Factors associated with parasite dominance in fishes from Brazil.

PubMed

Amarante, Cristina Fernandes do; Tassinari, Wagner de Souza; Luque, Jose Luis; Pereira, Maria Julia Salim

2016-06-14

The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.

Applications of MIDAS regression in analysing trends in water quality

NASA Astrophysics Data System (ADS)

Penev, Spiridon; Leonte, Daniela; Lazarov, Zdravetz; Mann, Rob A.

2014-04-01

We discuss novel statistical methods in analysing trends in water quality. Such analysis uses complex data sets of different classes of variables, including water quality, hydrological and meteorological. We analyse the effect of rainfall and flow on trends in water quality utilising a flexible model called Mixed Data Sampling (MIDAS). This model arises because of the mixed frequency in the data collection. Typically, water quality variables are sampled fortnightly, whereas the rain data is sampled daily. The advantage of using MIDAS regression is in the flexible and parsimonious modelling of the influence of the rain and flow on trends in water quality variables. We discuss the model and its implementation on a data set from the Shoalhaven Supply System and Catchments in the state of New South Wales, Australia. Information criteria indicate that MIDAS modelling improves upon simplistic approaches that do not utilise the mixed data sampling nature of the data.

Using multilevel modeling to assess case-mix adjusters in consumer experience surveys in health care.

PubMed

Damman, Olga C; Stubbe, Janine H; Hendriks, Michelle; Arah, Onyebuchi A; Spreeuwenberg, Peter; Delnoij, Diana M J; Groenewegen, Peter P

2009-04-01

Ratings on the quality of healthcare from the consumer's perspective need to be adjusted for consumer characteristics to ensure fair and accurate comparisons between healthcare providers or health plans. Although multilevel analysis is already considered an appropriate method for analyzing healthcare performance data, it has rarely been used to assess case-mix adjustment of such data. The purpose of this article is to investigate whether multilevel regression analysis is a useful tool to detect case-mix adjusters in consumer assessment of healthcare. We used data on 11,539 consumers from 27 Dutch health plans, which were collected using the Dutch Consumer Quality Index health plan instrument. We conducted multilevel regression analyses of consumers' responses nested within health plans to assess the effects of consumer characteristics on consumer experience. We compared our findings to the results of another methodology: the impact factor approach, which combines the predictive effect of each case-mix variable with its heterogeneity across health plans. Both multilevel regression and impact factor analyses showed that age and education were the most important case-mix adjusters for consumer experience and ratings of health plans. With the exception of age, case-mix adjustment had little impact on the ranking of health plans. On both theoretical and practical grounds, multilevel modeling is useful for adequate case-mix adjustment and analysis of performance ratings.

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...

Confidence Intervals for Assessing Heterogeneity in Generalized Linear Mixed Models

ERIC Educational Resources Information Center

Wagler, Amy E.

2014-01-01

Generalized linear mixed models are frequently applied to data with clustered categorical outcomes. The effect of clustering on the response is often difficult to practically assess partly because it is reported on a scale on which comparisons with regression parameters are difficult to make. This article proposes confidence intervals for…

Bias and uncertainty of δ13CO2 isotopic mixing models

Treesearch

Zachary E. Kayler; Lisa Ganio; Mark Hauck; Thomas G. Pypker; Elizabeth W. Sulzman; Alan C. Mix; Barbara J. Bond

2009-01-01

The goal of this study was to evaluate how factorial combinations of two mixing models and two regression approaches (Keeling-OLS, Miller—Tans-OLS, Keeling-GMR, Miller—Tans-GMR) compare in small [CO2] range versus large[CO2] range regimes, with different combinations of...

Semiparametric regression during 2003–2007*

PubMed Central

Ruppert, David; Wand, M.P.; Carroll, Raymond J.

2010-01-01

Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application. PMID:20305800

Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA.

PubMed

Zhao, Xin; Han, Meng; Ding, Lili; Calin, Adrian Cantemir

2018-01-01

The accurate forecast of carbon dioxide emissions is critical for policy makers to take proper measures to establish a low carbon society. This paper discusses a hybrid of the mixed data sampling (MIDAS) regression model and BP (back propagation) neural network (MIDAS-BP model) to forecast carbon dioxide emissions. Such analysis uses mixed frequency data to study the effects of quarterly economic growth on annual carbon dioxide emissions. The forecasting ability of MIDAS-BP is remarkably better than MIDAS, ordinary least square (OLS), polynomial distributed lags (PDL), autoregressive distributed lags (ADL), and auto-regressive moving average (ARMA) models. The MIDAS-BP model is suitable for forecasting carbon dioxide emissions for both the short and longer term. This research is expected to influence the methodology for forecasting carbon dioxide emissions by improving the forecast accuracy. Empirical results show that economic growth has both negative and positive effects on carbon dioxide emissions that last 15 quarters. Carbon dioxide emissions are also affected by their own change within 3 years. Therefore, there is a need for policy makers to explore an alternative way to develop the economy, especially applying new energy policies to establish a low carbon society.

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

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

Regression Analysis of Mixed Recurrent-Event and Panel-Count Data with Additive Rate Models

PubMed Central

Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L.

2015-01-01

Summary Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007; Zhao et al., 2011). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013). In this paper, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. PMID:25345405

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.

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.

Using existing case-mix methods to fund trauma cases.

PubMed

Monakova, Julia; Blais, Irene; Botz, Charles; Chechulin, Yuriy; Picciano, Gino; Basinski, Antoni

2010-01-01

Policymakers frequently face the need to increase funding in isolated and frequently heterogeneous (clinically and in terms of resource consumption) patient subpopulations. This article presents a methodologic solution for testing the appropriateness of using existing grouping and weighting methodologies for funding subsets of patients in the scenario where a case-mix approach is preferable to a flat-rate based payment system. Using as an example the subpopulation of trauma cases of Ontario lead trauma hospitals, the statistical techniques of linear and nonlinear regression models, regression trees, and spline models were applied to examine the fit of the existing case-mix groups and reference weights for the trauma cases. The analyses demonstrated that for funding Ontario trauma cases, the existing case-mix systems can form the basis for rational and equitable hospital funding, decreasing the need to develop a different grouper for this subset of patients. This study confirmed that Injury Severity Score is a poor predictor of costs for trauma patients. Although our analysis used the Canadian case-mix classification system and cost weights, the demonstrated concept of using existing case-mix systems to develop funding rates for specific subsets of patient populations may be applicable internationally.

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.

The use of copulas to practical estimation of multivariate stochastic differential equation mixed effects models

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

Rupšys, P.

A system of stochastic differential equations (SDE) with mixed-effects parameters and multivariate normal copula density function were used to develop tree height model for Scots pine trees in Lithuania. A two-step maximum likelihood parameter estimation method is used and computational guidelines are given. After fitting the conditional probability density functions to outside bark diameter at breast height, and total tree height, a bivariate normal copula distribution model was constructed. Predictions from the mixed-effects parameters SDE tree height model calculated during this research were compared to the regression tree height equations. The results are implemented in the symbolic computational language MAPLE.

Comparison and Contrast of Two General Functional Regression Modeling Frameworks

PubMed Central

Morris, Jeffrey S.

2017-01-01

In this article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modeling framework. Over the past number of years, my collaborators and I have also been developing a general framework for functional regression, functional mixed models, which shares many similarities with this framework, but has many differences as well. In this discussion, I compare and contrast these two frameworks, to hopefully illuminate characteristics of each, highlighting their respecitve strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable. PMID:28736502

Comparison and Contrast of Two General Functional Regression Modeling Frameworks.

PubMed

Morris, Jeffrey S

2017-02-01

In this article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modeling framework. Over the past number of years, my collaborators and I have also been developing a general framework for functional regression, functional mixed models, which shares many similarities with this framework, but has many differences as well. In this discussion, I compare and contrast these two frameworks, to hopefully illuminate characteristics of each, highlighting their respecitve strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable.

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions.

PubMed

Fischer, A; Friggens, N C; Berry, D P; Faverdin, P

2018-07-01

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

Penalized nonparametric scalar-on-function regression via principal coordinates

PubMed Central

Reiss, Philip T.; Miller, David L.; Wu, Pei-Shien; Hua, Wen-Yu

2016-01-01

A number of classical approaches to nonparametric regression have recently been extended to the case of functional predictors. This paper introduces a new method of this type, which extends intermediate-rank penalized smoothing to scalar-on-function regression. In the proposed method, which we call principal coordinate ridge regression, one regresses the response on leading principal coordinates defined by a relevant distance among the functional predictors, while applying a ridge penalty. Our publicly available implementation, based on generalized additive modeling software, allows for fast optimal tuning parameter selection and for extensions to multiple functional predictors, exponential family-valued responses, and mixed-effects models. In an application to signature verification data, principal coordinate ridge regression, with dynamic time warping distance used to define the principal coordinates, is shown to outperform a functional generalized linear model. PMID:29217963

Strengthen forensic entomology in court--the need for data exploration and the validation of a generalised additive mixed model.

PubMed

Baqué, Michèle; Amendt, Jens

2013-01-01

Developmental data of juvenile blow flies (Diptera: Calliphoridae) are typically used to calculate the age of immature stages found on or around a corpse and thus to estimate a minimum post-mortem interval (PMI(min)). However, many of those data sets don't take into account that immature blow flies grow in a non-linear fashion. Linear models do not supply a sufficient reliability on age estimates and may even lead to an erroneous determination of the PMI(min). According to the Daubert standard and the need for improvements in forensic science, new statistic tools like smoothing methods and mixed models allow the modelling of non-linear relationships and expand the field of statistical analyses. The present study introduces into the background and application of these statistical techniques by analysing a model which describes the development of the forensically important blow fly Calliphora vicina at different temperatures. The comparison of three statistical methods (linear regression, generalised additive modelling and generalised additive mixed modelling) clearly demonstrates that only the latter provided regression parameters that reflect the data adequately. We focus explicitly on both the exploration of the data--to assure their quality and to show the importance of checking it carefully prior to conducting the statistical tests--and the validation of the resulting models. Hence, we present a common method for evaluating and testing forensic entomological data sets by using for the first time generalised additive mixed models.

Regression analysis using dependent Polya trees.

PubMed

Schörgendorfer, Angela; Branscum, Adam J

2013-11-30

Many commonly used models for linear regression analysis force overly simplistic shape and scale constraints on the residual structure of data. We propose a semiparametric Bayesian model for regression analysis that produces data-driven inference by using a new type of dependent Polya tree prior to model arbitrary residual distributions that are allowed to evolve across increasing levels of an ordinal covariate (e.g., time, in repeated measurement studies). By modeling residual distributions at consecutive covariate levels or time points using separate, but dependent Polya tree priors, distributional information is pooled while allowing for broad pliability to accommodate many types of changing residual distributions. We can use the proposed dependent residual structure in a wide range of regression settings, including fixed-effects and mixed-effects linear and nonlinear models for cross-sectional, prospective, and repeated measurement data. A simulation study illustrates the flexibility of our novel semiparametric regression model to accurately capture evolving residual distributions. In an application to immune development data on immunoglobulin G antibodies in children, our new model outperforms several contemporary semiparametric regression models based on a predictive model selection criterion. Copyright © 2013 John Wiley & Sons, Ltd.

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.

Regression analysis of mixed recurrent-event and panel-count data with additive rate models.

PubMed

Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L

2015-03-01

Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study. © 2014, The International Biometric Society.

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

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.

[Primary branch size of Pinus koraiensis plantation: a prediction based on linear mixed effect model].

PubMed

Dong, Ling-Bo; Liu, Zhao-Gang; Li, Feng-Ri; Jiang, Li-Chun

2013-09-01

By using the branch analysis data of 955 standard branches from 60 sampled trees in 12 sampling plots of Pinus koraiensis plantation in Mengjiagang Forest Farm in Heilongjiang Province of Northeast China, and based on the linear mixed-effect model theory and methods, the models for predicting branch variables, including primary branch diameter, length, and angle, were developed. Considering tree effect, the MIXED module of SAS software was used to fit the prediction models. The results indicated that the fitting precision of the models could be improved by choosing appropriate random-effect parameters and variance-covariance structure. Then, the correlation structures including complex symmetry structure (CS), first-order autoregressive structure [AR(1)], and first-order autoregressive and moving average structure [ARMA(1,1)] were added to the optimal branch size mixed-effect model. The AR(1) improved the fitting precision of branch diameter and length mixed-effect model significantly, but all the three structures didn't improve the precision of branch angle mixed-effect model. In order to describe the heteroscedasticity during building mixed-effect model, the CF1 and CF2 functions were added to the branch mixed-effect model. CF1 function improved the fitting effect of branch angle mixed model significantly, whereas CF2 function improved the fitting effect of branch diameter and length mixed model significantly. Model validation confirmed that the mixed-effect model could improve the precision of prediction, as compare to the traditional regression model for the branch size prediction of Pinus koraiensis plantation.

MANOVA vs nonlinear mixed effects modeling: The comparison of growth patterns of female and male quail

NASA Astrophysics Data System (ADS)

Gürcan, Eser Kemal

2017-04-01

The most commonly used methods for analyzing time-dependent data are multivariate analysis of variance (MANOVA) and nonlinear regression models. The aim of this study was to compare some MANOVA techniques and nonlinear mixed modeling approach for investigation of growth differentiation in female and male Japanese quail. Weekly individual body weight data of 352 male and 335 female quail from hatch to 8 weeks of age were used to perform analyses. It is possible to say that when all the analyses are evaluated, the nonlinear mixed modeling is superior to the other techniques because it also reveals the individual variation. In addition, the profile analysis also provides important information.

OPC modeling by genetic algorithm

NASA Astrophysics Data System (ADS)

Huang, W. C.; Lai, C. M.; Luo, B.; Tsai, C. K.; Tsay, C. S.; Lai, C. W.; Kuo, C. C.; Liu, R. G.; Lin, H. T.; Lin, B. J.

2005-05-01

Optical proximity correction (OPC) is usually used to pre-distort mask layouts to make the printed patterns as close to the desired shapes as possible. For model-based OPC, a lithographic model to predict critical dimensions after lithographic processing is needed. The model is usually obtained via a regression of parameters based on experimental data containing optical proximity effects. When the parameters involve a mix of the continuous (optical and resist models) and the discrete (kernel numbers) sets, the traditional numerical optimization method may have difficulty handling model fitting. In this study, an artificial-intelligent optimization method was used to regress the parameters of the lithographic models for OPC. The implemented phenomenological models were constant-threshold models that combine diffused aerial image models with loading effects. Optical kernels decomposed from Hopkin"s equation were used to calculate aerial images on the wafer. Similarly, the numbers of optical kernels were treated as regression parameters. This way, good regression results were obtained with different sets of optical proximity effect data.

Impact of case-mix on comparisons of patient-reported experience in NHS acute hospital trusts in England.

PubMed

Raleigh, Veena; Sizmur, Steve; Tian, Yang; Thompson, James

2015-04-01

To examine the impact of patient-mix on National Health Service (NHS) acute hospital trust scores in two national NHS patient surveys. Secondary analysis of 2012 patient survey data for 57,915 adult inpatients at 142 NHS acute hospital trusts and 45,263 adult emergency department attendees at 146 NHS acute hospital trusts in England. Changes in trust scores for selected questions, ranks, inter-trust variance and score-based performance bands were examined using three methods: no adjustment for case-mix; the current standardization method with weighting for age, sex and, for inpatients only, admission method; and a regression model adjusting in addition for ethnicity, presence of a long-term condition, proxy response (inpatients only) and previous emergency attendances (emergency department survey only). For both surveys, all the variables examined were associated with patients' responses and affected inter-trust variance in scores, although the direction and strength of impact differed between variables. Inter-trust variance was generally greatest for the unadjusted scores and lowest for scores derived from the full regression model. Although trust scores derived from the three methods were highly correlated (Kendall's tau coefficients 0.70-0.94), up to 14% of trusts had discordant ranks of when the standardization and regression methods were compared. Depending on the survey and question, up to 14 trusts changed performance bands when the regression model with its fuller case-mix adjustment was used rather than the current standardization method. More comprehensive case-mix adjustment of patient survey data than the current limited adjustment reduces performance variation between NHS acute hospital trusts and alters the comparative performance bands of some trusts. Given the use of these data for high-impact purposes such as performance assessment, regulation, commissioning, quality improvement and patient choice, a review of the long-standing method for analysing patient survey data would be timely, and could improve rigour and comparability across the NHS. Performance comparisons need to be perceived as fair and scientifically robust to maintain confidence in publicly reported data, and to support their use by both the public and the NHS. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Assessing Discriminative Performance at External Validation of Clinical Prediction Models

PubMed Central

Nieboer, Daan; van der Ploeg, Tjeerd; Steyerberg, Ewout W.

2016-01-01

Introduction External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting. Methods We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury. Results The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2. Conclusion The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients. PMID:26881753

Assessing Discriminative Performance at External Validation of Clinical Prediction Models.

PubMed

Nieboer, Daan; van der Ploeg, Tjeerd; Steyerberg, Ewout W

2016-01-01

External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting. We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury. The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2. The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.

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.

Modeling stream network-scale variation in coho salmon overwinter survival and smolt size

EPA Science Inventory

We used multiple regression and hierarchical mixed-effects models to examine spatial patterns of overwinter survival and size at smolting in juvenile coho salmon Oncorhynchus kisutch in relation to habitat attributes across an extensive stream network in southwestern Oregon over ...

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

PubMed

Stanley, T D; Doucouliagos, Hristos

2017-03-01

Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Spatial regression methods capture prediction uncertainty in species distribution model projections through time

Treesearch

Alan K. Swanson; Solomon Z. Dobrowski; Andrew O. Finley; James H. Thorne; Michael K. Schwartz

2013-01-01

The uncertainty associated with species distribution model (SDM) projections is poorly characterized, despite its potential value to decision makers. Error estimates from most modelling techniques have been shown to be biased due to their failure to account for spatial autocorrelation (SAC) of residual error. Generalized linear mixed models (GLMM) have the ability to...

Solving large mixed linear models using preconditioned conjugate gradient iteration.

PubMed

Strandén, I; Lidauer, M

1999-12-01

Continuous evaluation of dairy cattle with a random regression test-day model requires a fast solving method and algorithm. A new computing technique feasible in Jacobi and conjugate gradient based iterative methods using iteration on data is presented. In the new computing technique, the calculations in multiplication of a vector by a matrix were recorded to three steps instead of the commonly used two steps. The three-step method was implemented in a general mixed linear model program that used preconditioned conjugate gradient iteration. Performance of this program in comparison to other general solving programs was assessed via estimation of breeding values using univariate, multivariate, and random regression test-day models. Central processing unit time per iteration with the new three-step technique was, at best, one-third that needed with the old technique. Performance was best with the test-day model, which was the largest and most complex model used. The new program did well in comparison to other general software. Programs keeping the mixed model equations in random access memory required at least 20 and 435% more time to solve the univariate and multivariate animal models, respectively. Computations of the second best iteration on data took approximately three and five times longer for the animal and test-day models, respectively, than did the new program. Good performance was due to fast computing time per iteration and quick convergence to the final solutions. Use of preconditioned conjugate gradient based methods in solving large breeding value problems is supported by our findings.

