefits of SSRIs at higher doses are somewhat offset by decreased tolerability at high doses.

Randomized Clinical Trial of Motivational Enhancement of Substance Use Treatment among Incarcerated Adolescents: Post-Release Condom Non-Use

ERIC Educational Resources Information Center

Rosengard, Cynthia; Stein, L. A. R.; Barnett, Nancy P.; Monti, Peter M.; Golembeske, Charles; Lebeau-Craven, Rebecca; Miranda, Robert

2007-01-01

Evaluated impact of motivational enhancement (ME) of substance abuse treatment compared to relaxation training (RT) on sex without condoms (overall and involving substance use) 3 months following release among incarcerated adolescents. This randomized clinical trial involved 114 incarcerated adolescents from the Northeast. Regression analyses…

Logistic quantile regression provides improved estimates for bounded avian counts: A case study of California Spotted Owl fledgling production

USGS Publications Warehouse

Cade, Brian S.; Noon, Barry R.; Scherer, Rick D.; Keane, John J.

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical conditional distribution of a bounded discrete random variable. The logistic quantile regression model requires that counts are randomly jittered to a continuous random variable, logit transformed to bound them between specified lower and upper values, then estimated in conventional linear quantile regression, repeating the 3 steps and averaging estimates. Back-transformation to the original discrete scale relies on the fact that quantiles are equivariant to monotonic transformations. We demonstrate this statistical procedure by modeling 20 years of California Spotted Owl fledgling production (0−3 per territory) on the Lassen National Forest, California, USA, as related to climate, demographic, and landscape habitat characteristics at territories. Spotted Owl fledgling counts increased nonlinearly with decreasing precipitation in the early nesting period, in the winter prior to nesting, and in the prior growing season; with increasing minimum temperatures in the early nesting period; with adult compared to subadult parents; when there was no fledgling production in the prior year; and when percentage of the landscape surrounding nesting sites (202 ha) with trees ≥25 m height increased. Changes in production were primarily driven by changes in the proportion of territories with 2 or 3 fledglings. Average variances of the discrete cumulative distributions of the estimated fledgling counts indicated that temporal changes in climate and parent age class explained 18% of the annual variance in owl fledgling production, which was 34% of the total variance. Prior fledgling production explained as much of the variance in the fledgling counts as climate, parent age class, and landscape habitat predictors. Our logistic quantile regression model can be used for any discrete response variables with fixed upper and lower bounds.

Effects of greening and community reuse of vacant lots on crime

PubMed Central

Kondo, Michelle; Hohl, Bernadette; Han, SeungHoon; Branas, Charles

2016-01-01

The Youngstown Neighborhood Development Corporation initiated a ‘Lots of Green’ programme to reuse vacant land in 2010. We performed a difference-in-differences analysis of the effects of this programme on crime in and around newly treated lots, in comparison to crimes in and around randomly selected and matched, untreated vacant lot controls. The effects of two types of vacant lot treatments on crime were tested: a cleaning and greening ‘stabilisation’ treatment and a ‘community reuse’ treatment mostly involving community gardens. The combined effects of both types of vacant lot treatments were also tested. After adjustment for various sociodemographic factors, linear and Poisson regression models demonstrated statistically significant reductions in all crime classes for at least one lot treatment type. Regression models adjusted for spatial autocorrelation found the most consistent significant reductions in burglaries around stabilisation lots, and in assaults around community reuse lots. Spill-over crime reduction effects were found in contiguous areas around newly treated lots. Significant increases in motor vehicle thefts around both types of lots were also found after they had been greened. Community-initiated vacant lot greening may have a greater impact on reducing more serious, violent crimes. PMID:28529389

Misspecification of Cox regression models with composite endpoints

PubMed Central

Wu, Longyang; Cook, Richard J

2012-01-01

Researchers routinely adopt composite endpoints in multicenter randomized trials designed to evaluate the effect of experimental interventions in cardiovascular disease, diabetes, and cancer. Despite their widespread use, relatively little attention has been paid to the statistical properties of estimators of treatment effect based on composite endpoints. We consider this here in the context of multivariate models for time to event data in which copula functions link marginal distributions with a proportional hazards structure. We then examine the asymptotic and empirical properties of the estimator of treatment effect arising from a Cox regression model for the time to the first event. We point out that even when the treatment effect is the same for the component events, the limiting value of the estimator based on the composite endpoint is usually inconsistent for this common value. We find that in this context the limiting value is determined by the degree of association between the events, the stochastic ordering of events, and the censoring distribution. Within the framework adopted, marginal methods for the analysis of multivariate failure time data yield consistent estimators of treatment effect and are therefore preferred. We illustrate the methods by application to a recent asthma study. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22736519

The Effect of Improved Water Supply on Diarrhea Prevalence of Children under Five in the Volta Region of Ghana: A Cluster-Randomized Controlled Trial.

PubMed

Cha, Seungman; Kang, Douk; Tuffuor, Benedict; Lee, Gyuhong; Cho, Jungmyung; Chung, Jihye; Kim, Myongjin; Lee, Hoonsang; Lee, Jaeeun; Oh, Chunghyeon

2015-09-25

Although a number of studies have been conducted to explore the effect of water quality improvement, the majority of them have focused mainly on point-of-use water treatment, and the studies investigating the effect of improved water supply have been based on observational or inadequately randomized trials. We report the results of a matched cluster randomized trial investigating the effect of improved water supply on diarrheal prevalence of children under five living in rural areas of the Volta Region in Ghana. We compared the diarrheal prevalence of 305 children in 10 communities of intervention with 302 children in 10 matched communities with no intervention (October 2012 to February 2014). A modified Poisson regression was used to estimate the prevalence ratio. An intention-to-treat analysis was undertaken. The crude prevalence ratio of diarrhea in the intervention compared with the control communities was 0.85 (95% CI 0.74-0.97) for Krachi West, 0.96 (0.87-1.05) for Krachi East, and 0.91 (0.83-0.98) for both districts. Sanitation was adjusted for in the model to remove the bias due to residual imbalance since it was not balanced even after randomization. The adjusted prevalence ratio was 0.82 (95% CI 0.71-0.96) for Krachi West, 0.95 (0.86-1.04) for Krachi East, and 0.89 (0.82-0.97) for both districts. This study provides a basis for a better approach to water quality interventions.

The Effect of Improved Water Supply on Diarrhea Prevalence of Children under Five in the Volta Region of Ghana: A Cluster-Randomized Controlled Trial

PubMed Central

Cha, Seungman; Kang, Douk; Tuffuor, Benedict; Lee, Gyuhong; Cho, Jungmyung; Chung, Jihye; Kim, Myongjin; Lee, Hoonsang; Lee, Jaeeun; Oh, Chunghyeon

2015-01-01

Although a number of studies have been conducted to explore the effect of water quality improvement, the majority of them have focused mainly on point-of-use water treatment, and the studies investigating the effect of improved water supply have been based on observational or inadequately randomized trials. We report the results of a matched cluster randomized trial investigating the effect of improved water supply on diarrheal prevalence of children under five living in rural areas of the Volta Region in Ghana. We compared the diarrheal prevalence of 305 children in 10 communities of intervention with 302 children in 10 matched communities with no intervention (October 2012 to February 2014). A modified Poisson regression was used to estimate the prevalence ratio. An intention-to-treat analysis was undertaken. The crude prevalence ratio of diarrhea in the intervention compared with the control communities was 0.85 (95% CI 0.74–0.97) for Krachi West, 0.96 (0.87–1.05) for Krachi East, and 0.91 (0.83–0.98) for both districts. Sanitation was adjusted for in the model to remove the bias due to residual imbalance since it was not balanced even after randomization. The adjusted prevalence ratio was 0.82 (95% CI 0.71–0.96) for Krachi West, 0.95 (0.86–1.04) for Krachi East, and 0.89 (0.82–0.97) for both districts. This study provides a basis for a better approach to water quality interventions. PMID:26404337

Using structural equation modeling to construct calibration equations relating PM2.5 mass concentration samplers to the federal reference method sampler

NASA Astrophysics Data System (ADS)

Bilonick, Richard A.; Connell, Daniel P.; Talbott, Evelyn O.; Rager, Judith R.; Xue, Tao