Testing homogeneity in Weibull-regression models.

PubMed

Bolfarine, Heleno; Valença, Dione M

2005-10-01

In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.

The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges.

PubMed

Phillips, Charles D

2015-01-01

Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges.

The Pediatric Home Care/Expenditure Classification Model (P/ECM): A Home Care Case-Mix Model for Children Facing Special Health Care Challenges

PubMed Central

Phillips, Charles D.

2015-01-01

Case-mix classification and payment systems help assure that persons with similar needs receive similar amounts of care resources, which is a major equity concern for consumers, providers, and programs. Although health service programs for adults regularly use case-mix payment systems, programs providing health services to children and youth rarely use such models. This research utilized Medicaid home care expenditures and assessment data on 2,578 children receiving home care in one large state in the USA. Using classification and regression tree analyses, a case-mix model for long-term pediatric home care was developed. The Pediatric Home Care/Expenditure Classification Model (P/ECM) grouped children and youth in the study sample into 24 groups, explaining 41% of the variance in annual home care expenditures. The P/ECM creates the possibility of a more equitable, and potentially more effective, allocation of home care resources among children and youth facing serious health care challenges. PMID:26740744

Growth and inactivation of Salmonella at low refrigerated storage temperatures and thermal inactivation on raw chicken meat and laboratory media: mixed effect meta-analysis.

PubMed

Smadi, Hanan; Sargeant, Jan M; Shannon, Harry S; Raina, Parminder

2012-12-01

Growth and inactivation regression equations were developed to describe the effects of temperature on Salmonella concentration on chicken meat for refrigerated temperatures (⩽10°C) and for thermal treatment temperatures (55-70°C). The main objectives were: (i) to compare Salmonella growth/inactivation in chicken meat versus laboratory media; (ii) to create regression equations to estimate Salmonella growth in chicken meat that can be used in quantitative risk assessment (QRA) modeling; and (iii) to create regression equations to estimate D-values needed to inactivate Salmonella in chicken meat. A systematic approach was used to identify the articles, critically appraise them, and pool outcomes across studies. Growth represented in density (Log10CFU/g) and D-values (min) as a function of temperature were modeled using hierarchical mixed effects regression models. The current meta-analysis analysis found a significant difference (P⩽0.05) between the two matrices - chicken meat and laboratory media - for both growth at refrigerated temperatures and inactivation by thermal treatment. Growth and inactivation were significantly influenced by temperature after controlling for other variables; however, no consistent pattern in growth was found. Validation of growth and inactivation equations against data not used in their development is needed. Copyright © 2012 Ministry of Health, Saudi Arabia. Published by Elsevier Ltd. All rights reserved.

Improving Lidar-based Aboveground Biomass Estimation with Site Productivity for Central Hardwood Forests, USA

NASA Astrophysics Data System (ADS)

Shao, G.; Gallion, J.; Fei, S.

2016-12-01

Sound forest aboveground biomass estimation is required to monitor diverse forest ecosystems and their impacts on the changing climate. Lidar-based regression models provided promised biomass estimations in most forest ecosystems. However, considerable uncertainties of biomass estimations have been reported in the temperate hardwood and hardwood-dominated mixed forests. Varied site productivities in temperate hardwood forests largely diversified height and diameter growth rates, which significantly reduced the correlation between tree height and diameter at breast height (DBH) in mature and complex forests. It is, therefore, difficult to utilize height-based lidar metrics to predict DBH-based field-measured biomass through a simple regression model regardless the variation of site productivity. In this study, we established a multi-dimension nonlinear regression model incorporating lidar metrics and site productivity classes derived from soil features. In the regression model, lidar metrics provided horizontal and vertical structural information and productivity classes differentiated good and poor forest sites. The selection and combination of lidar metrics were discussed. Multiple regression models were employed and compared. Uncertainty analysis was applied to the best fit model. The effects of site productivity on the lidar-based biomass model were addressed.

Multivariate statistical approach to estimate mixing proportions for unknown end members

USGS Publications Warehouse

Valder, Joshua F.; Long, Andrew J.; Davis, Arden D.; Kenner, Scott J.

2012-01-01

A multivariate statistical method is presented, which includes principal components analysis (PCA) and an end-member mixing model to estimate unknown end-member hydrochemical compositions and the relative mixing proportions of those end members in mixed waters. PCA, together with the Hotelling T2 statistic and a conceptual model of groundwater flow and mixing, was used in selecting samples that best approximate end members, which then were used as initial values in optimization of the end-member mixing model. This method was tested on controlled datasets (i.e., true values of estimates were known a priori) and found effective in estimating these end members and mixing proportions. The controlled datasets included synthetically generated hydrochemical data, synthetically generated mixing proportions, and laboratory analyses of sample mixtures, which were used in an evaluation of the effectiveness of this method for potential use in actual hydrological settings. For three different scenarios tested, correlation coefficients (R2) for linear regression between the estimated and known values ranged from 0.968 to 0.993 for mixing proportions and from 0.839 to 0.998 for end-member compositions. The method also was applied to field data from a study of end-member mixing in groundwater as a field example and partial method validation.

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.

Safety analysis of urban signalized intersections under mixed traffic.

PubMed

S, Anjana; M V L R, Anjaneyulu

2015-02-01

This study examined the crash causative factors of signalized intersections under mixed traffic using advanced statistical models. Hierarchical Poisson regression and logistic regression models were developed to predict the crash frequency and severity of signalized intersection approaches. The prediction models helped to develop general safety countermeasures for signalized intersections. The study shows that exclusive left turn lanes and countdown timers are beneficial for improving the safety of signalized intersections. Safety is also influenced by the presence of a surveillance camera, green time, median width, traffic volume, and proportion of two wheelers in the traffic stream. The factors that influence the severity of crashes were also identified in this study. As a practical application, the safe values of deviation of green time provided from design green time, with varying traffic volume, is presented in this study. This is a useful tool for setting the appropriate green time for a signalized intersection approach with variations in the traffic volume. Copyright © 2014 Elsevier Ltd. All rights reserved.

Penalized spline estimation for functional coefficient regression models.

PubMed

Cao, Yanrong; Lin, Haiqun; Wu, Tracy Z; Yu, Yan

2010-04-01

The functional coefficient regression models assume that the regression coefficients vary with some "threshold" variable, providing appreciable flexibility in capturing the underlying dynamics in data and avoiding the so-called "curse of dimensionality" in multivariate nonparametric estimation. We first investigate the estimation, inference, and forecasting for the functional coefficient regression models with dependent observations via penalized splines. The P-spline approach, as a direct ridge regression shrinkage type global smoothing method, is computationally efficient and stable. With established fixed-knot asymptotics, inference is readily available. Exact inference can be obtained for fixed smoothing parameter λ, which is most appealing for finite samples. Our penalized spline approach gives an explicit model expression, which also enables multi-step-ahead forecasting via simulations. Furthermore, we examine different methods of choosing the important smoothing parameter λ: modified multi-fold cross-validation (MCV), generalized cross-validation (GCV), and an extension of empirical bias bandwidth selection (EBBS) to P-splines. In addition, we implement smoothing parameter selection using mixed model framework through restricted maximum likelihood (REML) for P-spline functional coefficient regression models with independent observations. The P-spline approach also easily allows different smoothness for different functional coefficients, which is enabled by assigning different penalty λ accordingly. We demonstrate the proposed approach by both simulation examples and a real data application.

Robust and efficient estimation with weighted composite quantile regression

NASA Astrophysics Data System (ADS)

Jiang, Xuejun; Li, Jingzhi; Xia, Tian; Yan, Wanfeng

2016-09-01

In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH-type models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator (MLE) for a variety of error distributions including the normal, mixed-normal, Student's t, Cauchy distributions, etc. We also suggest an algorithm for the fast implementation of the proposed methodology. Simulations are carried out to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which verifies the effectiveness and efficiency of our theoretical results.

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.

Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution

ERIC Educational Resources Information Center

Verkuilen, Jay; Smithson, Michael

2012-01-01

Doubly bounded continuous data are common in the social and behavioral sciences. Examples include judged probabilities, confidence ratings, derived proportions such as percent time on task, and bounded scale scores. Dependent variables of this kind are often difficult to analyze using normal theory models because their distributions may be quite…

A Growth Model for Academic Program Life Cycle (APLC): A Theoretical and Empirical Analysis

ERIC Educational Resources Information Center

Acquah, Edward H. K.

2010-01-01

Academic program life cycle concept states each program's life flows through several stages: introduction, growth, maturity, and decline. A mixed-influence diffusion growth model is fitted to enrolment data on academic programs to analyze the factors determining progress of academic programs through their life cycles. The regression analysis yield…

Modeling stream network-scale variation in Coho salmon overwinter survival and smolt size

Treesearch

Joseph L. Ebersole; Mike E. Colvin; Parker J. Wigington; Scott G. Leibowitz; Joan P. Baker; Jana E. Compton; Bruce A. Miller; Michael A. Carins; Bruce P. Hansen; Henry R. La Vigne

2009-01-01

We used multiple regression and hierarchical mixed-effects models to examine spatial patterns of overwinter survival and size at smolting in juvenile coho salmon Oncorhynchus kisutch in relation to habitat attributes across an extensive stream network in southwestern Oregon over 3 years. Contributing basin area explained the majority of spatial...

[Spatial heterogeneity in body condition of small yellow croaker in Yellow Sea and East China Sea based on mixed-effects model and quantile regression analysis].

PubMed

Liu, Zun-Lei; Yuan, Xing-Wei; Yan, Li-Ping; Yang, Lin-Lin; Cheng, Jia-Hua

2013-09-01

By using the 2008-2010 investigation data about the body condition of small yellow croaker in the offshore waters of southern Yellow Sea (SYS), open waters of northern East China Sea (NECS), and offshore waters of middle East China Sea (MECS), this paper analyzed the spatial heterogeneity of body length-body mass of juvenile and adult small yellow croakers by the statistical approaches of mean regression model and quantile regression model. The results showed that the residual standard errors from the analysis of covariance (ANCOVA) and the linear mixed-effects model were similar, and those from the simple linear regression were the highest. For the juvenile small yellow croakers, their mean body mass in SYS and NECS estimated by the mixed-effects mean regression model was higher than the overall average mass across the three regions, while the mean body mass in MECS was below the overall average. For the adult small yellow croakers, their mean body mass in NECS was higher than the overall average, while the mean body mass in SYS and MECS was below the overall average. The results from quantile regression indicated the substantial differences in the allometric relationships of juvenile small yellow croakers between SYS, NECS, and MECS, with the estimated mean exponent of the allometric relationship in SYS being 2.85, and the interquartile range being from 2.63 to 2.96, which indicated the heterogeneity of body form. The results from ANCOVA showed that the allometric body length-body mass relationships were significantly different between the 25th and 75th percentile exponent values (F=6.38, df=1737, P<0.01) and the 25th percentile and median exponent values (F=2.35, df=1737, P=0.039). The relationship was marginally different between the median and 75th percentile exponent values (F=2.21, df=1737, P=0.051). The estimated body length-body mass exponent of adult small yellow croakers in SYS was 3.01 (10th and 95th percentiles = 2.77 and 3.1, respectively). The estimated body length-body mass relationships were significantly different from the lower and upper quantiles of the exponent (F=3.31, df=2793, P=0.01) and the median and upper quantiles (F=3.56, df=2793, P<0.01), while no significant difference was observed between the lower and median quantiles (F=0.98, df=2793, P=0.43).

The Weight of Euro Coins: Its Distribution Might Not Be as Normal as You Would Expect

ERIC Educational Resources Information Center

Shkedy, Ziv; Aerts, Marc; Callaert, Herman

2006-01-01

Classical regression models, ANOVA models and linear mixed models are just three examples (out of many) in which the normal distribution of the response is an essential assumption of the model. In this paper we use a dataset of 2000 euro coins containing information (up to the milligram) about the weight of each coin, to illustrate that the…

[Influence of sample surface roughness on mathematical model of NIR quantitative analysis of wood density].

PubMed

Huang, An-Min; Fei, Ben-Hua; Jiang, Ze-Hui; Hse, Chung-Yun

2007-09-01

Near infrared spectroscopy is widely used as a quantitative method, and the main multivariate techniques consist of regression methods used to build prediction models, however, the accuracy of analysis results will be affected by many factors. In the present paper, the influence of different sample roughness on the mathematical model of NIR quantitative analysis of wood density was studied. The result of experiments showed that if the roughness of predicted samples was consistent with that of calibrated samples, the result was good, otherwise the error would be much higher. The roughness-mixed model was more flexible and adaptable to different sample roughness. The prediction ability of the roughness-mixed model was much better than that of the single-roughness model.

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.

Prediction of forest fires occurrences with area-level Poisson mixed models.

PubMed

Boubeta, Miguel; Lombardía, María José; Marey-Pérez, Manuel Francisco; Morales, Domingo

2015-05-01

The number of fires in forest areas of Galicia (north-west of Spain) during the summer period is quite high. Local authorities are interested in analyzing the factors that explain this phenomenon. Poisson regression models are good tools for describing and predicting the number of fires per forest areas. This work employs area-level Poisson mixed models for treating real data about fires in forest areas. A parametric bootstrap method is applied for estimating the mean squared errors of fires predictors. The developed methodology and software are applied to a real data set of fires in forest areas of Galicia. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

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 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.

Empirical Behavioral Models to Support Alternative Tools for the Analysis of Mixed-Priority Pedestrian-Vehicle Interaction in a Highway Capacity Context

PubMed Central

Rouphail, Nagui M.

2011-01-01

This paper presents behavioral-based models for describing pedestrian gap acceptance at unsignalized crosswalks in a mixed-priority environment, where some drivers yield and some pedestrians cross in gaps. Logistic regression models are developed to predict the probability of pedestrian crossings as a function of vehicle dynamics, pedestrian assertiveness, and other factors. In combination with prior work on probabilistic yielding models, the results can be incorporated in a simulation environment, where they can more fully describe the interaction of these two modes. The approach is intended to supplement HCM analytical procedure for locations where significant interaction occurs between drivers and pedestrians, including modern roundabouts. PMID:21643488

Variability in case-mix adjusted in-hospital cardiac arrest rates.

PubMed

Merchant, Raina M; Yang, Lin; Becker, Lance B; Berg, Robert A; Nadkarni, Vinay; Nichol, Graham; Carr, Brendan G; Mitra, Nandita; Bradley, Steven M; Abella, Benjamin S; Groeneveld, Peter W

2012-02-01

It is unknown how in-hospital cardiac arrest (IHCA) rates vary across hospitals and predictors of variability. Measure variability in IHCA across hospitals and determine if hospital-level factors predict differences in case-mix adjusted event rates. Get with the Guidelines Resuscitation (GWTG-R) (n=433 hospitals) was used to identify IHCA events between 2003 and 2007. The American Hospital Association survey, Medicare, and US Census were used to obtain detailed information about GWTG-R hospitals. Adult patients with IHCA. Case-mix-adjusted predicted IHCA rates were calculated for each hospital and variability across hospitals was compared. A regression model was used to predict case-mix adjusted event rates using hospital measures of volume, nurse-to-bed ratio, percent intensive care unit beds, palliative care services, urban designation, volume of black patients, income, trauma designation, academic designation, cardiac surgery capability, and a patient risk score. We evaluated 103,117 adult IHCAs at 433 US hospitals. The case-mix adjusted IHCA event rate was highly variable across hospitals, median 1/1000 bed days (interquartile range: 0.7 to 1.3 events/1000 bed days). In a multivariable regression model, case-mix adjusted IHCA event rates were highest in urban hospitals [rate ratio (RR), 1.1; 95% confidence interval (CI), 1.0-1.3; P=0.03] and hospitals with higher proportions of black patients (RR, 1.2; 95% CI, 1.0-1.3; P=0.01) and lower in larger hospitals (RR, 0.54; 95% CI, 0.45-0.66; P<0.0001). Case-mix adjusted IHCA event rates varied considerably across hospitals. Several hospital factors associated with higher IHCA event rates were consistent with factors often linked with lower hospital quality of care.

Creep analysis of silicone for podiatry applications.

PubMed

Janeiro-Arocas, Julia; Tarrío-Saavedra, Javier; López-Beceiro, Jorge; Naya, Salvador; López-Canosa, Adrián; Heredia-García, Nicolás; Artiaga, Ramón

2016-10-01

This work shows an effective methodology to characterize the creep-recovery behavior of silicones before their application in podiatry. The aim is to characterize, model and compare the creep-recovery properties of different types of silicone used in podiatry orthotics. Creep-recovery phenomena of silicones used in podiatry orthotics is characterized by dynamic mechanical analysis (DMA). Silicones provided by Herbitas are compared by observing their viscoelastic properties by Functional Data Analysis (FDA) and nonlinear regression. The relationship between strain and time is modeled by fixed and mixed effects nonlinear regression to compare easily and intuitively podiatry silicones. Functional ANOVA and Kohlrausch-Willians-Watts (KWW) model with fixed and mixed effects allows us to compare different silicones observing the values of fitting parameters and their physical meaning. The differences between silicones are related to the variations of breadth of creep-recovery time distribution and instantaneous deformation-permanent strain. Nevertheless, the mean creep-relaxation time is the same for all the studied silicones. Silicones used in palliative orthoses have higher instantaneous deformation-permanent strain and narrower creep-recovery distribution. The proposed methodology based on DMA, FDA and nonlinear regression is an useful tool to characterize and choose the proper silicone for each podiatry application according to their viscoelastic properties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, Gábor; Yule, I. J.

2016-07-01

On-farm assessment of mixed pasture nutrient concentrations is important for animal production and pasture management. Hyperspectral imaging is recognized as a potential tool to quantify the nutrient content of vegetation. However, it is a great challenge to estimate macro and micro nutrients in heterogeneous mixed pastures. In this study, canopy reflectance data was measured by using a high resolution airborne visible-to-shortwave infrared (Vis-SWIR) imaging spectrometer measuring in the wavelength region 380-2500 nm to predict nutrient concentrations, nitrogen (N) phosphorus (P), potassium (K), sulfur (S), zinc (Zn), sodium (Na), manganese (Mn) copper (Cu) and magnesium (Mg) in heterogeneous mixed pastures across a sheep and beef farm in hill country, within New Zealand. Prediction models were developed using four different methods which are included partial least squares regression (PLSR), kernel PLSR, support vector regression (SVR), random forest regression (RFR) algorithms and their performance compared using the test data. The results from the study revealed that RFR produced highest accuracy (0.55 ⩽ R2CV ⩽ 0.78; 6.68% ⩽ nRMSECV ⩽ 26.47%) compared to all other algorithms for the majority of nutrients (N, P, K, Zn, Na, Cu and Mg) described, and the remaining nutrients (S and Mn) were predicted with high accuracy (0.68 ⩽ R2CV ⩽ 0.86; 13.00% ⩽ nRMSECV ⩽ 14.64%) using SVR. The best training models were used to extrapolate over the whole farm with the purpose of predicting those pasture nutrients and expressed through pixel based spatial maps. These spatially registered nutrient maps demonstrate the range and geographical location of often large differences in pasture nutrient values which are normally not measured and therefore not included in decision making when considering more effective ways to utilized pasture.