2015-02-01

The objective of this study was to remove systematic bias among fine particulate matter (PM2.5) mass concentration measurements made by different types of samplers used in the Pittsburgh Aerosol Research and Inhalation Epidemiology Study (PARIES). PARIES is a retrospective epidemiology study that aims to provide a comprehensive analysis of the associations between air quality and human health effects in the Pittsburgh, Pennsylvania, region from 1999 to 2008. Calibration was needed in order to minimize the amount of systematic error in PM2.5 exposure estimation as a result of including data from 97 different PM2.5 samplers at 47 monitoring sites. Ordinary regression often has been used for calibrating air quality measurements from pairs of measurement devices; however, this is only appropriate when one of the two devices (the "independent" variable) is free from random error, which is rarely the case. A group of methods known as "errors-in-variables" (e.g., Deming regression, reduced major axis regression) has been developed to handle calibration between two devices when both are subject to random error, but these methods require information on the relative sizes of the random errors for each device, which typically cannot be obtained from the observed data. When data from more than two devices (or repeats of the same device) are available, the additional information is not used to inform the calibration. A more general approach that often has been overlooked is the use of a measurement error structural equation model (SEM) that allows the simultaneous comparison of three or more devices (or repeats). The theoretical underpinnings of all of these approaches to calibration are described, and the pros and cons of each are discussed. In particular, it is shown that both ordinary regression (when used for calibration) and Deming regression are particular examples of SEMs but with substantial deficiencies. To illustrate the use of SEMs, the 7865 daily average PM2.5 mass concentration measurements made by seven collocated samplers at an urban monitoring site in Pittsburgh, Pennsylvania, were used. These samplers, which included three federal reference method (FRM) samplers, three speciation samplers, and a tapered element oscillating microbalance (TEOM), operated at various times during the 10-year PARIES study period. Because TEOM measurements are known to depend on temperature, the constructed SEM provided calibration equations relating the TEOM to the FRM and speciation samplers as a function of ambient temperature. It was shown that TEOM imprecision and TEOM bias (relative to the FRM) both decreased as temperature increased. It also was shown that the temperature dependency for bias was non-linear and followed a sigmoidal (logistic) pattern. The speciation samplers exhibited only small bias relative to the FRM samplers, although the FRM samplers were shown to be substantially more precise than both the TEOM and the speciation samplers. Comparison of the SEM results to pairwise simple linear regression results showed that the regression results can differ substantially from the correctly-derived calibration equations, especially if the less-precise device is used as the independent variable in the regression.

Efficacy of Cognitive Behavioral Therapy for Generalized Anxiety Disorder in Older Adults: Systematic Review, Meta-Analysis, and Meta-Regression.

PubMed

Hall, Jo; Kellett, Stephen; Berrios, Raul; Bains, Manreesh Kaur; Scott, Shonagh

2016-11-01

Generalized anxiety disorder (GAD) is a common disorder in older adults producing functional impairment, and psychotherapy is the preferred treatment option. Meta-analytic methods sought to determine the efficacy of outpatient cognitive behavioral therapy (CBT) with respect to the hallmark feature of GAD-uncontrolled and excessive worry. In order to optimize clinical applicability, variables associated with GAD treatment outcomes were also examined. Systematic search of relevant databases and iterative searches of references from articles retrieved. All studies were required to have been a randomized control trial (RCT), to have used the Penn State Worry Questionnaire (PSWQ) or its abbreviated version (PSWQ-A) as an outcome measure, and to have conducted CBT with outpatient older adults. Fourteen RCTs (N = 985) were suitable and random-effects meta-analyses and univariate meta-regressions were conducted. At the end of treatment, and at 6-month follow-up, significant treatment effects favoring CBT were found in comparison to a waitlist or treatment-as-usual. When CBT was compared with active controls, a small nonsignificant treatment advantage was found for CBT at the end of treatment, with equivalence of outcomes at follow-up. Treatment effect size of CBT for GAD was significantly associated with attrition rates and depression outcomes. CBT is more helpful than having no treatment for GAD in later life. Nevertheless, whether CBT shows long-term durability, or is superior to other commonly available treatments (such as supportive psychotherapy), remains to be tested. The relationship between treatment effects for GAD and depression following CBT warrants further research. Copyright © 2016 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

POOLED ESTIMATES OF INCIDENCE OF ENDOPHTHALMITIS AFTER INTRAVITREAL INJECTION OF ANTI-VASCULAR ENDOTHELIAL GROWTH FACTOR AGENTS WITH AND WITHOUT TOPICAL ANTIBIOTIC PROPHYLAXIS.

PubMed

Reibaldi, Michele; Pulvirenti, Alfredo; Avitabile, Teresio; Bonfiglio, Vincenza; Russo, Andrea; Mariotti, Cesare; Bucolo, Claudio; Mastropasqua, Rodolfo; Parisi, Guglielmo; Longo, Antonio

2018-01-01

To assess the effect of topical antibiotic prophylaxis on postoperative endophthalmitis after intravitreal injection of anti-vascular endothelial growth factor agents. A systematic literature search was performed from inception to March 2016 using PubMed, Medline, Web of Science, Embase, and the Cochrane Library, to identify articles that reported cases of endophthalmitis after intravitreal injection of anti-vascular endothelial growth factor agents. We used a pooled analysis to estimate the incidence of cases of endophthalmitis who developed after injections performed with and without topical antibiotic prophylaxis. We used regression analysis to explore the effects of study characteristics on heterogeneity. From our search of electronic databases, we identified and screened 4,561 unique records. We judged 60 articles to have reported findings for cohorts of patients who met our inclusion criteria, (12 arms of randomized clinical trials, 11 prospective cohort studies, and 37 retrospective cohort studies), which included 244 cases of endophthalmitis and 639,391 intravitreal injections of anti-vascular endothelial growth factor agents. The final pooled estimate endophthalmitis proportions were 9/10,000 (95% confidence interval, 7/10,000-12/10,000) in the antibiotic-treated group and 3/10,000 (95% confidence interval, 2/10,000-5/10,000) in the untreated group. The estimated incidence of endophthalmitis with topical antibiotic prophylaxis was approximated three times the incidence without prophylaxis. Random effects regression showed that none of the study characteristics significantly affected the effect size in either group. Topical antibiotic after intravitreal injection of anti-vascular endothelial growth factor agents is associated with a higher risk of endophthalmitis.

Academic performance of children born preterm: a meta-analysis and meta-regression.

PubMed

Twilhaar, E Sabrina; de Kieviet, Jorrit F; Aarnoudse-Moens, Cornelieke Sh; van Elburg, Ruurd M; Oosterlaan, Jaap

2017-08-28

Advances in neonatal healthcare have resulted in decreased mortality after preterm birth but have not led to parallel decreases in morbidity. Academic performance provides insight in the outcomes and specific difficulties and needs of preterm children. To study academic performance in preterm children born in the antenatal steroids and surfactant era and possible moderating effects of perinatal and demographic factors. PubMed, Web of Science and PsycINFO were searched for peer-reviewed articles. Cohort studies with a full-term control group reporting standardised academic performance scores of preterm children (<37 weeks of gestation) at age 5 years or older and born in the antenatal steroids and surfactant era were included. Academic test scores and special educational needs of preterm and full-term children were analysed using random effects meta-analysis. Random effects meta-regressions were performed to explore the predictive role of perinatal and demographic factors for between-study variance in effect sizes. The 17 eligible studies included 2390 preterm children and 1549 controls. Preterm children scored 0.71 SD below full-term peers on arithmetic (p<0.001), 0.44 and 0.52 SD lower on reading and spelling (p<0.001) and were 2.85 times more likely to receive special educational assistance (95% CI 2.12 to 3.84, p<0.001). Bronchopulmonarydysplasia explained 44% of the variance in academic performance (p=0.006). Preterm children born in the antenatal steroids and surfactant era show considerable academic difficulties. Preterm children with bronchopulmonarydysplasia are at particular risk for poor academic outcome. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

The impact of pneumatic tube system on routine laboratory parameters: a systematic review and meta-analysis.

PubMed

Kapoula, Georgia V; Kontou, Panagiota I; Bagos, Pantelis G

2017-10-26

Pneumatic tube system (PTS) is a widely used method of transporting blood samples in hospitals. The aim of this study was to evaluate the effects of the PTS transport in certain routine laboratory parameters as it has been implicated with hemolysis. A systematic review and a meta-analysis were conducted. PubMed and Scopus databases were searched (up until November 2016) to identify prospective studies evaluating the impact of PTS transport in hematological, biochemical and coagulation measurements. The random-effects model was used in the meta-analysis utilizing the mean difference (MD). Heterogeneity was quantitatively assessed using the Cohran's Q and the I2 index. Subgroup analysis, meta-regression analysis, sensitivity analysis, cumulative meta-analysis and assessment of publication bias were performed for all outcomes. From a total of 282 studies identified by the searching procedure, 24 were finally included in the meta-analysis. The meta-analysis yielded statistically significant results for potassium (K) [MD=0.04 mmol/L; 95% confidence interval (CI)=0.015-0.065; p=0.002], lactate dehydrogenase (LDH) (MD=10.343 U/L; 95% CI=6.132-14.554; p<10-4) and aspartate aminotransferase (AST) (MD=1.023 IU/L; 95% CI=0.344-1.702; p=0.003). Subgroup analysis and random-effects meta-regression analysis according to the speed and distance of the samples traveled via the PTS revealed that there is relation between the rate and the distance of PTS with the measurements of K, LDH, white blood cells and red blood cells. This meta-analysis suggests that PTS may be associated with alterations in K, LDH and AST measurements. Although these findings may not have any significant clinical effect on laboratory results, it is wise that each hospital validates their PTS.

Observational studies using propensity score analysis underestimated the effect sizes in critical care medicine.