A comparison of methods for the analysis of binomial clustered outcomes in behavioral research.

PubMed

Ferrari, Alberto; Comelli, Mario

2016-12-01

In behavioral research, data consisting of a per-subject proportion of "successes" and "failures" over a finite number of trials often arise. This clustered binary data are usually non-normally distributed, which can distort inference if the usual general linear model is applied and sample size is small. A number of more advanced methods is available, but they are often technically challenging and a comparative assessment of their performances in behavioral setups has not been performed. We studied the performances of some methods applicable to the analysis of proportions; namely linear regression, Poisson regression, beta-binomial regression and Generalized Linear Mixed Models (GLMMs). We report on a simulation study evaluating power and Type I error rate of these models in hypothetical scenarios met by behavioral researchers; plus, we describe results from the application of these methods on data from real experiments. Our results show that, while GLMMs are powerful instruments for the analysis of clustered binary outcomes, beta-binomial regression can outperform them in a range of scenarios. Linear regression gave results consistent with the nominal level of significance, but was overall less powerful. Poisson regression, instead, mostly led to anticonservative inference. GLMMs and beta-binomial regression are generally more powerful than linear regression; yet linear regression is robust to model misspecification in some conditions, whereas Poisson regression suffers heavily from violations of the assumptions when used to model proportion data. We conclude providing directions to behavioral scientists dealing with clustered binary data and small sample sizes. Copyright © 2016 Elsevier B.V. All rights reserved.

Estimating surface pCO2 in the northern Gulf of Mexico: Which remote sensing model to use?

NASA Astrophysics Data System (ADS)

Chen, Shuangling; Hu, Chuanmin; Cai, Wei-Jun; Yang, Bo

2017-12-01

Various approaches and models have been proposed to remotely estimate surface pCO2 in the ocean, with variable performance as they were designed for different environments. Among these, a recently developed mechanistic semi-analytical approach (MeSAA) has shown its advantage for its explicit inclusion of physical and biological forcing in the model, yet its general applicability is unknown. Here, with extensive in situ measurements of surface pCO2, the MeSAA, originally developed for the summertime East China Sea, was tested in the northern Gulf of Mexico (GOM) where river plumes dominate water's biogeochemical properties during summer. Specifically, the MeSAA-predicted surface pCO2 was estimated by combining the dominating effects of thermodynamics, river-ocean mixing and biological activities on surface pCO2. Firstly, effects of thermodynamics and river-ocean mixing (pCO2@Hmixing) were estimated with a two-endmember mixing model, assuming conservative mixing. Secondly, pCO2 variations caused by biological activities (ΔpCO2@bio) was determined through an empirical relationship between sea surface temperature (SST)-normalized pCO2 and MODIS (Moderate Resolution Imaging Spectroradiometer) 8-day composite chlorophyll concentration (CHL). The MeSAA-modeled pCO2 (sum of pCO2@Hmixing and ΔpCO2@bio) was compared with the field-measured pCO2. The Root Mean Square Error (RMSE) was 22.94 μatm (5.91%), with coefficient of determination (R2) of 0.25, mean bias (MB) of - 0.23 μatm and mean ratio (MR) of 1.001, for pCO2 ranging between 316 and 452 μatm. To improve the model performance, a locally tuned MeSAA was developed through the use of a locally tuned ΔpCO2@bio term. A multi-variate empirical regression model was also developed using the same dataset. Both the locally tuned MeSAA and the regression models showed improved performance comparing to the original MeSAA, with R2 of 0.78 and 0.84, RMSE of 12.36 μatm (3.14%) and 10.66 μatm (2.68%), MB of 0.00 μatm and - 0.10 μatm, MR of 1.001 and 1.000, respectively. A sensitivity analysis was conducted to study the uncertainties in the predicted pCO2 as a result of the uncertainties in the input variables of each model. Although the MeSAA was more sensitive to variations in SST and CHL than in sea surface salinity (SSS), and the locally tuned MeSAA and the empirical regression models were more sensitive to changes in SST and SSS than in CHL, generally for these three models the bias induced by the uncertainties in the empirically derived parameters (river endmember total alkalinity (TA) and dissolved inorganic carbon (DIC), biological coefficient of the MeSAA and locally tuned MeSAA models) and environmental variables (SST, SSS, CHL) was within or close to the uncertainty of each model. While all these three models showed that surface pCO2 was positively correlated to SST, the MeSAA showed negative correlation between surface pCO2 and SSS and CHL but the locally tuned MeSAA and the empirical regression showed the opposite. These results suggest that the locally tuned MeSAA worked better in the river-dominated northern GOM than the original MeSAA, with slightly worse statistics but more meaningful physical and biogeochemical interpretations than the empirical regression model. Because data from abnormal upwelling were not used to train the models, they are not applicable for waters with strong upwelling, yet the empirical regression approach showed ability to be further tuned to adapt to such cases.

Cox Regression Models with Functional Covariates for Survival Data.

PubMed

Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M

2015-06-01

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

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.

Solid precipitation measurement intercomparison in Bismarck, North Dakota, from 1988 through 1997

USGS Publications Warehouse

Ryberg, Karen R.; Emerson, Douglas G.; Macek-Rowland, Kathleen M.

2009-01-01

A solid precipitation measurement intercomparison was recommended by the World Meteorological Organization (WMO) and was initiated after approval by the ninth session of the Commission for Instruments and Methods of Observation. The goal of the intercomparison was to assess national methods of measuring solid precipitation against methods whose accuracy and reliability were known. A field study was started in Bismarck, N. Dak., during the 1988-89 winter as part of the intercomparison. The last official field season of the WMO intercomparison was 1992-93; however, the Bismarck site continued to operate through the winter of 1996-97. Precipitation events at Bismarck were categorized as snow, mixed, or rain on the basis of descriptive notes recorded as part of the solid precipitation intercomparison. The rain events were not further analyzed in this study. Catch ratios (CRs) - the ratio of the precipitation catch at each gage to the true precipitation measurement (the corrected double fence intercomparison reference) - were calculated. Then, regression analysis was used to develop equations that model the snow and mixed precipitation CRs at each gage as functions of wind speed and temperature. Wind speed at the gages, functions of temperature, and upper air conditions (wind speed and air temperature at 700 millibars pressure) were used as possible explanatory variables in the multiple regression analysis done for this study. The CRs were modeled by using multiple regression analysis for the Tretyakov gage, national shielded gage, national unshielded gage, AeroChem gage, national gage with double fence, and national gage with Wyoming windshield. As in earlier studies by the WMO, wind speed and air temperature were found to influence the CR of the Tretyakov gage. However, in this study, the temperature variable represented the average upper air temperature over the duration of the event. The WMO did not use upper air conditions in its analysis. The national shielded and unshielded gages where found to be influenced by functions of wind speed only, as in other studies, but the upper air wind speed was used as an explanatory variable in this study. The AeroChem gage was not used in the WMO intercomparison study for 1987-93. The AeroChem gage had a highly varied CR at Bismarck, and a number of variables related to wind speed and temperature were used in the model for the CR. Despite extensive efforts to find a model for the national gage with double fence, no statistically significant regression model was found at the 0.05 level of statistical significance. The national gage with Wyoming windshield had a CR modeled by temperature and wind speed variables, and the regression relation had the highest coefficient of determination (R2 = 0.572) and adjusted coefficient of multiple determination (R2a = 0.476) of all of the models identified for any gage. Three of the gage CRs evaluated could be compared with those in the WMO intercomparison study for 1987-93. The WMO intercomparison had the advantage of a much larger dataset than this study. However, the data in this study represented a longer time period. Snow precipitation catch is highly varied depending on the equipment used and the weather conditions. Much of the variation is not accounted for in the WMO equations or in the equations developed in this study, particularly for unshielded gages. Extensive attempts at regression analysis were made with the mixed precipitation data, but it was concluded that the sample sizes were not large enough to model the CRs. However, the data could be used to test the WMO intercomparison equations. The mixed precipitation equations for the Tretyakov and national shielded gages are similar to those for snow in that they are more likely to underestimate precipitation when observed amounts were small and overestimate precipitation when observed amounts were relatively large. Mixed precipitation is underestimated by the WMO adjustment and t

Comparison of linear and non-linear models for predicting energy expenditure from raw accelerometer data.

PubMed

Montoye, Alexander H K; Begum, Munni; Henning, Zachary; Pfeiffer, Karin A

2017-02-01

This study had three purposes, all related to evaluating energy expenditure (EE) prediction accuracy from body-worn accelerometers: (1) compare linear regression to linear mixed models, (2) compare linear models to artificial neural network models, and (3) compare accuracy of accelerometers placed on the hip, thigh, and wrists. Forty individuals performed 13 activities in a 90 min semi-structured, laboratory-based protocol. Participants wore accelerometers on the right hip, right thigh, and both wrists and a portable metabolic analyzer (EE criterion). Four EE prediction models were developed for each accelerometer: linear regression, linear mixed, and two ANN models. EE prediction accuracy was assessed using correlations, root mean square error (RMSE), and bias and was compared across models and accelerometers using repeated-measures analysis of variance. For all accelerometer placements, there were no significant differences for correlations or RMSE between linear regression and linear mixed models (correlations: r  =  0.71-0.88, RMSE: 1.11-1.61 METs; p  >  0.05). For the thigh-worn accelerometer, there were no differences in correlations or RMSE between linear and ANN models (ANN-correlations: r  =  0.89, RMSE: 1.07-1.08 METs. Linear models-correlations: r  =  0.88, RMSE: 1.10-1.11 METs; p  >  0.05). Conversely, one ANN had higher correlations and lower RMSE than both linear models for the hip (ANN-correlation: r  =  0.88, RMSE: 1.12 METs. Linear models-correlations: r  =  0.86, RMSE: 1.18-1.19 METs; p  <  0.05), and both ANNs had higher correlations and lower RMSE than both linear models for the wrist-worn accelerometers (ANN-correlations: r  =  0.82-0.84, RMSE: 1.26-1.32 METs. Linear models-correlations: r  =  0.71-0.73, RMSE: 1.55-1.61 METs; p  <  0.01). For studies using wrist-worn accelerometers, machine learning models offer a significant improvement in EE prediction accuracy over linear models. Conversely, linear models showed similar EE prediction accuracy to machine learning models for hip- and thigh-worn accelerometers and may be viable alternative modeling techniques for EE prediction for hip- or thigh-worn accelerometers.

The use of cognitive ability measures as explanatory variables in regression analysis.

PubMed

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2012-12-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual's wage, or a decision such as an individual's education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score , constructed via standard psychometric practice from individuals' responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a "mixed effects structural equations" (MESE) model, may be more appropriate in many circumstances.

Boosting structured additive quantile regression for longitudinal childhood obesity data.

PubMed

Fenske, Nora; Fahrmeir, Ludwig; Hothorn, Torsten; Rzehak, Peter; Höhle, Michael

2013-07-25

Childhood obesity and the investigation of its risk factors has become an important public health issue. Our work is based on and motivated by a German longitudinal study including 2,226 children with up to ten measurements on their body mass index (BMI) and risk factors from birth to the age of 10 years. We introduce boosting of structured additive quantile regression as a novel distribution-free approach for longitudinal quantile regression. The quantile-specific predictors of our model include conventional linear population effects, smooth nonlinear functional effects, varying-coefficient terms, and individual-specific effects, such as intercepts and slopes. Estimation is based on boosting, a computer intensive inference method for highly complex models. We propose a component-wise functional gradient descent boosting algorithm that allows for penalized estimation of the large variety of different effects, particularly leading to individual-specific effects shrunken toward zero. This concept allows us to flexibly estimate the nonlinear age curves of upper quantiles of the BMI distribution, both on population and on individual-specific level, adjusted for further risk factors and to detect age-varying effects of categorical risk factors. Our model approach can be regarded as the quantile regression analog of Gaussian additive mixed models (or structured additive mean regression models), and we compare both model classes with respect to our obesity data.

Polythermal investigation of viscosity of solution of metal carboxylates in VIK-grade mixed carboxylic acids: Yttrium and gadolinium carboxylates

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

Mezhov, E.A.; Samatov, A.V.; Troyanovskii, L.V.

Kinematic viscosities have been measured for solutions of yttrium and gadolinium carboxylates in grade VIK mixed carboxylic acids (MCA). It has been established that the optimal fluidity of these metal carboxylate solutions for application to articles is reached at 333 K. A regression model has been developed to describe the concentration and temperature dependences of the viscosity of yttrium- and gadolinium-containing MCA solutions. 2 refs., 3 tabs.

Comparing colon cancer outcomes: The impact of low hospital case volume and case-mix adjustment.

PubMed

Fischer, C; Lingsma, H F; van Leersum, N; Tollenaar, R A E M; Wouters, M W; Steyerberg, E W

2015-08-01

When comparing performance across hospitals it is essential to consider the noise caused by low hospital case volume and to perform adequate case-mix adjustment. We aimed to quantify the role of noise and case-mix adjustment on standardized postoperative mortality and anastomotic leakage (AL) rates. We studied 13,120 patients who underwent colon cancer resection in 85 Dutch hospitals. We addressed differences between hospitals in postoperative mortality and AL, using fixed (ignoring noise) and random effects (incorporating noise) logistic regression models with general and additional, disease specific, case-mix adjustment. Adding disease specific variables improved the performance of the case-mix adjustment models for postoperative mortality (c-statistic increased from 0.77 to 0.81). The overall variation in standardized mortality ratios was similar, but some individual hospitals changed considerably. For the standardized AL rates the performance of the adjustment models was poor (c-statistic 0.59 and 0.60) and overall variation was small. Most of the observed variation between hospitals was actually noise. Noise had a larger effect on hospital performance than extended case-mix adjustment, although some individual hospital outcome rates were affected by more detailed case-mix adjustment. To compare outcomes between hospitals it is crucial to consider noise due to low hospital case volume with a random effects model. Copyright © 2015 Elsevier Ltd. All rights reserved.

Land use regression models to assess air pollution exposure in Mexico City using finer spatial and temporal input parameters.

PubMed

Son, Yeongkwon; Osornio-Vargas, Álvaro R; O'Neill, Marie S; Hystad, Perry; Texcalac-Sangrador, José L; Ohman-Strickland, Pamela; Meng, Qingyu; Schwander, Stephan

2018-05-17

The Mexico City Metropolitan Area (MCMA) is one of the largest and most populated urban environments in the world and experiences high air pollution levels. To develop models that estimate pollutant concentrations at fine spatiotemporal scales and provide improved air pollution exposure assessments for health studies in Mexico City. We developed finer spatiotemporal land use regression (LUR) models for PM 2.5 , PM 10 , O 3 , NO 2 , CO and SO 2 using mixed effect models with the Least Absolute Shrinkage and Selection Operator (LASSO). Hourly traffic density was included as a temporal variable besides meteorological and holiday variables. Models of hourly, daily, monthly, 6-monthly and annual averages were developed and evaluated using traditional and novel indices. The developed spatiotemporal LUR models yielded predicted concentrations with good spatial and temporal agreements with measured pollutant levels except for the hourly PM 2.5 , PM 10 and SO 2 . Most of the LUR models met performance goals based on the standardized indices. LUR models with temporal scales greater than one hour were successfully developed using mixed effect models with LASSO and showed superior model performance compared to earlier LUR models, especially for time scales of a day or longer. The newly developed LUR models will be further refined with ongoing Mexico City air pollution sampling campaigns to improve personal exposure assessments. Copyright © 2018. Published by Elsevier B.V.

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control

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

Baker, Kyri A; Shi, Ying; Christensen, Dane T

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

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

Raszmann, Emma; Baker, Kyri; Shi, Ying

Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

Predicting chemical bioavailability using microarray gene expression data and regression modeling: A tale of three explosive compounds.

PubMed

Gong, Ping; Nan, Xiaofei; Barker, Natalie D; Boyd, Robert E; Chen, Yixin; Wilkins, Dawn E; Johnson, David R; Suedel, Burton C; Perkins, Edward J

2016-03-08

Chemical bioavailability is an important dose metric in environmental risk assessment. Although many approaches have been used to evaluate bioavailability, not a single approach is free from limitations. Previously, we developed a new genomics-based approach that integrated microarray technology and regression modeling for predicting bioavailability (tissue residue) of explosives compounds in exposed earthworms. In the present study, we further compared 18 different regression models and performed variable selection simultaneously with parameter estimation. This refined approach was applied to both previously collected and newly acquired earthworm microarray gene expression datasets for three explosive compounds. Our results demonstrate that a prediction accuracy of R(2) = 0.71-0.82 was achievable at a relatively low model complexity with as few as 3-10 predictor genes per model. These results are much more encouraging than our previous ones. This study has demonstrated that our approach is promising for bioavailability measurement, which warrants further studies of mixed contamination scenarios in field settings.

Alternative High School Students: Prevalence and Correlates of Overweight

ERIC Educational Resources Information Center

Kubik, Martha Y.; Davey, Cynthia; Fulkerson, Jayne A.; Sirard, John; Story, Mary; Arcan, Chrisa

2009-01-01

Objective: To determine prevalence and correlates of overweight among adolescents attending alternative high schools (AHS). Methods: AHS students (n=145) from 6 schools completed surveys and anthropometric measures. Cross-sectional associations were assessed using mixed model multivariate logistic regression. Results: Among students, 42% were…

Analysis and generation of groundwater concentration time series

NASA Astrophysics Data System (ADS)

Crăciun, Maria; Vamoş, Călin; Suciu, Nicolae

2018-01-01

Concentration time series are provided by simulated concentrations of a nonreactive solute transported in groundwater, integrated over the transverse direction of a two-dimensional computational domain and recorded at the plume center of mass. The analysis of a statistical ensemble of time series reveals subtle features that are not captured by the first two moments which characterize the approximate Gaussian distribution of the two-dimensional concentration fields. The concentration time series exhibit a complex preasymptotic behavior driven by a nonstationary trend and correlated fluctuations with time-variable amplitude. Time series with almost the same statistics are generated by successively adding to a time-dependent trend a sum of linear regression terms, accounting for correlations between fluctuations around the trend and their increments in time, and terms of an amplitude modulated autoregressive noise of order one with time-varying parameter. The algorithm generalizes mixing models used in probability density function approaches. The well-known interaction by exchange with the mean mixing model is a special case consisting of a linear regression with constant coefficients.

Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach

NASA Astrophysics Data System (ADS)

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.

Assessing spatial inequalities in accessing community pharmacies: a mixed geographically weighted approach.

PubMed

Domnich, Alexander; Arata, Lucia; Amicizia, Daniela; Signori, Alessio; Gasparini, Roberto; Panatto, Donatella

2016-11-16

Geographical accessibility is an important determinant for the utilisation of community pharmacies. The present study explored patterns of spatial accessibility with respect to pharmacies in Liguria, Italy, a region with particular geographical and demographic features. Municipal density of pharmacies was proxied as the number of pharmacies per capita and per km2, and spatial autocorrelation analysis was performed to identify spatial clusters. Both non-spatial and spatial models were constructed to predict the study outcome. Spatial autocorrelation analysis showed a highly significant clustered pattern in the density of pharmacies per capita (I=0.082) and per km2 (I=0.295). Potentially under-supplied areas were mostly located in the mountainous hinterland. Ordinary least-squares (OLS) regressions established a significant positive relationship between the density of pharmacies and income among municipalities located at high altitudes, while no such association was observed in lower-lying areas. However, residuals of the OLS models were spatially auto-correlated. The best-fitting mixed geographically weighted regression (GWR) models outperformed the corresponding OLS models. Pharmacies per capita were best predicted by two local predictors (altitude and proportion of immigrants) and two global ones (proportion of elderly residents and income), while the local terms population, mean altitude and rural status and the global term income functioned as independent variables predicting pharmacies per km2. The density of pharmacies in Liguria was found to be associated with both socio-economic and landscape factors. Mapping of mixed GWR results would be helpful to policy-makers.

A comparative study of generalized linear mixed modelling and artificial neural network approach for the joint modelling of survival and incidence of Dengue patients in Sri Lanka

NASA Astrophysics Data System (ADS)

Hapugoda, J. C.; Sooriyarachchi, M. R.

2017-09-01

Survival time of patients with a disease and the incidence of that particular disease (count) is frequently observed in medical studies with the data of a clustered nature. In many cases, though, the survival times and the count can be correlated in a way that, diseases that occur rarely could have shorter survival times or vice versa. Due to this fact, joint modelling of these two variables will provide interesting and certainly improved results than modelling these separately. Authors have previously proposed a methodology using Generalized Linear Mixed Models (GLMM) by joining the Discrete Time Hazard model with the Poisson Regression model to jointly model survival and count model. As Aritificial Neural Network (ANN) has become a most powerful computational tool to model complex non-linear systems, it was proposed to develop a new joint model of survival and count of Dengue patients of Sri Lanka by using that approach. Thus, the objective of this study is to develop a model using ANN approach and compare the results with the previously developed GLMM model. As the response variables are continuous in nature, Generalized Regression Neural Network (GRNN) approach was adopted to model the data. To compare the model fit, measures such as root mean square error (RMSE), absolute mean error (AME) and correlation coefficient (R) were used. The measures indicate the GRNN model fits the data better than the GLMM model.

Meta-analytical synthesis of regression coefficients under different categorization scheme of continuous covariates.

PubMed

Yoneoka, Daisuke; Henmi, Masayuki

2017-11-30

Recently, the number of clinical prediction models sharing the same regression task has increased in the medical literature. However, evidence synthesis methodologies that use the results of these regression models have not been sufficiently studied, particularly in meta-analysis settings where only regression coefficients are available. One of the difficulties lies in the differences between the categorization schemes of continuous covariates across different studies. In general, categorization methods using cutoff values are study specific across available models, even if they focus on the same covariates of interest. Differences in the categorization of covariates could lead to serious bias in the estimated regression coefficients and thus in subsequent syntheses. To tackle this issue, we developed synthesis methods for linear regression models with different categorization schemes of covariates. A 2-step approach to aggregate the regression coefficient estimates is proposed. The first step is to estimate the joint distribution of covariates by introducing a latent sampling distribution, which uses one set of individual participant data to estimate the marginal distribution of covariates with categorization. The second step is to use a nonlinear mixed-effects model with correction terms for the bias due to categorization to estimate the overall regression coefficients. Especially in terms of precision, numerical simulations show that our approach outperforms conventional methods, which only use studies with common covariates or ignore the differences between categorization schemes. The method developed in this study is also applied to a series of WHO epidemiologic studies on white blood cell counts. Copyright © 2017 John Wiley & Sons, Ltd.

Log-normal frailty models fitted as Poisson generalized linear mixed models.

PubMed

Hirsch, Katharina; Wienke, Andreas; Kuss, Oliver

2016-12-01

The equivalence of a survival model with a piecewise constant baseline hazard function and a Poisson regression model has been known since decades. As shown in recent studies, this equivalence carries over to clustered survival data: A frailty model with a log-normal frailty term can be interpreted and estimated as a generalized linear mixed model with a binary response, a Poisson likelihood, and a specific offset. Proceeding this way, statistical theory and software for generalized linear mixed models are readily available for fitting frailty models. This gain in flexibility comes at the small price of (1) having to fix the number of pieces for the baseline hazard in advance and (2) having to "explode" the data set by the number of pieces. In this paper we extend the simulations of former studies by using a more realistic baseline hazard (Gompertz) and by comparing the model under consideration with competing models. Furthermore, the SAS macro %PCFrailty is introduced to apply the Poisson generalized linear mixed approach to frailty models. The simulations show good results for the shared frailty model. Our new %PCFrailty macro provides proper estimates, especially in case of 4 events per piece. The suggested Poisson generalized linear mixed approach for log-normal frailty models based on the %PCFrailty macro provides several advantages in the analysis of clustered survival data with respect to more flexible modelling of fixed and random effects, exact (in the sense of non-approximate) maximum likelihood estimation, and standard errors and different types of confidence intervals for all variance parameters. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Transdermal Rivastigmine Delivery for Alzheimer Disease: Amenability of Exposure Predictions of Rivastigmine and Metabolite, NAP226-90, by Linear Regression Model Using Limited Samples.

PubMed

Srinivas, Nuggehally R

2016-01-01

Although an optimized delivery of rivastigmine for management of Alzhiemer disease (AD) is provided by the transdermal patch, it is critical to establish a limited sampling strategy for the measurement of exposure of rivastigmine/NAP226-90. The relationship Cmax versus AUC0-24h for rivastigmine/NAP226-90 was established by regression models. The derived regression equations enabled the prediction AUC0-24h for rivastigmine and NAP226-90. Models were evaluated using statistical criteria. Mixed model was used to predict AUC0-24h for rivastigmine/NAP226-90 from time points such as 8 (C8h), 12 (C12h), and 18 (C18h) hours. Excellent correlation was established for between Cmax and AUC0-24h for rivastigmine and NAP226-90. AUC0-24h predictions of either rivastigmine or NAP226-90 were within 0.8- to 1.25-fold difference. The RMSE in the AUC0-24h predictions ranged from 17.6% to 21.9%, and the R for prediction were greater than 0.96 for both rivastigmine and NAP226-90. Use of mixed model for C8h, C12h, and C18h resulted in AUC0-24h within 1.5-fold difference for rivastigmine or NAP226-90. Cmax of rivastigmine and NAP226-90 was highly correlated with the corresponding AUC0-24h values confirming the role of a time point closer to Cmax for an effective AUC measurement of rivastigmine or the metabolite.

Correcting for population structure and kinship using the linear mixed model: theory and extensions.

PubMed

Hoffman, Gabriel E

2013-01-01

Population structure and kinship are widespread confounding factors in genome-wide association studies (GWAS). It has been standard practice to include principal components of the genotypes in a regression model in order to account for population structure. More recently, the linear mixed model (LMM) has emerged as a powerful method for simultaneously accounting for population structure and kinship. The statistical theory underlying the differences in empirical performance between modeling principal components as fixed versus random effects has not been thoroughly examined. We undertake an analysis to formalize the relationship between these widely used methods and elucidate the statistical properties of each. Moreover, we introduce a new statistic, effective degrees of freedom, that serves as a metric of model complexity and a novel low rank linear mixed model (LRLMM) to learn the dimensionality of the correction for population structure and kinship, and we assess its performance through simulations. A comparison of the results of LRLMM and a standard LMM analysis applied to GWAS data from the Multi-Ethnic Study of Atherosclerosis (MESA) illustrates how our theoretical results translate into empirical properties of the mixed model. Finally, the analysis demonstrates the ability of the LRLMM to substantially boost the strength of an association for HDL cholesterol in Europeans.

Do Mixed-Flora Preoperative Urine Cultures Matter?

PubMed

Polin, Michael R; Kawasaki, Amie; Amundsen, Cindy L; Weidner, Alison C; Siddiqui, Nazema Y

2017-06-01

To determine whether mixed-flora preoperative urine cultures, as compared with no-growth preoperative urine cultures, are associated with a higher prevalence of postoperative urinary tract infections (UTIs). This was a retrospective cohort study. Women who underwent urogynecologic surgery were included if their preoperative clean-catch urine culture result was mixed flora or no growth. Women were excluded if they received postoperative antibiotics for reasons other than treatment of a UTI. Women were divided into two cohorts based on preoperative urine culture results-mixed flora or no growth; the prevalence of postoperative UTI was compared between cohorts. Baseline characteristics were compared using χ 2 or Student t tests. A logistic regression analysis then was performed. We included 282 women who were predominantly postmenopausal, white, and overweight. There were many concomitant procedures; 46% underwent a midurethral sling procedure and 68% underwent pelvic organ prolapse surgery. Preoperative urine cultures resulted as mixed flora in 192 (68%) and no growth in 90 (32%) patients. Overall, 14% were treated for a UTI postoperatively. There was no difference in the proportion of patients treated for a postoperative UTI between the two cohorts (25 mixed flora vs 13 no growth, P = 0.77). These results remained when controlling for potentially confounding variables in a logistic regression model (adjusted odds ratio 0.92, 95% confidence interval 0.43-1.96). In women with mixed-flora compared with no-growth preoperative urine cultures, there were no differences in the prevalence of postoperative UTI. The clinical practice of interpreting mixed-flora cultures as negative is appropriate.

The use of cognitive ability measures as explanatory variables in regression analysis

PubMed Central

Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J

2015-01-01

Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual’s wage, or a decision such as an individual’s education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score, constructed via standard psychometric practice from individuals’ responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a “mixed effects structural equations” (MESE) model, may be more appropriate in many circumstances. PMID:26998417

Sensitivity of Chemical Shift-Encoded Fat Quantification to Calibration of Fat MR Spectrum

PubMed Central

Wang, Xiaoke; Hernando, Diego; Reeder, Scott B.

2015-01-01

Purpose To evaluate the impact of different fat spectral models on proton density fat-fraction (PDFF) quantification using chemical shift-encoded (CSE) MRI. Material and Methods Simulations and in vivo imaging were performed. In a simulation study, spectral models of fat were compared pairwise. Comparison of magnitude fitting and mixed fitting was performed over a range of echo times and fat fractions. In vivo acquisitions from 41 patients were reconstructed using 7 published spectral models of fat. T2-corrected STEAM-MRS was used as reference. Results Simulations demonstrate that imperfectly calibrated spectral models of fat result in biases that depend on echo times and fat fraction. Mixed fitting is more robust against this bias than magnitude fitting. Multi-peak spectral models showed much smaller differences among themselves than when compared to the single-peak spectral model. In vivo studies show all multi-peak models agree better (for mixed fitting, slope ranged from 0.967–1.045 using linear regression) with reference standard than the single-peak model (for mixed fitting, slope=0.76). Conclusion It is essential to use a multi-peak fat model for accurate quantification of fat with CSE-MRI. Further, fat quantification techniques using multi-peak fat models are comparable and no specific choice of spectral model is shown to be superior to the rest. PMID:25845713

A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

PubMed

Karabatsos, George

2017-02-01

Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected functionals and values of covariates. The software is illustrated through the BNP regression analysis of real data.

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

Combining data visualization and statistical approaches for interpreting measurements and meta-data: Integrating heatmaps, variable clustering, and mixed regression models

EPA Science Inventory

The advent of new higher throughput analytical instrumentation has put a strain on interpreting and explaining the results from complex studies. Contemporary human, environmental, and biomonitoring data sets are comprised of tens or hundreds of analytes, multiple repeat measures...

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.

Wavelet-based functional linear mixed models: an application to measurement error-corrected distributed lag models.

PubMed

Malloy, Elizabeth J; Morris, Jeffrey S; Adar, Sara D; Suh, Helen; Gold, Diane R; Coull, Brent A

2010-07-01

Frequently, exposure data are measured over time on a grid of discrete values that collectively define a functional observation. In many applications, researchers are interested in using these measurements as covariates to predict a scalar response in a regression setting, with interest focusing on the most biologically relevant time window of exposure. One example is in panel studies of the health effects of particulate matter (PM), where particle levels are measured over time. In such studies, there are many more values of the functional data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary for estimation. Additional issues in this setting are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, that themselves may have effects that vary over time. To accommodate all these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model. A Bayesian approach to model fitting uses wavelet shrinkage to regularize functional coefficients. We show that, as long as the exposure error induces fine-scale variability in the functional exposure profile and the distributed lag function representing the exposure effect varies smoothly in time, the model corrects for the exposure measurement error without further adjustment. Both these conditions are likely to hold in the environmental applications we consider. We examine properties of the method using simulations and apply the method to data from a study examining the association between PM, measured as hourly averages for 1-7 days, and markers of acute systemic inflammation. We use the method to fully control for the effects of confounding by other time-varying predictors, such as temperature and co-pollutants.

Estimating daily surface NO2 concentrations from satellite data - a case study over Hong Kong using land use regression models

NASA Astrophysics Data System (ADS)

Anand, Jasdeep S.; Monks, Paul S.

2017-07-01

Land use regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed-effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during the period 2005-2015. In situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A, and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in situ data shows that the mixed-effects LUR model using OMI data has a high predictive power (adj. R2 = 0. 84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0. 11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

Spatiotemporal Modeling of Ozone Levels in Quebec (Canada): A Comparison of Kriging, Land-Use Regression (LUR), and Combined Bayesian Maximum Entropy–LUR Approaches

PubMed Central

Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael

2014-01-01

Background: Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. Objectives: We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. Methods: We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. Results: The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Conclusions: Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data. Citation: Adam-Poupart A, Brand A, Fournier M, Jerrett M, Smargiassi A. 2014. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches. Environ Health Perspect 122:970–976; http://dx.doi.org/10.1289/ehp.1306566 PMID:24879650

Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA)

PubMed Central

González, Juan R; Carrasco, Josep L; Armengol, Lluís; Villatoro, Sergi; Jover, Lluís; Yasui, Yutaka; Estivill, Xavier

2008-01-01

Background MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample. Results Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace. Conclusion Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed. PMID:18522760

Validation of ACG Case-mix for equitable resource allocation in Swedish primary health care.

PubMed

Zielinski, Andrzej; Kronogård, Maria; Lenhoff, Håkan; Halling, Anders

2009-09-18

Adequate resource allocation is an important factor to ensure equity in health care. Previous reimbursement models have been based on age, gender and socioeconomic factors. An explanatory model based on individual need of primary health care (PHC) has not yet been used in Sweden to allocate resources. The aim of this study was to examine to what extent the ACG case-mix system could explain concurrent costs in Swedish PHC. Diagnoses were obtained from electronic PHC records of inhabitants in Blekinge County (approx. 150,000) listed with public PHC (approx. 120,000) for three consecutive years, 2004-2006. The inhabitants were then classified into six different resource utilization bands (RUB) using the ACG case-mix system. The mean costs for primary health care were calculated for each RUB and year. Using linear regression models and log-cost as dependent variable the adjusted R2 was calculated in the unadjusted model (gender) and in consecutive models where age, listing with specific PHC and RUB were added. In an additional model the ACG groups were added. Gender, age and listing with specific PHC explained 14.48-14.88% of the variance in individual costs for PHC. By also adding information on level of co-morbidity, as measured by the ACG case-mix system, to specific PHC the adjusted R2 increased to 60.89-63.41%. The ACG case-mix system explains patient costs in primary care to a high degree. Age and gender are important explanatory factors, but most of the variance in concurrent patient costs was explained by the ACG case-mix system.

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.

Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data.

PubMed

Huff, Andrew G; Hodges, James S; Kennedy, Shaun P; Kircher, Amy

2015-08-01

To protect and secure food resources for the United States, it is crucial to have a method to compare food systems' criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank-order of food and agriculture system criticality to a large degree. © 2015 Society for Risk Analysis.

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

Mixed geographically weighted regression (MGWR) model with weighted adaptive bi-square for case of dengue hemorrhagic fever (DHF) in Surakarta

NASA Astrophysics Data System (ADS)

Astuti, H. N.; Saputro, D. R. S.; Susanti, Y.

2017-06-01

MGWR model is combination of linear regression model and geographically weighted regression (GWR) model, therefore, MGWR model could produce parameter estimation that had global parameter estimation, and other parameter that had local parameter in accordance with its observation location. The linkage between locations of the observations expressed in specific weighting that is adaptive bi-square. In this research, we applied MGWR model with weighted adaptive bi-square for case of DHF in Surakarta based on 10 factors (variables) that is supposed to influence the number of people with DHF. The observation unit in the research is 51 urban villages and the variables are number of inhabitants, number of houses, house index, many public places, number of healthy homes, number of Posyandu, area width, level population density, welfare of the family, and high-region. Based on this research, we obtained 51 MGWR models. The MGWR model were divided into 4 groups with significant variable is house index as a global variable, an area width as a local variable and the remaining variables vary in each. Global variables are variables that significantly affect all locations, while local variables are variables that significantly affect a specific location.

Boosted Regression Tree Models to Explain Watershed ...

EPA Pesticide Factsheets

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base-flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite-nitrate (NO2-NO3), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO2-NO3 concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed-use and heavily impacted watershed

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees.

PubMed

Fokkema, M; Smits, N; Zeileis, A; Hothorn, T; Kelderman, H

2017-10-25

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.

Effects of Morphological Family Size for Young Readers

ERIC Educational Resources Information Center

Perdijk, Kors; Schreuder, Robert; Baayen, R. Harald; Verhoeven, Ludo

2012-01-01

Dutch children, from the second and fourth grade of primary school, were each given a visual lexical decision test on 210 Dutch monomorphemic words. After removing words not recognized by a majority of the younger group, (lexical) decisions were analysed by mixed-model regression methods to see whether morphological Family Size influenced decision…

A preliminary case-mix classification system for Medicare home health clients.

PubMed

Branch, L G; Goldberg, H B

1993-04-01

In this study, a hierarchical case-mix model was developed for grouping Medicare home health beneficiaries homogeneously, based on the allowed charges for their home care. Based on information from a two-page form from 2,830 clients from ten states and using the classification and regression trees method, a four-component model was developed that yielded 11 case-mix groups and explained 22% of the variance for the test sample of 1,929 clients. The four components are rehabilitation, special care, skilled-nurse monitoring, and paralysis; each are categorized as present or absent. The range of mean-allowed charges for the 11 groups in the total sample was $473 to $2,562 with a mean of $847. Of the six groups with mean charges above $1,000, none exceeded 5.2% of clients; thus, the high-cost groups are relatively rare.