PubMed

Zhang, Zhongheng; Ni, Hongying; Xu, Xiao

2014-08-01

Propensity score (PS) analysis has been increasingly used in critical care medicine; however, its validation has not been systematically investigated. The present study aimed to compare effect sizes in PS-based observational studies vs. randomized controlled trials (RCTs) (or meta-analysis of RCTs). Critical care observational studies using PS were systematically searched in PubMed from inception to April 2013. Identified PS-based studies were matched to one or more RCTs in terms of population, intervention, comparison, and outcome. The effect sizes of experimental treatments were compared for PS-based studies vs. RCTs (or meta-analysis of RCTs) with sign test. Furthermore, ratio of odds ratio (ROR) was calculated from the interaction term of treatment × study type in a logistic regression model. A ROR < 1 indicates greater benefit for experimental treatment in RCTs compared with PS-based studies. RORs of each comparison were pooled by using meta-analytic approach with random-effects model. A total of 20 PS-based studies were identified and matched to RCTs. Twelve of the 20 comparisons showed greater beneficial effect for experimental treatment in RCTs than that in PS-based studies (sign test P = 0.503). The difference was statistically significant in four comparisons. ROR can be calculated from 13 comparisons, of which four showed significantly greater beneficial effect for experimental treatment in RCTs. The pooled ROR was 0.71 (95% CI: 0.63, 0.79; P = 0.002), suggesting that RCTs (or meta-analysis of RCTs) were more likely to report beneficial effect for the experimental treatment than PS-based studies. The result remained unchanged in sensitivity analysis and meta-regression. In critical care literature, PS-based observational study is likely to report less beneficial effect of experimental treatment compared with RCTs (or meta-analysis of RCTs). Copyright © 2014 Elsevier Inc. All rights reserved.

Causal inference from observational data.

PubMed

Listl, Stefan; Jürges, Hendrik; Watt, Richard G

2016-10-01

Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data'). © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

On Models for Binomial Data with Random Numbers of Trials

PubMed Central

Comulada, W. Scott; Weiss, Robert E.

2010-01-01

Summary A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability π of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how π is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study. PMID:17688514

Individual relocation decisions after tornadoes: a multi-level analysis.

PubMed

Cong, Zhen; Nejat, Ali; Liang, Daan; Pei, Yaolin; Javid, Roxana J

2018-04-01

This study examines how multi-level factors affected individuals' relocation decisions after EF4 and EF5 (Enhanced Fujita Tornado Intensity Scale) tornadoes struck the United States in 2013. A telephone survey was conducted with 536 respondents, including oversampled older adults, one year after these two disaster events. Respondents' addresses were used to associate individual information with block group-level variables recorded by the American Community Survey. Logistic regression revealed that residential damage and homeownership are important predictors of relocation. There was also significant interaction between these two variables, indicating less difference between homeowners and renters at higher damage levels. Homeownership diminished the likelihood of relocation among younger respondents. Random effects logistic regression found that the percentage of homeownership and of higher income households in the community buffered the effect of damage on relocation; the percentage of older adults reduced the likelihood of this group relocating. The findings are assessed from the standpoint of age difference, policy implications, and social capital and vulnerability. © 2018 The Author(s). Disasters © Overseas Development Institute, 2018.

Estimating the Impact of the PROMISE Scholarship Using Propensity Score Weighted Frontier Fuzzy Regression Discontinuity Design

ERIC Educational Resources Information Center

Shobo, Yetty; Wong, Jen D.; Bell, Angie

2014-01-01

Regression discontinuity (RD), an "as good as randomized," research design is increasingly prominent in education research in recent years; the design gets eligible quasi-experimental designs as close as possible to experimental designs by using a stated threshold on a continuous baseline variable to assign individuals to a…

Testing the Hypothesis of a Homoscedastic Error Term in Simple, Nonparametric Regression

ERIC Educational Resources Information Center

Wilcox, Rand R.

2006-01-01

Consider the nonparametric regression model Y = m(X)+ [tau](X)[epsilon], where X and [epsilon] are independent random variables, [epsilon] has a median of zero and variance [sigma][squared], [tau] is some unknown function used to model heteroscedasticity, and m(X) is an unknown function reflecting some conditional measure of location associated…

Development of Multiple Regression Equations To Predict Fourth Graders' Achievement in Reading and Selected Content Areas.

ERIC Educational Resources Information Center

Hafner, Lawrence E.