Comparison of four methods for deriving hospital standardised mortality ratios from a single hierarchical logistic regression model.

PubMed

Mohammed, Mohammed A; Manktelow, Bradley N; Hofer, Timothy P

2016-04-01

There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable. © The Author(s) 2012.

Many-level multilevel structural equation modeling: An efficient evaluation strategy.

PubMed

Pritikin, Joshua N; Hunter, Michael D; von Oertzen, Timo; Brick, Timothy R; Boker, Steven M

2017-01-01

Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a state-wide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software.

Use of non-linear mixed-effects modelling and regression analysis to predict the number of somatic coliphages by plaque enumeration after 3 hours of incubation.

PubMed

Mendez, Javier; Monleon-Getino, Antonio; Jofre, Juan; Lucena, Francisco

2017-10-01

The present study aimed to establish the kinetics of the appearance of coliphage plaques using the double agar layer titration technique to evaluate the feasibility of using traditional coliphage plaque forming unit (PFU) enumeration as a rapid quantification method. Repeated measurements of the appearance of plaques of coliphages titrated according to ISO 10705-2 at different times were analysed using non-linear mixed-effects regression to determine the most suitable model of their appearance kinetics. Although this model is adequate, to simplify its applicability two linear models were developed to predict the numbers of coliphages reliably, using the PFU counts as determined by the ISO after only 3 hours of incubation. One linear model, when the number of plaques detected was between 4 and 26 PFU after 3 hours, had a linear fit of: (1.48 × Counts 3 h + 1.97); and the other, values >26 PFU, had a fit of (1.18 × Counts 3 h + 2.95). If the number of plaques detected was <4 PFU after 3 hours, we recommend incubation for (18 ± 3) hours. The study indicates that the traditional coliphage plating technique has a reasonable potential to provide results in a single working day without the need to invest in additional laboratory equipment.

[New method of mixed gas infrared spectrum analysis based on SVM].

PubMed

Bai, Peng; Xie, Wen-Jun; Liu, Jun-Hua

2007-07-01

A new method of infrared spectrum analysis based on support vector machine (SVM) for mixture gas was proposed. The kernel function in SVM was used to map the seriously overlapping absorption spectrum into high-dimensional space, and after transformation, the high-dimensional data could be processed in the original space, so the regression calibration model was established, then the regression calibration model with was applied to analyze the concentration of component gas. Meanwhile it was proved that the regression calibration model with SVM also could be used for component recognition of mixture gas. The method was applied to the analysis of different data samples. Some factors such as scan interval, range of the wavelength, kernel function and penalty coefficient C that affect the model were discussed. Experimental results show that the component concentration maximal Mean AE is 0.132%, and the component recognition accuracy is higher than 94%. The problems of overlapping absorption spectrum, using the same method for qualitative and quantitative analysis, and limit number of training sample, were solved. The method could be used in other mixture gas infrared spectrum analyses, promising theoretic and application values.

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

Modeling the outcomes of nursing home care.

PubMed

Rohrer, J E; Hogan, A J

1987-01-01

In this exploratory analysis using data on 290 patients, we use regression analysis to model patient outcomes in two Veterans Administration nursing homes. We find resource use, as measured with minutes of nursing time, to be associated with outcomes when case mix is controlled. Our results suggest that, under case-based reimbursement systems, nursing homes could increase their revenues by withholding unskilled and psychosocial care and discouraging physicians' visits. Implications for nursing home policy are discussed.

Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy-LUR approaches.

PubMed

Adam-Poupart, Ariane; Brand, Allan; Fournier, Michel; Jerrett, Michael; Smargiassi, Audrey

2014-09-01

Ambient air ozone (O3) is a pulmonary irritant that has been associated with respiratory health effects including increased lung inflammation and permeability, airway hyperreactivity, respiratory symptoms, and decreased lung function. Estimation of O3 exposure is a complex task because the pollutant exhibits complex spatiotemporal patterns. To refine the quality of exposure estimation, various spatiotemporal methods have been developed worldwide. We sought to compare the accuracy of three spatiotemporal models to predict summer ground-level O3 in Quebec, Canada. We developed a land-use mixed-effects regression (LUR) model based on readily available data (air quality and meteorological monitoring data, road networks information, latitude), a Bayesian maximum entropy (BME) model incorporating both O3 monitoring station data and the land-use mixed model outputs (BME-LUR), and a kriging method model based only on available O3 monitoring station data (BME kriging). We performed leave-one-station-out cross-validation and visually assessed the predictive capability of each model by examining the mean temporal and spatial distributions of the average estimated errors. The BME-LUR was the best predictive model (R2 = 0.653) with the lowest root mean-square error (RMSE ;7.06 ppb), followed by the LUR model (R2 = 0.466, RMSE = 8.747) and the BME kriging model (R2 = 0.414, RMSE = 9.164). Our findings suggest that errors of estimation in the interpolation of O3 concentrations with BME can be greatly reduced by incorporating outputs from a LUR model developed with readily available data.

Micro-epidemiological structuring of Plasmodium falciparum parasite populations in regions with varying transmission intensities in Africa.

PubMed Central

Omedo, Irene; Mogeni, Polycarp; Bousema, Teun; Rockett, Kirk; Amambua-Ngwa, Alfred; Oyier, Isabella; C. Stevenson, Jennifer; Y. Baidjoe, Amrish; de Villiers, Etienne P.; Fegan, Greg; Ross, Amanda; Hubbart, Christina; Jeffreys, Anne; N. Williams, Thomas; Kwiatkowski, Dominic; Bejon, Philip

2017-01-01

Background: The first models of malaria transmission assumed a completely mixed and homogeneous population of parasites.  Recent models include spatial heterogeneity and variably mixed populations. However, there are few empiric estimates of parasite mixing with which to parametize such models. Methods: Here we genotype 276 single nucleotide polymorphisms (SNPs) in 5199 P. falciparum isolates from two Kenyan sites (Kilifi county and Rachuonyo South district) and one Gambian site (Kombo coastal districts) to determine the spatio-temporal extent of parasite mixing, and use Principal Component Analysis (PCA) and linear regression to examine the relationship between genetic relatedness and distance in space and time for parasite pairs. Results: Using 107, 177 and 82 SNPs that were successfully genotyped in 133, 1602, and 1034 parasite isolates from The Gambia, Kilifi and Rachuonyo South district, respectively, we show that there are no discrete geographically restricted parasite sub-populations, but instead we see a diffuse spatio-temporal structure to parasite genotypes.  Genetic relatedness of sample pairs is predicted by relatedness in space and time. Conclusions: Our findings suggest that targeted malaria control will benefit the surrounding community, but unfortunately also that emerging drug resistance will spread rapidly through the population. PMID:28612053

Civic Purpose in Late Adolescence: Factors That Prevent Decline in Civic Engagement after High School

ERIC Educational Resources Information Center

Malin, Heather; Han, Hyemin; Liauw, Indrawati

2017-01-01

This study investigated the effects of internal and demographic variables on civic development in late adolescence using the construct "civic purpose." We conducted surveys on civic engagement with 480 high school seniors, and surveyed them again 2 years later. Using multivariate regression and linear mixed models, we tested the main…

Sedentary Activity and Body Composition of Middle School Girls: The Trial of Activity for Adolescent Girls

ERIC Educational Resources Information Center

Pratt, Charlotte; Webber, Larry S.; Baggett, Chris D.; Ward, Dianne; Pate, Russell R.; Murray, David; Lohman, Timothy; Lytle, Leslie; Elder, John P.

2008-01-01

This study describes the relationships between sedentary activity and body composition in 1,458 sixth-grade girls from 36 middle schools across the United States. Multivariate associations between sedentary activity and body composition were examined with regression analyses using general linear mixed models. Mean age, body mass index, and…

Curriculum-Based Measurement of Oral Reading: Quality of Progress Monitoring Outcomes

ERIC Educational Resources Information Center

Christ, Theodore J.; Zopluoglu, Cengiz; Long, Jeffery D.; Monaghen, Barbara D.

2012-01-01

Curriculum-based measurement of oral reading (CBM-R) is frequently used to set student goals and monitor student progress. This study examined the quality of growth estimates derived from CBM-R progress monitoring data. The authors used a linear mixed effects regression (LMER) model to simulate progress monitoring data for multiple levels of…

Composites from southern pine juvenile wood. Part 3. Juvenile and mature wood furnish mixtures

Treesearch

A.D. Pugel; E.W. Price; Chung-Yun Hse; T.F. Shupe

2004-01-01

Composite panelsmade from mixtures ofmature andjuvenile southern pine (Pinus taeda L.) were evaluated for initial mechanical properties and dimensional stability. The effect that the proportion of juvenile wood had on panel properties was analyzed by regression and rule-of-mixtures models. The mixed furnish data: 1) highlighted the degree to which...

A Cross-National Study of the Relationship between Elderly Suicide Rates and Urbanization

ERIC Educational Resources Information Center

Shah, Ajit

2008-01-01

There is mixed evidence of a relationship between suicide rates in the general population and urbanization, and a paucity of studies examining this relationship in the elderly. A cross-national study with curve estimation regression model analysis, was undertaken to examine the a priori hypothesis that the relationship between elderly suicide…

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

Statistical inference methods for sparse biological time series data.

PubMed

Ndukum, Juliet; Fonseca, Luís L; Santos, Helena; Voit, Eberhard O; Datta, Susmita

2011-04-25

Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values <0.0001). We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures, based on ANOVA likelihood ratio tests, for testing the significance of differences between short time course data under different biological perturbations.

Dysglycemia, Glycemic Variability, and Outcome After Cardiac Arrest and Temperature Management at 33°C and 36°C.

PubMed

Borgquist, Ola; Wise, Matt P; Nielsen, Niklas; Al-Subaie, Nawaf; Cranshaw, Julius; Cronberg, Tobias; Glover, Guy; Hassager, Christian; Kjaergaard, Jesper; Kuiper, Michael; Smid, Ondrej; Walden, Andrew; Friberg, Hans

2017-08-01

Dysglycemia and glycemic variability are associated with poor outcomes in critically ill patients. Targeted temperature management alters blood glucose homeostasis. We investigated the association between blood glucose concentrations and glycemic variability and the neurologic outcomes of patients randomized to targeted temperature management at 33°C or 36°C after cardiac arrest. Post hoc analysis of the multicenter TTM-trial. Primary outcome of this analysis was neurologic outcome after 6 months, referred to as "Cerebral Performance Category." Thirty-six sites in Europe and Australia. All 939 patients with out-of-hospital cardiac arrest of presumed cardiac cause that had been included in the TTM-trial. Targeted temperature management at 33°C or 36°C. Nonparametric tests as well as multiple logistic regression and mixed effects logistic regression models were used. Median glucose concentrations on hospital admission differed significantly between Cerebral Performance Category outcomes (p < 0.0001). Hyper- and hypoglycemia were associated with poor neurologic outcome (p = 0.001 and p = 0.054). In the multiple logistic regression models, the median glycemic level was an independent predictor of poor Cerebral Performance Category (Cerebral Performance Category, 3-5) with an odds ratio (OR) of 1.13 in the adjusted model (p = 0.008; 95% CI, 1.03-1.24). It was also a predictor in the mixed model, which served as a sensitivity analysis to adjust for the multiple time points. The proportion of hyperglycemia was higher in the 33°C group compared with the 36°C group. Higher blood glucose levels at admission and during the first 36 hours, and higher glycemic variability, were associated with poor neurologic outcome and death. More patients in the 33°C treatment arm had hyperglycemia.

Bayesian function-on-function regression for multilevel functional data.

PubMed

Meyer, Mark J; Coull, Brent A; Versace, Francesco; Cinciripini, Paul; Morris, Jeffrey S

2015-09-01

Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images. © 2015, The International Biometric Society.

Conservative management or gamma knife radiosurgery for vestibular schwannoma: tumor growth, symptoms, and quality of life.

PubMed

Breivik, Cathrine Nansdal; Nilsen, Roy Miodini; Myrseth, Erling; Pedersen, Paal Henning; Varughese, Jobin K; Chaudhry, Aqeel Asghar; Lund-Johansen, Morten

2013-07-01

There are few reports about the course of vestibular schwannoma (VS) patients following gamma knife radiosurgery (GKRS) compared with the course following conservative management (CM). In this study, we present prospectively collected data of 237 patients with unilateral VS extending outside the internal acoustic canal who received either GKRS (113) or CM (124). The aim was to measure the effect of GKRS compared with the natural course on tumor growth rate and hearing loss. Secondary end points were postinclusion additional treatment, quality of life (QoL), and symptom development. The patients underwent magnetic resonance imaging scans, clinical examination, and QoL assessment by SF-36 questionnaire. Statistics were performed by using Spearman correlation coefficient, Kaplan-Meier plot, Poisson regression model, mixed linear regression models, and mixed logistic regression models. Mean follow-up time was 55.0 months (26.1 standard deviation, range 10-132). Thirteen patients were lost to follow-up. Serviceable hearing was lost in 54 of 71 (76%) (CM) and 34 of 53 (64%) (GKRS) patients during the study period (not significant, log-rank test). There was a significant reduction in tumor volume over time in the GKRS group. The need for treatment following initial GKRS or CM differed at highly significant levels (log-rank test, P < .001). Symptom and QoL development did not differ significantly between the groups. In VS patients, GKRS reduces the tumor growth rate and thereby the incidence rate of new treatment about tenfold. Hearing is lost at similar rates in both groups. Symptoms and QoL seem not to be significantly affected by GKRS.

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

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.

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.

Examining the influence of link function misspecification in conventional regression models for developing crash modification factors.

PubMed

Wu, Lingtao; Lord, Dominique

2017-05-01

This study further examined the use of regression models for developing crash modification factors (CMFs), specifically focusing on the misspecification in the link function. The primary objectives were to validate the accuracy of CMFs derived from the commonly used regression models (i.e., generalized linear models or GLMs with additive linear link functions) when some of the variables have nonlinear relationships and quantify the amount of bias as a function of the nonlinearity. Using the concept of artificial realistic data, various linear and nonlinear crash modification functions (CM-Functions) were assumed for three variables. Crash counts were randomly generated based on these CM-Functions. CMFs were then derived from regression models for three different scenarios. The results were compared with the assumed true values. The main findings are summarized as follows: (1) when some variables have nonlinear relationships with crash risk, the CMFs for these variables derived from the commonly used GLMs are all biased, especially around areas away from the baseline conditions (e.g., boundary areas); (2) with the increase in nonlinearity (i.e., nonlinear relationship becomes stronger), the bias becomes more significant; (3) the quality of CMFs for other variables having linear relationships can be influenced when mixed with those having nonlinear relationships, but the accuracy may still be acceptable; and (4) the misuse of the link function for one or more variables can also lead to biased estimates for other parameters. This study raised the importance of the link function when using regression models for developing CMFs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Regression calibration for models with two predictor variables measured with error and their interaction, using instrumental variables and longitudinal data.

PubMed

Strand, Matthew; Sillau, Stefan; Grunwald, Gary K; Rabinovitch, Nathan

2014-02-10

Regression calibration provides a way to obtain unbiased estimators of fixed effects in regression models when one or more predictors are measured with error. Recent development of measurement error methods has focused on models that include interaction terms between measured-with-error predictors, and separately, methods for estimation in models that account for correlated data. In this work, we derive explicit and novel forms of regression calibration estimators and associated asymptotic variances for longitudinal models that include interaction terms, when data from instrumental and unbiased surrogate variables are available but not the actual predictors of interest. The longitudinal data are fit using linear mixed models that contain random intercepts and account for serial correlation and unequally spaced observations. The motivating application involves a longitudinal study of exposure to two pollutants (predictors) - outdoor fine particulate matter and cigarette smoke - and their association in interactive form with levels of a biomarker of inflammation, leukotriene E4 (LTE 4 , outcome) in asthmatic children. Because the exposure concentrations could not be directly observed, we used measurements from a fixed outdoor monitor and urinary cotinine concentrations as instrumental variables, and we used concentrations of fine ambient particulate matter and cigarette smoke measured with error by personal monitors as unbiased surrogate variables. We applied the derived regression calibration methods to estimate coefficients of the unobserved predictors and their interaction, allowing for direct comparison of toxicity of the different pollutants. We used simulations to verify accuracy of inferential methods based on asymptotic theory. Copyright © 2013 John Wiley & Sons, Ltd.

Competing regression models for longitudinal data.

PubMed

Alencar, Airlane P; Singer, Julio M; Rocha, Francisco Marcelo M

2012-03-01

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretest-posttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spatially resolved regression analysis of pre-treatment FDG, FLT and Cu-ATSM PET from post-treatment FDG PET: an exploratory study

PubMed Central

Bowen, Stephen R; Chappell, Richard J; Bentzen, Søren M; Deveau, Michael A; Forrest, Lisa J; Jeraj, Robert

2012-01-01

Purpose To quantify associations between pre-radiotherapy and post-radiotherapy PET parameters via spatially resolved regression. Materials and methods Ten canine sinonasal cancer patients underwent PET/CT scans of [18F]FDG (FDGpre), [18F]FLT (FLTpre), and [61Cu]Cu-ATSM (Cu-ATSMpre). Following radiotherapy regimens of 50 Gy in 10 fractions, veterinary patients underwent FDG PET/CT scans at three months (FDGpost). Regression of standardized uptake values in baseline FDGpre, FLTpre and Cu-ATSMpre tumour voxels to those in FDGpost images was performed for linear, log-linear, generalized-linear and mixed-fit linear models. Goodness-of-fit in regression coefficients was assessed by R2. Hypothesis testing of coefficients over the patient population was performed. Results Multivariate linear model fits of FDGpre to FDGpost were significantly positive over the population (FDGpost~0.17 FDGpre, p=0.03), and classified slopes of RECIST non-responders and responders to be different (0.37 vs. 0.07, p=0.01). Generalized-linear model fits related FDGpre to FDGpost by a linear power law (FDGpost~FDGpre0.93, p<0.001). Univariate mixture model fits of FDGpre improved R2 from 0.17 to 0.52. Neither baseline FLT PET nor Cu-ATSM PET uptake contributed statistically significant multivariate regression coefficients. Conclusions Spatially resolved regression analysis indicates that pre-treatment FDG PET uptake is most strongly associated with three-month post-treatment FDG PET uptake in this patient population, though associations are histopathology-dependent. PMID:22682748

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.

Solutions for Determining the Significance Region Using the Johnson-Neyman Type Procedure in Generalized Linear (Mixed) Models

ERIC Educational Resources Information Center

Lazar, Ann A.; Zerbe, Gary O.