A study developed a multiple regression prediction equation for each of six selected achievement variables in a popular standardized test of achievement. Subjects, 42 fourth-grade pupils randomly selected across several classes in a large elementary school in a north Florida city, were administered several standardized tests to determine predictor…

Randomized prospective study of olecranon fracture fixation: cable pin system versus tension band wiring.

PubMed

Liu, Q-H; Fu, Z-G; Zhou, J-L; Lu, T; Liu, T; Shan, L; Liu, Y; Bai, L

2012-01-01

This prospective, randomized study compared the effectiveness of the cable pin system (CPS) versus tension band wiring (TBW) for olecranon fracture fixation. Patients with acute transverse or slight oblique olecranon fractures were randomly divided into two groups: one fixed by CPS and the other by TBW. Clinical outcome data were collected and analysed following a mean duration of 21 months. The mean ± SD fracture healing time was significantly shorter in the CPS group (n = 30; 9.73 ± 2.02 weeks) compared with the TBW group (n = 32; 11.13 ± 2.21 weeks). One patient in the CPS group and seven patients in the TBW group experienced postoperative complications; this difference was statistically significant. The mean ± SD Mayo Elbow Performance Score in the CPS group was significantly higher (88.67 ± 6.42) than that in the TBW group (80.78 ± 11.99). Logistic regression analysis showed an association between fixation method and fracture healing time, complications and elbow function. Internal fixation by CPS is an effective method for olecranon fracture and is associated with a shorter healing time, fewer complications and better function than TBW.

Peer influence on pre-adolescent girls' snack intake: effects of weight status.

PubMed

Salvy, Sarah-Jeanne; Romero, Natalie; Paluch, Rocco; Epstein, Leonard H

2007-07-01

Although most eating occurs in a social context, the effects of peer influence on child eating have not been the object of systematic experimental study. The present study assesses the effects of peer influence on lean and overweight pre-adolescent girls' snack intake as a function of the co-eaters' weight status. The weight status of the participants was varied by studying weight discordant dyads (i.e., one lean and one overweight participant) and weight concordant dyads (i.e., both members of the dyads were either lean or overweight). Results from the random regression model indicate that overweight girls eating with an overweight peer consumed more kilocalories than overweight participants eating with a normal-weight peer. Normal-weight participants eating with overweight peers ate similar amounts as those eating with lean eating companions. The regression model improved when the partners' food intake was entered in the model, indicating that the peers' intake was a significant predictor of participants' snack consumption. This study underscores differences in responses to the social environment between overweight and non-overweight youths.

Impact of fibrates on circulating cystatin C levels: a systematic review and meta-analysis of clinical trials.

PubMed

Sahebkar, Amirhossein; Simental-Mendía, Luis E; Pirro, Matteo; Montecucco, Fabrizio; Carbone, Federico; Banach, Maciej; Barreto, George E; Butler, Alexandra E

2018-06-29

To assess the effect of fibrates on circulating cystatin C levels. Clinical studies evaluating the effect of a fibrate on circulating cystatin C levels were searched in PubMed-Medline, SCOPUS, Web of Science and Google Scholar databases. A random-effect model and generic inverse variance method were used for quantitative data synthesis, sensitivity analysis conducted using the leave-one-out method, and weighted random-effects meta-regression performed to evaluate potential confounders on cystatin C levels. This meta-analysis of data from 9 published studies (16 treatment arms) involved a total of 2195 subjects. In a single-arm analysis of clinical trials (without control group; 8 studies comprising 14 treatment arms), fibrate therapy increased circulating cystatin C concentrations (WMD: 0.07 mg/dL, 95% CI: 0.04, 0.10, p <0.001; I 2 = 82.66%). When the analysis was restricted to randomized controlled trials (4 studies comprising 6 treatment arms), again elevation of circulating cystatin C levels was observed (WMD: 0.06 mg/L, 95% CI: 0.03, 0.09, p <0.001; I 2 = 42.98%). Elevated cystatin C levels were only seen with fenofibrate, not other fibrates. The results suggest that fenofibrate treatment adversely affects cystatin C levels and might partially explain the limited efficacy of fenofibrate in reducing cardiovascular events.

Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

NASA Astrophysics Data System (ADS)

Hoffman, A.; Forest, C. E.; Kemanian, A.

2016-12-01

A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with traditional crop models, but likely at the cost of removing climate information. Our random forest models consistently discover the positive trend without removing any additional data. The application of random forests as a statistical crop model provides insight into understanding the impact of dust on yields in marginal food producing regions.

Foley catheter placement for induction of labor with or without stylette: a randomized clinical trial.

PubMed

Forgie, Marie M; Greer, Danielle M; Kram, Jessica J F; Vander Wyst, Kiley B; Salvo, Nicole P; Siddiqui, Danish S

2016-03-01

Foley catheters are used for cervical ripening during induction of labor. Previous studies suggest that use of a stylette (a thin, rigid wire) to guide catheter insertion decreases insertion failure. However, stylette effects on insertion outcomes have been sparsely studied. The purpose of this study was to compare catheter insertion times, patient-assessed pain levels, and insertion failure rates between women who received a digitally placed Foley catheter for cervical ripening with the aid of a stylette and women who received the catheter without a stylette. We conducted a randomized clinical trial of women aged ≥ 18 years who presented for induction of labor. Inclusion criteria were singletons with intact membranes and cephalic presentation. Women received a computer-generated random assignment of a Foley catheter insertion with a stylette (treatment group, n = 62) or without a stylette (control group, n = 61). For all women, a standard insertion technique protocol was used. Three primary outcomes were of interest, including the following: (1) insertion time (total minutes to successful catheter placement), (2) patient-assessed pain level (0-10), and (3) failure rate of the randomly assigned insertion method. Treatment control differences were first examined using the Pearson's test of independence and the Student t test. Per outcome, we also constructed 4 regression models, each including the random effect of physician and fixed effects of stylette use with patient nulliparity, a history of vaginal delivery, cervical dilation at presentation, or postgraduate year of the performing resident physician. Women who received the Foley catheter with the stylette vs without the stylette did not differ by age, race/ethnicity, body mass index, or any of several other characteristics. Regression models revealed that insertion time, patient pain, and insertion failure were unrelated to stylette use, nulliparity, and history of vaginal delivery. However, overall insertion time and failure were significantly influenced by cervical dilation, with insertion time decreasing by 21% (95% confidence interval [CI], 5-34%) and odds of failure decreasing by 71% (odds ratio, 0.29; 95% CI, 0.10-0.86) per 1 cm dilation. Resident postgraduate year also significantly influenced insertion time, with greater time required of physicians with less experience. Mean insertion time was 51% (95% CI, 23-69%) shorter for fourth-year than second-year residents. Statistically nonsignificant but prominent patterns in outcomes were also observed, suggesting stylette use may lengthen the overall insertion procedure but minimize variability in pain levels and decrease insertion failure. The randomized trial suggests that, even after accounting for nulliparity, history of vaginal delivery, cervical dilation, and physician experience, Foley catheter insertions with and without a stylette are equivalent in insertion times, patient pain levels, and failure of catheter placement. Copyright © 2016 Elsevier Inc. All rights reserved.

Use of Thematic Mapper for water quality assessment

NASA Technical Reports Server (NTRS)

Horn, E. M.; Morrissey, L. A.

1984-01-01

The evaluation of simulated TM data obtained on an ER-2 aircraft at twenty-five predesignated sample sites for mapping water quality factors such as conductivity, pH, suspended solids, turbidity, temperature, and depth, is discussed. Using a multiple regression for the seven TM bands, an equation is developed for the suspended solids. TM bands 1, 2, 3, 4, and 6 are used with logarithm conductivity in a multiple regression. The assessment of regression equations for a high coefficient of determination (R-squared) and statistical significance is considered. Confidence intervals about the mean regression point are calculated in order to assess the robustness of the regressions used for mapping conductivity, turbidity, and suspended solids, and by regressing random subsamples of sites and comparing the resultant range of R-squared, cross validation is conducted.

Finding structure in data using multivariate tree boosting

PubMed Central

Miller, Patrick J.; Lubke, Gitta H.; McArtor, Daniel B.; Bergeman, C. S.

2016-01-01

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001). Our extension, multivariate tree boosting, is a method for nonparametric regression that is useful for identifying important predictors, detecting predictors with nonlinear effects and interactions without specification of such effects, and for identifying predictors that cause two or more outcome variables to covary. We provide the R package ‘mvtboost’ to estimate, tune, and interpret the resulting model, which extends the implementation of univariate boosting in the R package ‘gbm’ (Ridgeway et al., 2015) to continuous, multivariate outcomes. To illustrate the approach, we analyze predictors of psychological well-being (Ryff & Keyes, 1995). Simulations verify that our approach identifies predictors with nonlinear effects and achieves high prediction accuracy, exceeding or matching the performance of (penalized) multivariate multiple regression and multivariate decision trees over a wide range of conditions. PMID:27918183

Effectiveness of strengthened stimulation during acupuncture for the treatment of Bell palsy: a randomized controlled trial.