2011-01-01

Researchers often compare the relationship between an outcome and covariate for two or more groups by evaluating whether the fitted regression curves differ significantly. When they do, researchers need to determine the "significance region," or the values of the covariate where the curves significantly differ. In analysis of covariance (ANCOVA),…

The Effects of Baseline Estimation on the Reliability, Validity, and Precision of CBM-R Growth Estimates

ERIC Educational Resources Information Center

Van Norman, Ethan R.; Christ, Theodore J.; Zopluoglu, Cengiz

2013-01-01

This study examined the effect of baseline estimation on the quality of trend estimates derived from Curriculum Based Measurement of Oral Reading (CBM-R) progress monitoring data. The authors used a linear mixed effects regression (LMER) model to simulate progress monitoring data for schedules ranging from 6-20 weeks for datasets with high and low…

Application of seemingly unrelated regression in medical data with intermittently observed time-dependent covariates.

PubMed

Keshavarzi, Sareh; Ayatollahi, Seyyed Mohammad Taghi; Zare, Najaf; Pakfetrat, Maryam

2012-01-01

BACKGROUND. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful. METHODS. In this study, we performed the seemingly unrelated regression (SUR) based models, with respect to each observation time in longitudinal data with intermittently observed time-dependent covariates and further compared these models with mixed-effect regression models (MRMs) under three classic imputation procedures. Simulation studies were performed to compare the sample size properties of the estimated coefficients for different modeling choices. RESULTS. In general, the proposed models in the presence of intermittently observed time-dependent covariates showed a good performance. However, when we considered only the observed values of the covariate without any imputations, the resulted biases were greater. The performances of the proposed SUR-based models in comparison with MRM using classic imputation methods were nearly similar with approximately equal amounts of bias and MSE. CONCLUSION. The simulation study suggests that the SUR-based models work as efficiently as MRM in the case of intermittently observed time-dependent covariates. Thus, it can be used as an alternative to MRM.

An analysis of the adoption of managerial innovation: cost accounting systems in hospitals.

PubMed

Glandon, G L; Counte, M A

1995-11-01

The adoption of new medical technologies has received significant attention in the hospital industry, in part, because of its observed relation to hospital cost increases. However, few comprehensive studies exist regarding the adoption of non-medical technologies in the hospital setting. This paper develops and tests a model of the adoption of a managerial innovation, new to the hospital industry, that of cost accounting systems based upon standard costs. The conceptual model hypothesizes that four organizational context factors (size, complexity, ownership and slack resources) and two environmental factors (payor mix and interorganizational dependency) influence hospital adoption of cost accounting systems. Based on responses to a mail survey of hospitals in the Chicago area and AHA annual survey information for 1986, a sample of 92 hospitals was analyzed. Greater hospital size, complexity, slack resources, and interorganizational dependency all were associated with adoption. Payor mix had no significant influence and the hospital ownership variables had a mixed influence. The logistic regression model was significant overall and explained over 15% of the variance in the adoption decision.

Case-mix groups for VA hospital-based home care.

PubMed

Smith, M E; Baker, C R; Branch, L G; Walls, R C; Grimes, R M; Karklins, J M; Kashner, M; Burrage, R; Parks, A; Rogers, P

1992-01-01

The purpose of this study is to group hospital-based home care (HBHC) patients homogeneously by their characteristics with respect to cost of care to develop alternative case mix methods for management and reimbursement (allocation) purposes. Six Veterans Affairs (VA) HBHC programs in Fiscal Year (FY) 1986 that maximized patient, program, and regional variation were selected, all of which agreed to participate. All HBHC patients active in each program on October 1, 1987, in addition to all new admissions through September 30, 1988 (FY88), comprised the sample of 874 unique patients. Statistical methods include the use of classification and regression trees (CART software: Statistical Software; Lafayette, CA), analysis of variance, and multiple linear regression techniques. The resulting algorithm is a three-factor model that explains 20% of the cost variance (R2 = 20%, with a cross validation R2 of 12%). Similar classifications such as the RUG-II, which is utilized for VA nursing home and intermediate care, the VA outpatient resource allocation model, and the RUG-HHC, utilized in some states for reimbursing home health care in the private sector, explained less of the cost variance and, therefore, are less adequate for VA home care resource allocation.

GWAS with longitudinal phenotypes: performance of approximate procedures

PubMed Central

Sikorska, Karolina; Montazeri, Nahid Mostafavi; Uitterlinden, André; Rivadeneira, Fernando; Eilers, Paul HC; Lesaffre, Emmanuel

2015-01-01

Analysis of genome-wide association studies with longitudinal data using standard procedures, such as linear mixed model (LMM) fitting, leads to discouragingly long computation times. There is a need to speed up the computations significantly. In our previous work (Sikorska et al: Fast linear mixed model computations for genome-wide association studies with longitudinal data. Stat Med 2012; 32.1: 165–180), we proposed the conditional two-step (CTS) approach as a fast method providing an approximation to the P-value for the longitudinal single-nucleotide polymorphism (SNP) effect. In the first step a reduced conditional LMM is fit, omitting all the SNP terms. In the second step, the estimated random slopes are regressed on SNPs. The CTS has been applied to the bone mineral density data from the Rotterdam Study and proved to work very well even in unbalanced situations. In another article (Sikorska et al: GWAS on your notebook: fast semi-parallel linear and logistic regression for genome-wide association studies. BMC Bioinformatics 2013; 14: 166), we suggested semi-parallel computations, greatly speeding up fitting many linear regressions. Combining CTS with fast linear regression reduces the computation time from several weeks to a few minutes on a single computer. Here, we explore further the properties of the CTS both analytically and by simulations. We investigate the performance of our proposal in comparison with a related but different approach, the two-step procedure. It is analytically shown that for the balanced case, under mild assumptions, the P-value provided by the CTS is the same as from the LMM. For unbalanced data and in realistic situations, simulations show that the CTS method does not inflate the type I error rate and implies only a minimal loss of power. PMID:25712081

Generalized Linear Mixed Model Analysis of Urban-Rural Differences in Social and Behavioral Factors for Colorectal Cancer Screening

PubMed Central

Wang, Ke-Sheng; Liu, Xuefeng; Ategbole, Muyiwa; Xie, Xin; Liu, Ying; Xu, Chun; Xie, Changchun; Sha, Zhanxin

2017-01-01

Objective: Screening for colorectal cancer (CRC) can reduce disease incidence, morbidity, and mortality. However, few studies have investigated the urban-rural differences in social and behavioral factors influencing CRC screening. The objective of the study was to investigate the potential factors across urban-rural groups on the usage of CRC screening. Methods: A total of 38,505 adults (aged ≥40 years) were selected from the 2009 California Health Interview Survey (CHIS) data - the latest CHIS data on CRC screening. The weighted generalized linear mixed-model (WGLIMM) was used to deal with this hierarchical structure data. Weighted simple and multiple mixed logistic regression analyses in SAS ver. 9.4 were used to obtain the odds ratios (ORs) and their 95% confidence intervals (CIs). Results: The overall prevalence of CRC screening was 48.1% while the prevalence in four residence groups - urban, second city, suburban, and town/rural, were 45.8%, 46.9%, 53.7% and 50.1%, respectively. The results of WGLIMM analysis showed that there was residence effect (p<0.0001) and residence groups had significant interactions with gender, age group, education level, and employment status (p<0.05). Multiple logistic regression analysis revealed that age, race, marital status, education level, employment stats, binge drinking, and smoking status were associated with CRC screening (p<0.05). Stratified by residence regions, age and poverty level showed associations with CRC screening in all four residence groups. Education level was positively associated with CRC screening in second city and suburban. Infrequent binge drinking was associated with CRC screening in urban and suburban; while current smoking was a protective factor in urban and town/rural groups. Conclusions: Mixed models are useful to deal with the clustered survey data. Social factors and behavioral factors (binge drinking and smoking) were associated with CRC screening and the associations were affected by living areas such as urban and rural regions. PMID:28952708

Generalized Linear Mixed Model Analysis of Urban-Rural Differences in Social and Behavioral Factors for Colorectal Cancer Screening

PubMed

Wang, Ke-Sheng; Liu, Xuefeng; Ategbole, Muyiwa; Xie, Xin; Liu, Ying; Xu, Chun; Xie, Changchun; Sha, Zhanxin

2017-09-27

Objective: Screening for colorectal cancer (CRC) can reduce disease incidence, morbidity, and mortality. However, few studies have investigated the urban-rural differences in social and behavioral factors influencing CRC screening. The objective of the study was to investigate the potential factors across urban-rural groups on the usage of CRC screening. Methods: A total of 38,505 adults (aged ≥40 years) were selected from the 2009 California Health Interview Survey (CHIS) data - the latest CHIS data on CRC screening. The weighted generalized linear mixed-model (WGLIMM) was used to deal with this hierarchical structure data. Weighted simple and multiple mixed logistic regression analyses in SAS ver. 9.4 were used to obtain the odds ratios (ORs) and their 95% confidence intervals (CIs). Results: The overall prevalence of CRC screening was 48.1% while the prevalence in four residence groups - urban, second city, suburban, and town/rural, were 45.8%, 46.9%, 53.7% and 50.1%, respectively. The results of WGLIMM analysis showed that there was residence effect (p<0.0001) and residence groups had significant interactions with gender, age group, education level, and employment status (p<0.05). Multiple logistic regression analysis revealed that age, race, marital status, education level, employment stats, binge drinking, and smoking status were associated with CRC screening (p<0.05). Stratified by residence regions, age and poverty level showed associations with CRC screening in all four residence groups. Education level was positively associated with CRC screening in second city and suburban. Infrequent binge drinking was associated with CRC screening in urban and suburban; while current smoking was a protective factor in urban and town/rural groups. Conclusions: Mixed models are useful to deal with the clustered survey data. Social factors and behavioral factors (binge drinking and smoking) were associated with CRC screening and the associations were affected by living areas such as urban and rural regions. Creative Commons Attribution License

Quantitative Analysis of Single and Mix Food Antiseptics Basing on SERS Spectra with PLSR Method

NASA Astrophysics Data System (ADS)

Hou, Mengjing; Huang, Yu; Ma, Lingwei; Zhang, Zhengjun

2016-06-01

Usage and dosage of food antiseptics are very concerned due to their decisive influence in food safety. Surface-enhanced Raman scattering (SERS) effect was employed in this research to realize trace potassium sorbate (PS) and sodium benzoate (SB) detection. HfO2 ultrathin film-coated Ag NR array was fabricated as SERS substrate. Protected by HfO2 film, the SERS substrate possesses good acid resistance, which enables it to be applicable in acidic environment where PS and SB work. Regression relationship between SERS spectra of 0.3~10 mg/L PS solution and their concentration was calibrated by partial least squares regression (PLSR) method, and the concentration prediction performance was quite satisfactory. Furthermore, mixture solution of PS and SB was also quantitatively analyzed by PLSR method. Spectrum data of characteristic peak sections corresponding to PS and SB was used to establish the regression models of these two solutes, respectively, and their concentrations were determined accurately despite their characteristic peak sections overlapping. It is possible that the unique modeling process of PLSR method prevented the overlapped Raman signal from reducing the model accuracy.

New methodology for modeling annual-aircraft emissions at airports

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

Woodmansey, B.G.; Patterson, J.G.

An as-accurate-as-possible estimation of total-aircraft emissions are an essential component of any environmental-impact assessment done for proposed expansions at major airports. To determine the amount of emissions generated by aircraft using present models it is necessary to know the emission characteristics of all engines that are on all planes using the airport. However, the published data base does not cover all engine types and, therefore, a new methodology is needed to assist in estimating annual emissions from aircraft at airports. Linear regression equations relating quantity of emissions to aircraft weight using a known-fleet mix are developed in this paper. Total-annualmore » emissions for CO, NO[sub x], NMHC, SO[sub x], CO[sub 2], and N[sub 2]O are tabulated for Toronto's international airport for 1990. The regression equations are statistically significant for all emissions except for NMHC from large jets and NO[sub x] and NMHC for piston-engine aircraft. This regression model is a relatively simple, fast, and inexpensive method of obtaining an annual-emission inventory for an airport.« less

Developing A New Predictive Dispersion Equation Based on Tidal Average (TA) Condition in Alluvial Estuaries

NASA Astrophysics Data System (ADS)

Anak Gisen, Jacqueline Isabella; Nijzink, Remko C.; Savenije, Hubert H. G.

2014-05-01

Dispersion mathematical representation of tidal mixing between sea water and fresh water in The definition of dispersion somehow remains unclear as it is not directly measurable. The role of dispersion is only meaningful if it is related to the appropriate temporal and spatial scale of mixing, which are identified as the tidal period, tidal excursion (longitudinal), width of estuary (lateral) and mixing depth (vertical). Moreover, the mixing pattern determines the salt intrusion length in an estuary. If a physically based description of the dispersion is defined, this would allow the analytical solution of the salt intrusion problem. The objective of this study is to develop a predictive equation for estimating the dispersion coefficient at tidal average (TA) condition, which can be applied in the salt intrusion model to predict the salinity profile for any estuary during different events. Utilizing available data of 72 measurements in 27 estuaries (including 6 recently studied estuaries in Malaysia), regressions analysis has been performed with various combinations of dimensionless parameters . The predictive dispersion equations have been developed for two different locations, at the mouth D0TA and at the inflection point D1TA (where the convergence length changes). Regressions have been carried out with two separated datasets: 1) more reliable data for calibration; and 2) less reliable data for validation. The combination of dimensionless ratios that give the best performance is selected as the final outcome which indicates that the dispersion coefficient is depending on the tidal excursion, tidal range, tidal velocity amplitude, friction and the Richardson Number. A limitation of the newly developed equation is that the friction is generally unknown. In order to compensate this problem, further analysis has been performed adopting the hydraulic model of Cai et. al. (2012) to estimate the friction and depth. Keywords: dispersion, alluvial estuaries, mixing, salt intrusion, predictive equation

Response Surface Methodology for the Optimization of Preparation of Biocomposites Based on Poly(lactic acid) and Durian Peel Cellulose

PubMed Central

Penjumras, Patpen; Abdul Rahman, Russly; Talib, Rosnita A.; Abdan, Khalina

2015-01-01

Response surface methodology was used to optimize preparation of biocomposites based on poly(lactic acid) and durian peel cellulose. The effects of cellulose loading, mixing temperature, and mixing time on tensile strength and impact strength were investigated. A central composite design was employed to determine the optimum preparation condition of the biocomposites to obtain the highest tensile strength and impact strength. A second-order polynomial model was developed for predicting the tensile strength and impact strength based on the composite design. It was found that composites were best fit by a quadratic regression model with high coefficient of determination (R 2) value. The selected optimum condition was 35 wt.% cellulose loading at 165°C and 15 min of mixing, leading to a desirability of 94.6%. Under the optimum condition, the tensile strength and impact strength of the biocomposites were 46.207 MPa and 2.931 kJ/m2, respectively. PMID:26167523

Predicting daily PM2.5 concentrations in Texas using high-resolution satellite aerosol optical depth.

PubMed

Zhang, Xueying; Chu, Yiyi; Wang, Yuxuan; Zhang, Kai

2018-08-01

The regulatory monitoring data of particulate matter with an aerodynamic diameter <2.5μm (PM 2.5 ) in Texas have limited spatial and temporal coverage. The purpose of this study is to estimate the ground-level PM 2.5 concentrations on a daily basis using satellite-retrieved Aerosol Optical Depth (AOD) in the state of Texas. We obtained the AOD values at 1-km resolution generated through the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm based on the images retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. We then developed mixed-effects models based on AODs, land use features, geographic characteristics, and weather conditions, and the day-specific as well as site-specific random effects to estimate the PM 2.5 concentrations (μg/m 3 ) in the state of Texas during the period 2008-2013. The mixed-effects models' performance was evaluated using the coefficient of determination (R 2 ) and square root of the mean squared prediction error (RMSPE) from ten-fold cross-validation, which randomly selected 90% of the observations for training purpose and 10% of the observations for assessing the models' true prediction ability. Mixed-effects regression models showed good prediction performance (R 2 values from 10-fold cross validation: 0.63-0.69). The model performance varied by regions and study years, and the East region of Texas, and year of 2009 presented relatively higher prediction precision (R 2 : 0.62 for the East region; R 2 : 0.69 for the year of 2009). The PM 2.5 concentrations generated through our developed models at 1-km grid cells in the state of Texas showed a decreasing trend from 2008 to 2013 and a higher reduction of predicted PM 2.5 in more polluted areas. Our findings suggest that mixed-effects regression models developed based on MAIAC AOD are a feasible approach to predict ground-level PM 2.5 in Texas. Predicted PM 2.5 concentrations at the 1-km resolution on a daily basis can be used for epidemiological studies to investigate short- and long-term health impact of PM 2.5 in Texas. Copyright © 2017 Elsevier B.V. All rights reserved.

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

PharmML in Action: an Interoperable Language for Modeling and Simulation.

PubMed

Bizzotto, R; Comets, E; Smith, G; Yvon, F; Kristensen, N R; Swat, M J

2017-10-01

PharmML is an XML-based exchange format created with a focus on nonlinear mixed-effect (NLME) models used in pharmacometrics, but providing a very general framework that also allows describing mathematical and statistical models such as single-subject or nonlinear and multivariate regression models. This tutorial provides an overview of the structure of this language, brief suggestions on how to work with it, and use cases demonstrating its power and flexibility. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models

PubMed Central

Hobbs, Brian P.; Sargent, Daniel J.; Carlin, Bradley P.

2014-01-01

Assessing between-study variability in the context of conventional random-effects meta-analysis is notoriously difficult when incorporating data from only a small number of historical studies. In order to borrow strength, historical and current data are often assumed to be fully homogeneous, but this can have drastic consequences for power and Type I error if the historical information is biased. In this paper, we propose empirical and fully Bayesian modifications of the commensurate prior model (Hobbs et al., 2011) extending Pocock (1976), and evaluate their frequentist and Bayesian properties for incorporating patient-level historical data using general and generalized linear mixed regression models. Our proposed commensurate prior models lead to preposterior admissible estimators that facilitate alternative bias-variance trade-offs than those offered by pre-existing methodologies for incorporating historical data from a small number of historical studies. We also provide a sample analysis of a colon cancer trial comparing time-to-disease progression using a Weibull regression model. PMID:24795786

Case-Mix Adjusting Performance Measures in a Veteran Population: Pharmacy- and Diagnosis-Based Approaches

PubMed Central

Liu, Chuan-Fen; Sales, Anne E; Sharp, Nancy D; Fishman, Paul; Sloan, Kevin L; Todd-Stenberg, Jeff; Nichol, W Paul; Rosen, Amy K; Loveland, Susan

2003-01-01

Objective To compare the rankings for health care utilization performance measures at the facility level in a Veterans Health Administration (VHA) health care delivery network using pharmacy- and diagnosis-based case-mix adjustment measures. Data Sources/Study Setting The study included veterans who used inpatient or outpatient services in Veterans Integrated Service Network (VISN) 20 during fiscal year 1998 (October 1997 to September 1998; N=126,076). Utilization and pharmacy data were extracted from VHA national databases and the VISN 20 data warehouse. Study Design We estimated concurrent regression models using pharmacy or diagnosis information in the base year (FY1998) to predict health service utilization in the same year. Utilization measures included bed days of care for inpatient care and provider visits for outpatient care. Principal Findings Rankings of predicted utilization measures across facilities vary by case-mix adjustment measure. There is greater consistency within the diagnosis-based models than between the diagnosis- and pharmacy-based models. The eight facilities were ranked differently by the diagnosis- and pharmacy-based models. Conclusions Choice of case-mix adjustment measure affects rankings of facilities on performance measures, raising concerns about the validity of profiling practices. Differences in rankings may reflect differences in comparability of data capture across facilities between pharmacy and diagnosis data sources, and unstable estimates due to small numbers of patients in a facility. PMID:14596393

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

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.

Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio.

PubMed

Lloyd-Jones, Luke R; Robinson, Matthew R; Yang, Jian; Visscher, Peter M

2018-04-01

Genome-wide association studies (GWAS) have identified thousands of loci that are robustly associated with complex diseases. The use of linear mixed model (LMM) methodology for GWAS is becoming more prevalent due to its ability to control for population structure and cryptic relatedness and to increase power. The odds ratio (OR) is a common measure of the association of a disease with an exposure ( e.g. , a genetic variant) and is readably available from logistic regression. However, when the LMM is applied to all-or-none traits it provides estimates of genetic effects on the observed 0-1 scale, a different scale to that in logistic regression. This limits the comparability of results across studies, for example in a meta-analysis, and makes the interpretation of the magnitude of an effect from an LMM GWAS difficult. In this study, we derived transformations from the genetic effects estimated under the LMM to the OR that only rely on summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects. Copyright © 2018 by the Genetics Society of America.

Symptoms of Depression Depend on Rigid Parenting Attitudes, Gender, and Race in an At-Risk Sample of Early Adolescents

ERIC Educational Resources Information Center

Weed, Keri; Morales, Dawn A.; Harjes, Rachel

2013-01-01

Trajectories of depressive symptoms were compared between European American and African American boys and girls from ages 8 to 14 in a longitudinal sample of 130 children born to adolescent mothers. Mixed-effects regression modeling was used to analyze individual and group differences in level of depressive symptoms and their changes over time.…

Regression Model for MODTRAN with Applications to Inactivation of Microbes Suspended in the Atmosphere by Solar Ultraviolet Radiation

DTIC Science & Technology

2012-05-01

mixed vegetation): 0.007 (0.017) For materials tested, • The albedo levels of old grass, dead grass, burnt grass, and maple leaf at 300 nm were...as 0.016-0.017 over vegetation, 0.04-0.05 over bare fertile soil, and 0.07-0.10 over concrete (autobahn, Germany). The albedo over dry bright sand

Use of Midlevel Practitioners to Achieve Labor Cost Savings in the Primary Care Practice of an MCO

PubMed Central

Roblin, Douglas W; Howard, David H; Becker, Edmund R; Kathleen Adams, E; Roberts, Melissa H

2004-01-01

Objective To estimate the savings in labor costs per primary care visit that might be realized from increased use of physician assistants (PAs) and nurse practitioners (NPs) in the primary care practices of a managed care organization (MCO). Study Setting/Data Sources Twenty-six capitated primary care practices of a group model MCO. Data on approximately two million visits provided by 206 practitioners were extracted from computerized visit records for 1997–2000. Computerized payroll ledgers were the source of annual labor costs per practice from 1997–2000. Study Design Likelihood of a visit attended by a PA/NP versus MD was modeled using logistic regression, with practice fixed effects, by department (adult medicine, pediatrics) and year. Parameter estimates and practice fixed effects from these regressions were used to predict the proportion of PA/NP visits per practice per year given a standard case mix. Least squares regressions, with practice fixed effects, were used to estimate the association of this standardized predicted proportion of PA/NP visits with average annual practitioner and total labor costs per visit, controlling for other practice characteristics. Results On average, PAs/NPs attended one in three adult medicine visits and one in five pediatric medicine visits. Likelihood of a PA/NP visit was significantly higher than average among patients presenting with minor acute illness (e.g., acute pharyngitis). In adult medicine, likelihood of a PA/NP visit was lower than average among older patients. Practitioner labor costs per visit and total labor costs per visit were lower (p<.01 and p=.08, respectively) among practices with greater use of PAs/NPs, standardized for case mix. Conclusions Primary care practices that used more PAs/NPs in care delivery realized lower practitioner labor costs per visit than practices that used less. Future research should investigate the cost savings and cost-effectiveness potential of delivery designs that change staffing mix and division of labor among clinical disciplines. PMID:15149481

Determining vehicle operating speed and lateral position along horizontal curves using linear mixed-effects models.

PubMed

Fitzsimmons, Eric J; Kvam, Vanessa; Souleyrette, Reginald R; Nambisan, Shashi S; Bonett, Douglas G

2013-01-01

Despite recent improvements in highway safety in the United States, serious crashes on curves remain a significant problem. To assist in better understanding causal factors leading to this problem, this article presents and demonstrates a methodology for collection and analysis of vehicle trajectory and speed data for rural and urban curves using Z-configured road tubes. For a large number of vehicle observations at 2 horizontal curves located in Dexter and Ames, Iowa, the article develops vehicle speed and lateral position prediction models for multiple points along these curves. Linear mixed-effects models were used to predict vehicle lateral position and speed along the curves as explained by operational, vehicle, and environmental variables. Behavior was visually represented for an identified subset of "risky" drivers. Linear mixed-effect regression models provided the means to predict vehicle speed and lateral position while taking into account repeated observations of the same vehicle along horizontal curves. Speed and lateral position at point of entry were observed to influence trajectory and speed profiles. Rural horizontal curve site models are presented that indicate that the following variables were significant and influenced both vehicle speed and lateral position: time of day, direction of travel (inside or outside lane), and type of vehicle.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies.

PubMed

Koerner, Tess K; Zhang, Yang

2017-02-27

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.

A comparative study of kinetic and connectionist modeling for shelf-life prediction of Basundi mix.

PubMed

Ruhil, A P; Singh, R R B; Jain, D K; Patel, A A; Patil, G R

2011-04-01

A ready-to-reconstitute formulation of Basundi, a popular Indian dairy dessert was subjected to storage at various temperatures (10, 25 and 40 °C) and deteriorative changes in the Basundi mix were monitored using quality indices like pH, hydroxyl methyl furfural (HMF), bulk density (BD) and insolubility index (II). The multiple regression equations and the Arrhenius functions that describe the parameters' dependence on temperature for the four physico-chemical parameters were integrated to develop mathematical models for predicting sensory quality of Basundi mix. Connectionist model using multilayer feed forward neural network with back propagation algorithm was also developed for predicting the storage life of the product employing artificial neural network (ANN) tool box of MATLAB software. The quality indices served as the input parameters whereas the output parameters were the sensorily evaluated flavour and total sensory score. A total of 140 observations were used and the prediction performance was judged on the basis of per cent root mean square error. The results obtained from the two approaches were compared. Relatively lower magnitudes of percent root mean square error for both the sensory parameters indicated that the connectionist models were better fitted than kinetic models for predicting storage life.

IMPACT: Investigating the impact of Models of Practice for Allied health Care in subacuTe settings. A protocol for a quasi-experimental mixed methods study of cost effectiveness and outcomes for patients exposed to different models of allied health care.

PubMed

Coker, Freya; Williams, Cylie M; Taylor, Nicholas F; Caspers, Kirsten; McAlinden, Fiona; Wilton, Anita; Shields, Nora; Haines, Terry P

2018-05-10

This protocol considers three allied health staffing models across public health subacute hospitals. This quasi-experimental mixed-methods study, including qualitative process evaluation, aims to evaluate the impact of additional allied health services in subacute care, in rehabilitation and geriatric evaluation management settings, on patient, health service and societal outcomes. This health services research will analyse outcomes of patients exposed to different allied health models of care at three health services. Each health service will have a control ward (routine care) and an intervention ward (additional allied health). This project has two parts. Part 1: a whole of site data extraction for included wards. Outcome measures will include: length of stay, rate of readmissions, discharge destinations, community referrals, patient feedback and staff perspectives. Part 2: Functional Independence Measure scores will be collected every 2-3 days for the duration of 60 patient admissions.Data from part 1 will be analysed by linear regression analysis for continuous outcomes using patient-level data and logistic regression analysis for binary outcomes. Qualitative data will be analysed using a deductive thematic approach. For part 2, a linear mixed model analysis will be conducted using therapy service delivery and days since admission to subacute care as fixed factors in the model and individual participant as a random factor. Graphical analysis will be used to examine the growth curve of the model and transformations. The days since admission factor will be used to examine non-linear growth trajectories to determine if they lead to better model fit. Findings will be disseminated through local reports and to the Department of Health and Human Services Victoria. Results will be presented at conferences and submitted to peer-reviewed journals. The Monash Health Human Research Ethics committee approved this multisite research (HREC/17/MonH/144 and HREC/17/MonH/547). © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Analyzing Health-Related Quality of Life Data to Estimate Parameters for Cost-Effectiveness Models: An Example Using Longitudinal EQ-5D Data from the SHIFT Randomized Controlled Trial.

PubMed

Griffiths, Alison; Paracha, Noman; Davies, Andrew; Branscombe, Neil; Cowie, Martin R; Sculpher, Mark

2017-03-01

The aim of this article is to discuss methods used to analyze health-related quality of life (HRQoL) data from randomized controlled trials (RCTs) for decision analytic models. The analysis presented in this paper was used to provide HRQoL data for the ivabradine health technology assessment (HTA) submission in chronic heart failure. We have used a large, longitudinal EuroQol five-dimension questionnaire (EQ-5D) dataset from the Systolic Heart Failure Treatment with the I f Inhibitor Ivabradine Trial (SHIFT) (clinicaltrials.gov: NCT02441218) to illustrate issues and methods. HRQoL weights (utility values) were estimated from a mixed regression model developed using SHIFT EQ-5D data (n = 5313 patients). The regression model was used to predict HRQoL outcomes according to treatment, patient characteristics, and key clinical outcomes for patients with a heart rate ≥75 bpm. Ivabradine was associated with an HRQoL weight gain of 0.01. HRQoL weights differed according to New York Heart Association (NYHA) class (NYHA I-IV, no hospitalization: standard care 0.82-0.46; ivabradine 0.84-0.47). A reduction in HRQoL weight was associated with hospitalizations within 30 days of an HRQoL assessment visit, with this reduction varying by NYHA class [-0.07 (NYHA I) to -0.21 (NYHA IV)]. The mixed model explained variation in EQ-5D data according to key clinical outcomes and patient characteristics, providing essential information for long-term predictions of patient HRQoL in the cost-effectiveness model. This model was also used to estimate the loss in HRQoL associated with hospitalizations. In SHIFT many hospitalizations did not occur close to EQ-5D visits; hence, any temporary changes in HRQoL associated with such events would not be captured fully in observed RCT evidence, but could be predicted in our cost-effectiveness analysis using the mixed model. Given the large reduction in hospitalizations associated with ivabradine this was an important feature of the analysis. The Servier Research Group.

Numerical investigation on the regression rate of hybrid rocket motor with star swirl fuel grain

NASA Astrophysics Data System (ADS)

Zhang, Shuai; Hu, Fan; Zhang, Weihua

2016-10-01

Although hybrid rocket motor is prospected to have distinct advantages over liquid and solid rocket motor, low regression rate and insufficient efficiency are two major disadvantages which have prevented it from being commercially viable. In recent years, complex fuel grain configurations are attractive in overcoming the disadvantages with the help of Rapid Prototyping technology. In this work, an attempt has been made to numerically investigate the flow field characteristics and local regression rate distribution inside the hybrid rocket motor with complex star swirl grain. A propellant combination with GOX and HTPB has been chosen. The numerical model is established based on the three dimensional Navier-Stokes equations with turbulence, combustion, and coupled gas/solid phase formulations. The calculated fuel regression rate is compared with the experimental data to validate the accuracy of numerical model. The results indicate that, comparing the star swirl grain with the tube grain under the conditions of the same port area and the same grain length, the burning surface area rises about 200%, the spatially averaged regression rate rises as high as about 60%, and the oxidizer can combust sufficiently due to the big vortex around the axis in the aft-mixing chamber. The combustion efficiency of star swirl grain is better and more stable than that of tube grain.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Random regression analyses using B-splines to model growth of Australian Angus cattle

PubMed Central

Meyer, Karin

2005-01-01

Regression on the basis function of B-splines has been advocated as an alternative to orthogonal polynomials in random regression analyses. Basic theory of splines in mixed model analyses is reviewed, and estimates from analyses of weights of Australian Angus cattle from birth to 820 days of age are presented. Data comprised 84 533 records on 20 731 animals in 43 herds, with a high proportion of animals with 4 or more weights recorded. Changes in weights with age were modelled through B-splines of age at recording. A total of thirteen analyses, considering different combinations of linear, quadratic and cubic B-splines and up to six knots, were carried out. Results showed good agreement for all ages with many records, but fluctuated where data were sparse. On the whole, analyses using B-splines appeared more robust against "end-of-range" problems and yielded more consistent and accurate estimates of the first eigenfunctions than previous, polynomial analyses. A model fitting quadratic B-splines, with knots at 0, 200, 400, 600 and 821 days and a total of 91 covariance components, appeared to be a good compromise between detailedness of the model, number of parameters to be estimated, plausibility of results, and fit, measured as residual mean square error. PMID:16093011

Estimating ground-level PM(10) in a Chinese city by combining satellite data, meteorological information and a land use regression model.

PubMed

Meng, Xia; Fu, Qingyan; Ma, Zongwei; Chen, Li; Zou, Bin; Zhang, Yan; Xue, Wenbo; Wang, Jinnan; Wang, Dongfang; Kan, Haidong; Liu, Yang

2016-01-01

Development of exposure assessment model is the key component for epidemiological studies concerning air pollution, but the evidence from China is limited. Therefore, a linear mixed effects (LME) model was established in this study in a Chinese metropolis by incorporating aerosol optical depth (AOD), meteorological information and the land use regression (LUR) model to predict ground PM10 levels on high spatiotemporal resolution. The cross validation (CV) R(2) and the RMSE of the LME model were 0.87 and 19.2 μg/m(3), respectively. The relative prediction error (RPE) of daily and annual mean predicted PM10 concentrations were 19.1% and 7.5%, respectively. This study was the first attempt in China to estimate both short-term and long-term variation of PM10 levels with high spatial resolution in a Chinese metropolis with the LME model. The results suggested that the LME model could provide exposure assessment for short-term and long-term epidemiological studies in China. Copyright © 2015 Elsevier Ltd. All rights reserved.

Functional Additive Mixed Models

PubMed Central

Scheipl, Fabian; Staicu, Ana-Maria; Greven, Sonja

2014-01-01

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R-package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well and also scales to larger data sets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach. PMID:26347592

Functional Additive Mixed Models.

PubMed

Scheipl, Fabian; Staicu, Ana-Maria; Greven, Sonja

2015-04-01

We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. It accommodates densely or sparsely observed functional responses and predictors which may be observed with additional error and includes both spline-based and functional principal component-based terms. Estimation and inference in this framework is based on standard additive mixed models, allowing us to take advantage of established methods and robust, flexible algorithms. We provide easy-to-use open source software in the pffr() function for the R-package refund. Simulations show that the proposed method recovers relevant effects reliably, handles small sample sizes well and also scales to larger data sets. Applications with spatially and longitudinally observed functional data demonstrate the flexibility in modeling and interpretability of results of our approach.

Water mass mixing: The dominant control on the zinc distribution in the North Atlantic Ocean

NASA Astrophysics Data System (ADS)

Roshan, Saeed; Wu, Jingfeng

2015-07-01

Dissolved zinc (dZn) concentration was determined in the North Atlantic during the U.S. GEOTRACES 2010 and 2011 cruise (GOETRACES GA03). A relatively poor linear correlation (R2 = 0.756) was observed between dZn and silicic acid (Si), the slope of which was 0.0577 nM/µmol/kg. We attribute the relatively poor dZn-Si correlation to the following processes: (a) differential regeneration of zinc relative to silicic acid, (b) mixing of multiple water masses that have different Zn/Si, and (c) zinc sources such as sedimentary or hydrothermal. To quantitatively distinguish these possibilities, we use the results of Optimum Multi-Parameter Water Mass Analysis by Jenkins et al. (2015) to model the zinc distribution below 500 m. We hypothesized two scenarios: conservative mixing and regenerative mixing. The first scenario (conservative) could be modeled to results in a correlation with observations with a R2 = 0.846. In the second scenario, we took a Si-related regeneration into account, which could model the observations with a R2 = 0.867. Through this regenerative mixing scenario, we estimated a Zn/Si = 0.0548 nM/µmol/kg that may be more realistic than linear regression slope due to accounting for process b. However, this did not improve the model substantially (R2 = 0.867 versus0.846), which may indicate the insignificant effect of remineralization on the zinc distribution in this region. The relative weakness in the model-observation correlation (R2~0.85 for both scenarios) implies that processes (a) and (c) may be plausible. Furthermore, dZn in the upper 500 m exhibited a very poor correlation with apparent oxygen utilization, suggesting a minimal role for the organic matter-associated remineralization process.

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape

PubMed Central

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for ‘difficult’ variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables. PMID:29713298

Modeling Linguistic Variables With Regression Models: Addressing Non-Gaussian Distributions, Non-independent Observations, and Non-linear Predictors With Random Effects and Generalized Additive Models for Location, Scale, and Shape.

PubMed

Coupé, Christophe

2018-01-01

As statistical approaches are getting increasingly used in linguistics, attention must be paid to the choice of methods and algorithms used. This is especially true since they require assumptions to be satisfied to provide valid results, and because scientific articles still often fall short of reporting whether such assumptions are met. Progress is being, however, made in various directions, one of them being the introduction of techniques able to model data that cannot be properly analyzed with simpler linear regression models. We report recent advances in statistical modeling in linguistics. We first describe linear mixed-effects regression models (LMM), which address grouping of observations, and generalized linear mixed-effects models (GLMM), which offer a family of distributions for the dependent variable. Generalized additive models (GAM) are then introduced, which allow modeling non-linear parametric or non-parametric relationships between the dependent variable and the predictors. We then highlight the possibilities offered by generalized additive models for location, scale, and shape (GAMLSS). We explain how they make it possible to go beyond common distributions, such as Gaussian or Poisson, and offer the appropriate inferential framework to account for 'difficult' variables such as count data with strong overdispersion. We also demonstrate how they offer interesting perspectives on data when not only the mean of the dependent variable is modeled, but also its variance, skewness, and kurtosis. As an illustration, the case of phonemic inventory size is analyzed throughout the article. For over 1,500 languages, we consider as predictors the number of speakers, the distance from Africa, an estimation of the intensity of language contact, and linguistic relationships. We discuss the use of random effects to account for genealogical relationships, the choice of appropriate distributions to model count data, and non-linear relationships. Relying on GAMLSS, we assess a range of candidate distributions, including the Sichel, Delaporte, Box-Cox Green and Cole, and Box-Cox t distributions. We find that the Box-Cox t distribution, with appropriate modeling of its parameters, best fits the conditional distribution of phonemic inventory size. We finally discuss the specificities of phoneme counts, weak effects, and how GAMLSS should be considered for other linguistic variables.