PubMed

Xu, Sha-bei; Huang, Bo; Zhang, Chen-yan; Du, Peng; Yuan, Qi; Bi, Gui-juan; Zhang, Gui-bin; Xie, Min-jie; Luo, Xiang; Huang, Guang-ying; Wang, Wei

2013-04-02

The traditional Chinese theory of acupuncture emphasizes that the intensity of acupuncture must reach a threshold to generate de qi, which is necessary to achieve the best therapeutic effect. De qi is an internal compound sensation of soreness, tingling, fullness, aching, cool, warmth and heaviness, and a radiating sensation at and around the acupoints. However, the notion that de qi must be achieved for maximum benefit has not been confirmed by modern scientific evidence. We performed a prospective multicentre randomized controlled trial involving patients with Bell palsy. Patients were randomly assigned to the de qi (n = 167) or control (n = 171) group. Both groups received acupuncture: in the de qi group, the needles were manipulated manually until de qi was reached, whereas in the control group, the needles were inserted without any manipulation. All patients received prednisone as a basic treatment. The primary outcome was facial nerve function at month 6. We also assessed disability and quality of life 6 months after randomization. After 6 months, patients in the de qi group had better facial function (adjusted odds ratio [OR] 4.16, 95% confidence interval [CI] 2.23-7.78), better disability assessment (differences of least squares means 9.80, 95% CI 6.29-13.30) and better quality of life (differences of least squares means 29.86, 95% CI 22.33-37.38). Logistic regression analysis showed a positive effect of the de qi score on facial-nerve function (adjusted OR 1.07, 95% CI 1.04-1.09). Among patients with Bell palsy, acupuncture with strong stimulation that elicited de qi had a greater therapeutic effect, and stronger intensity of de qi was associated with the better therapeutic effects. Clinicaltrials.gov no. NCT00685789.

How to derive biological information from the value of the normalization constant in allometric equations.

PubMed

Kaitaniemi, Pekka

2008-04-09

Allometric equations are widely used in many branches of biological science. The potential information content of the normalization constant b in allometric equations of the form Y = bX(a) has, however, remained largely neglected. To demonstrate the potential for utilizing this information, I generated a large number of artificial datasets that resembled those that are frequently encountered in biological studies, i.e., relatively small samples including measurement error or uncontrolled variation. The value of X was allowed to vary randomly within the limits describing different data ranges, and a was set to a fixed theoretical value. The constant b was set to a range of values describing the effect of a continuous environmental variable. In addition, a normally distributed random error was added to the values of both X and Y. Two different approaches were then used to model the data. The traditional approach estimated both a and b using a regression model, whereas an alternative approach set the exponent a at its theoretical value and only estimated the value of b. Both approaches produced virtually the same model fit with less than 0.3% difference in the coefficient of determination. Only the alternative approach was able to precisely reproduce the effect of the environmental variable, which was largely lost among noise variation when using the traditional approach. The results show how the value of b can be used as a source of valuable biological information if an appropriate regression model is selected.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Social relationships and longitudinal changes in body mass index and waist circumference: the coronary artery risk development in young adults study.

PubMed

Kershaw, Kiarri N; Hankinson, Arlene L; Liu, Kiang; Reis, Jared P; Lewis, Cora E; Loria, Catherine M; Carnethon, Mercedes R

2014-03-01

Few studies have examined longitudinal associations between close social relationships and weight change. Using data from 3,074 participants in the Coronary Artery Risk Development in Young Adults Study who were examined in 2000, 2005, and 2010 (at ages 33-45 years in 2000), we estimated separate logistic regression random-effects models to assess whether patterns of exposure to supportive and negative relationships were associated with 10% or greater increases in body mass index (BMI) (weight (kg)/height (m)(2)) and waist circumference. Linear regression random-effects modeling was used to examine associations of social relationships with mean changes in BMI and waist circumference. Participants with persistently high supportive relationships were significantly less likely to increase their BMI values and waist circumference by 10% or greater compared with those with persistently low supportive relationships after adjustment for sociodemographic characteristics, baseline BMI/waist circumference, depressive symptoms, and health behaviors. Persistently high negative relationships were associated with higher likelihood of 10% or greater increases in waist circumference (odds ratio = 1.62, 95% confidence interval: 1.15, 2.29) and marginally higher BMI increases (odds ratio = 1.50, 95% confidence interval: 1.00, 2.24) compared with participants with persistently low negative relationships. Increasingly negative relationships were associated with increases in waist circumference only. These findings suggest that supportive relationships may minimize weight gain, and that adverse relationships may contribute to weight gain, particularly via central fat accumulation.