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.

Real medical benefit assessed by indirect comparison.

PubMed

Falissard, Bruno; Zylberman, Myriam; Cucherat, Michel; Izard, Valérie; Meyer, François

2009-01-01

Frequently, in data packages submitted for Marketing Approval to the CHMP, there is a lack of relevant head-to-head comparisons of medicinal products that could enable national authorities responsible for the approval of reimbursement to assess the Added Therapeutic Value (ASMR) of new clinical entities or line extensions of existing therapies.Indirect or mixed treatment comparisons (MTC) are methods stemming from the field of meta-analysis that have been designed to tackle this problem. Adjusted indirect comparisons, meta-regressions, mixed models, Bayesian network analyses pool results of randomised controlled trials (RCTs), enabling a quantitative synthesis.The REAL procedure, recently developed by the HAS (French National Authority for Health), is a mixture of an MTC and effect model based on expert opinions. It is intended to translate the efficacy observed in the trials into effectiveness expected in day-to-day clinical practice in France.

Retrieval of forest biomass for tropical deciduous mixed forest using ALOS PALSAR mosaic imagery and field plot data

NASA Astrophysics Data System (ADS)

Ningthoujam, Ramesh K.; Joshi, P. K.; Roy, P. S.

2018-07-01

Tropical forest is an important ecosystem rich in biodiversity and structural complexity with high woody biomass content. Longer wavelength radar data at L-band sensor provides improved forest biomass (AGB) information due to its higher penetration level and sensitivity to canopy structure. The study presents a regression based woody biomass estimation for tropical deciduous mixed forest dominated by Shorea robusta using ALOS PALSAR mosaic (HH, HV) and field data at the lower Himalayan belt of Northern India. For the purpose of understanding the scattering mechanisms at L-band from this forest type, Michigan Microwave Canopy Scattering model (MIMICS-I) was parameterized with field data to simulate backscatter across polarization and incidence range. Regression analysis between field measured forest biomass and L-band backscatter data from PALSAR mosaic show retrieval of woody biomass up to 100 Mg ha-1 with error between 92 and 94 Mg ha-1 and coefficient of determination (r2) between 0.53 and 0.55 for HH and HH + HV polarized channel at 0.25 ha resolution. This positive relationship could be due to strong volume scattering from ground/trunk interaction at HH-polarized while in combination with direct canopy scattering for HV-polarization at ALOS specific incidence angles as predicted by MIMICS-I model. This study has found that L-band SAR data from currently ALOS-1/-2 and upcoming joint NASA-ISRO SAR (NISAR) are suitable for mapping forest biomass ≤100 Mg ha-1 at 25 m resolution in far incidence range in dense deciduous mixed forest of Northern India.

REVEAL: An Extensible Reduced Order Model Builder for Simulation and Modeling

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

Agarwal, Khushbu; Sharma, Poorva; Ma, Jinliang

2013-04-30

Many science domains need to build computationally efficient and accurate representations of high fidelity, computationally expensive simulations. These computationally efficient versions are known as reduced-order models. This paper presents the design and implementation of a novel reduced-order model (ROM) builder, the REVEAL toolset. This toolset generates ROMs based on science- and engineering-domain specific simulations executed on high performance computing (HPC) platforms. The toolset encompasses a range of sampling and regression methods that can be used to generate a ROM, automatically quantifies the ROM accuracy, and provides support for an iterative approach to improve ROM accuracy. REVEAL is designed to bemore » extensible in order to utilize the core functionality with any simulator that has published input and output formats. It also defines programmatic interfaces to include new sampling and regression techniques so that users can ‘mix and match’ mathematical techniques to best suit the characteristics of their model. In this paper, we describe the architecture of REVEAL and demonstrate its usage with a computational fluid dynamics model used in carbon capture.« less

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.

Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images

NASA Astrophysics Data System (ADS)

Fernández-Manso, O.; Fernández-Manso, A.; Quintano, C.

2014-09-01

Aboveground biomass (AGB) estimation from optical satellite data is usually based on regression models of original or synthetic bands. To overcome the poor relation between AGB and spectral bands due to mixed-pixels when a medium spatial resolution sensor is considered, we propose to base the AGB estimation on fraction images from Linear Spectral Mixture Analysis (LSMA). Our study area is a managed Mediterranean pine woodland (Pinus pinaster Ait.) in central Spain. A total of 1033 circular field plots were used to estimate AGB from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) optical data. We applied Pearson correlation statistics and stepwise multiple regression to identify suitable predictors from the set of variables of original bands, fraction imagery, Normalized Difference Vegetation Index and Tasselled Cap components. Four linear models and one nonlinear model were tested. A linear combination of ASTER band 2 (red, 0.630-0.690 μm), band 8 (short wave infrared 5, 2.295-2.365 μm) and green vegetation fraction (from LSMA) was the best AGB predictor (Radj2=0.632, the root-mean-squared error of estimated AGB was 13.3 Mg ha-1 (or 37.7%), resulting from cross-validation), rather than other combinations of the above cited independent variables. Results indicated that using ASTER fraction images in regression models improves the AGB estimation in Mediterranean pine forests. The spatial distribution of the estimated AGB, based on a multiple linear regression model, may be used as baseline information for forest managers in future studies, such as quantifying the regional carbon budget, fuel accumulation or monitoring of management practices.

Predictive modeling of hazardous waste landfill total above-ground biomass using passive optical and LIDAR remotely sensed data

NASA Astrophysics Data System (ADS)

Hadley, Brian Christopher

This dissertation assessed remotely sensed data and geospatial modeling technique(s) to map the spatial distribution of total above-ground biomass present on the surface of the Savannah River National Laboratory's (SRNL) Mixed Waste Management Facility (MWMF) hazardous waste landfill. Ordinary least squares (OLS) regression, regression kriging, and tree-structured regression were employed to model the empirical relationship between in-situ measured Bahia (Paspalum notatum Flugge) and Centipede [Eremochloa ophiuroides (Munro) Hack.] grass biomass against an assortment of explanatory variables extracted from fine spatial resolution passive optical and LIDAR remotely sensed data. Explanatory variables included: (1) discrete channels of visible, near-infrared (NIR), and short-wave infrared (SWIR) reflectance, (2) spectral vegetation indices (SVI), (3) spectral mixture analysis (SMA) modeled fractions, (4) narrow-band derivative-based vegetation indices, and (5) LIDAR derived topographic variables (i.e. elevation, slope, and aspect). Results showed that a linear combination of the first- (1DZ_DGVI), second- (2DZ_DGVI), and third-derivative of green vegetation indices (3DZ_DGVI) calculated from hyperspectral data recorded over the 400--960 nm wavelengths of the electromagnetic spectrum explained the largest percentage of statistical variation (R2 = 0.5184) in the total above-ground biomass measurements. In general, the topographic variables did not correlate well with the MWMF biomass data, accounting for less than five percent of the statistical variation. It was concluded that tree-structured regression represented the optimum geospatial modeling technique due to a combination of model performance and efficiency/flexibility factors.

Stress and health behaviors as potential mediators of the relationship between neighborhood quality and allostatic load.

PubMed

Buschmann, Robert N; Prochaska, John D; Cutchin, Malcolm P; Peek, M Kristen

2018-03-29

Neighborhood quality is associated with health. Increasingly, researchers are focusing on the mechanisms underlying that association, including the role of stress, risky health behaviors, and subclinical measures such as allostatic load (AL). This study uses mixed-effects regression modeling to examine the association between two objective measures and one subjective measure of neighborhood quality and AL in an ethnically diverse population-based sample (N = 2706) from a medium-sized Texas city. We also examine whether several measures of psychological stress and health behaviors mediate any relationship between neighborhood quality and AL. In this sample, all three separate measures of neighborhood quality were associated with individual AL (P < .01). However, only the subjective measure, perceived neighborhood quality, was associated with AL after adjusting for covariates. In mixed-effects multiple regression models there was no evidence of mediation by either stress or health behaviors. In this study, only one measure of neighborhood quality was related to a measure of health, which contrasts with considerable previous research in this area. In this sample, neighborhood quality may affect AL through other mechanisms, or there may be other health-affecting factors is this area that share that overshadow local neighborhood variation. Copyright © 2018 Elsevier Inc. All rights reserved.

Ice cream structural elements that affect melting rate and hardness.

PubMed

Muse, M R; Hartel, R W

2004-01-01

Statistical models were developed to reveal which structural elements of ice cream affect melting rate and hardness. Ice creams were frozen in a batch freezer with three types of sweetener, three levels of the emulsifier polysorbate 80, and two different draw temperatures to produce ice creams with a range of microstructures. Ice cream mixes were analyzed for viscosity, and finished ice creams were analyzed for air cell and ice crystal size, overrun, and fat destabilization. The ice phase volume of each ice cream were calculated based on the freezing point of the mix. Melting rate and hardness of each hardened ice cream was measured and correlated with the structural attributes by using analysis of variance and multiple linear regression. Fat destabilization, ice crystal size, and the consistency coefficient of the mix were found to affect the melting rate of ice cream, whereas hardness was influenced by ice phase volume, ice crystal size, overrun, fat destabilization, and the rheological properties of the mix.

Advanced Statistical Analyses to Reduce Inconsistency of Bond Strength Data.

PubMed

Minamino, T; Mine, A; Shintani, A; Higashi, M; Kawaguchi-Uemura, A; Kabetani, T; Hagino, R; Imai, D; Tajiri, Y; Matsumoto, M; Yatani, H

2017-11-01

This study was designed to clarify the interrelationship of factors that affect the value of microtensile bond strength (µTBS), focusing on nondestructive testing by which information of the specimens can be stored and quantified. µTBS test specimens were prepared from 10 noncarious human molars. Six factors of µTBS test specimens were evaluated: presence of voids at the interface, X-ray absorption coefficient of resin, X-ray absorption coefficient of dentin, length of dentin part, size of adhesion area, and individual differences of teeth. All specimens were observed nondestructively by optical coherence tomography and micro-computed tomography before µTBS testing. After µTBS testing, the effect of these factors on µTBS data was analyzed by the general linear model, linear mixed effects regression model, and nonlinear regression model with 95% confidence intervals. By the general linear model, a significant difference in individual differences of teeth was observed ( P < 0.001). A significantly positive correlation was shown between µTBS and length of dentin part ( P < 0.001); however, there was no significant nonlinearity ( P = 0.157). Moreover, a significantly negative correlation was observed between µTBS and size of adhesion area ( P = 0.001), with significant nonlinearity ( P = 0.014). No correlation was observed between µTBS and X-ray absorption coefficient of resin ( P = 0.147), and there was no significant nonlinearity ( P = 0.089). Additionally, a significantly positive correlation was observed between µTBS and X-ray absorption coefficient of dentin ( P = 0.022), with significant nonlinearity ( P = 0.036). A significant difference was also observed between the presence and absence of voids by linear mixed effects regression analysis. Our results showed correlations between various parameters of tooth specimens and µTBS data. To evaluate the performance of the adhesive more precisely, the effect of tooth variability and a method to reduce variation in bond strength values should also be considered.

Nursing home case-mix reimbursement in Mississippi and South Dakota.

PubMed

Arling, Greg; Daneman, Barry

2002-04-01

To evaluate the effects of nursing home case-mix reimbursement on facility case mix and costs in Mississippi and South Dakota. Secondary data from resident assessments and Medicaid cost reports from 154 Mississippi and 107 South Dakota nursing facilities in 1992 and 1994, before and after implementation of new case-mix reimbursement systems. The study relied on a two-wave panel design to examine case mix (resident acuity) and direct care costs in 1-year periods before and after implementation of a nursing home case-mix reimbursement system. Cross-lagged regression models were used to assess change in case mix and costs between periods while taking into account facility characteristics. Facility-level measures were constructed from Medicaid cost reports and Minimum Data Set-Plus assessment records supplied by each state. Resident case mix was based on the RUG-III classification system. Facility case-mix scores and direct care costs increased significantly between periods in both states. Changes in facility costs and case mix were significantly related in a positive direction. Medicare utilization and the rate of hospitalizations from the nursing facility also increased significantly between periods, particularly in Mississippi. The case-mix reimbursement systems appeared to achieve their intended goals: improved access for heavy-care residents and increased direct care expenditures in facilities with higher acuity residents. However, increases in Medicare utilization may have influenced facility case mix or costs, and some facilities may have been unprepared to care for higher acuity residents, as indicated by increased rates of hospitalization.

Medicaid payment rates, case-mix reimbursement, and nursing home staffing--1996-2004.

PubMed

Feng, Zhanlian; Grabowski, David C; Intrator, Orna; Zinn, Jacqueline; Mor, Vincent

2008-01-01

We examined the impact of state Medicaid payment rates and case-mix reimbursement on direct care staffing levels in US nursing homes. We used a recent time series of national nursing home data from the Online Survey Certification and Reporting system for 1996-2004, merged with annual state Medicaid payment rates and case-mix reimbursement information. A 5-category response measure of total staffing levels was defined according to expert recommended thresholds, and examined in a multinomial logistic regression model. Facility fixed-effects models were estimated separately for Registered Nurse (RN), Licensed Practical Nurse (LPN), and Certified Nurse Aide (CNA) staffing levels measured as average hours per resident day. Higher Medicaid payment rates were associated with increases in total staffing levels to meet a higher recommended threshold. However, these gains in overall staffing were accompanied by a reduction of RN staffing and an increase in both LPN and CNA staffing levels. Under case-mix reimbursement, the likelihood of nursing homes achieving higher recommended staffing thresholds decreased, as did levels of professional staffing. Independent of the effects of state, market, and facility characteristics, there was a significant downward trend in RN staffing and an upward trend in both LPN and CNA staffing. Although overall staffing may increase in response to more generous Medicaid reimbursement, it may not translate into improvements in the skill mix of staff. Adjusting for reimbursement levels and resident acuity, total staffing has not increased after the implementation of case-mix reimbursement.

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.

Statistical classification of drug incidents due to look-alike sound-alike mix-ups.

PubMed

Wong, Zoie Shui Yee

2016-06-01

It has been recognised that medication names that look or sound similar are a cause of medication errors. This study builds statistical classifiers for identifying medication incidents due to look-alike sound-alike mix-ups. A total of 227 patient safety incident advisories related to medication were obtained from the Canadian Patient Safety Institute's Global Patient Safety Alerts system. Eight feature selection strategies based on frequent terms, frequent drug terms and constituent terms were performed. Statistical text classifiers based on logistic regression, support vector machines with linear, polynomial, radial-basis and sigmoid kernels and decision tree were trained and tested. The models developed achieved an average accuracy of above 0.8 across all the model settings. The receiver operating characteristic curves indicated the classifiers performed reasonably well. The results obtained in this study suggest that statistical text classification can be a feasible method for identifying medication incidents due to look-alike sound-alike mix-ups based on a database of advisories from Global Patient Safety Alerts. © The Author(s) 2014.

How to test validity in orthodontic research: a mixed dentition analysis example.

PubMed

Donatelli, Richard E; Lee, Shin-Jae

2015-02-01

The data used to test the validity of a prediction method should be different from the data used to generate the prediction model. In this study, we explored whether an independent data set is mandatory for testing the validity of a new prediction method and how validity can be tested without independent new data. Several validation methods were compared in an example using the data from a mixed dentition analysis with a regression model. The validation errors of real mixed dentition analysis data and simulation data were analyzed for increasingly large data sets. The validation results of both the real and the simulation studies demonstrated that the leave-1-out cross-validation method had the smallest errors. The largest errors occurred in the traditional simple validation method. The differences between the validation methods diminished as the sample size increased. The leave-1-out cross-validation method seems to be an optimal validation method for improving the prediction accuracy in a data set with limited sample sizes. Copyright © 2015 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Motion patterns in acupuncture needle manipulation.

PubMed

Seo, Yoonjeong; Lee, In-Seon; Jung, Won-Mo; Ryu, Ho-Sun; Lim, Jinwoong; Ryu, Yeon-Hee; Kang, Jung-Won; Chae, Younbyoung

2014-10-01

In clinical practice, acupuncture manipulation is highly individualised for each practitioner. Before we establish a standard for acupuncture manipulation, it is important to understand completely the manifestations of acupuncture manipulation in the actual clinic. To examine motion patterns during acupuncture manipulation, we generated a fitted model of practitioners' motion patterns and evaluated their consistencies in acupuncture manipulation. Using a motion sensor, we obtained real-time motion data from eight experienced practitioners while they conducted acupuncture manipulation using their own techniques. We calculated the average amplitude and duration of a sampled motion unit for each practitioner and, after normalisation, we generated a true regression curve of motion patterns for each practitioner using a generalised additive mixed modelling (GAMM). We observed significant differences in rotation amplitude and duration in motion samples among practitioners. GAMM showed marked variations in average regression curves of motion patterns among practitioners but there was strong consistency in motion parameters for individual practitioners. The fitted regression model showed that the true regression curve accounted for an average of 50.2% of variance in the motion pattern for each practitioner. Our findings suggest that there is great inter-individual variability between practitioners, but remarkable intra-individual consistency within each practitioner. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Multi Objective Optimization of Multi Wall Carbon Nanotube Based Nanogrinding Wheel Using Grey Relational and Regression Analysis

NASA Astrophysics Data System (ADS)

Sethuramalingam, Prabhu; Vinayagam, Babu Kupusamy

2016-07-01

Carbon nanotube mixed grinding wheel is used in the grinding process to analyze the surface characteristics of AISI D2 tool steel material. Till now no work has been carried out using carbon nanotube based grinding wheel. Carbon nanotube based grinding wheel has excellent thermal conductivity and good mechanical properties which are used to improve the surface finish of the workpiece. In the present study, the multi response optimization of process parameters like surface roughness and metal removal rate of grinding process of single wall carbon nanotube (CNT) in mixed cutting fluids is undertaken using orthogonal array with grey relational analysis. Experiments are performed with designated grinding conditions obtained using the L9 orthogonal array. Based on the results of the grey relational analysis, a set of optimum grinding parameters is obtained. Using the analysis of variance approach the significant machining parameters are found. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared empirically, for conditions of with and without CNT grinding wheel in grinding process.

Composition and structure of Pinus koraiensis mixed forest respond to spatial climatic changes.

PubMed

Zhang, Jingli; Zhou, Yong; Zhou, Guangsheng; Xiao, Chunwang

2014-01-01

Although some studies have indicated that climate changes can affect Pinus koraiensis mixed forest, the responses of composition and structure of Pinus koraiensis mixed forests to climatic changes are unknown and the key climatic factors controlling the composition and structure of Pinus koraiensis mixed forest are uncertain. Field survey was conducted in the natural Pinus koraiensis mixed forests along a latitudinal gradient and an elevational gradient in Northeast China. In order to build the mathematical models for simulating the relationships of compositional and structural attributes of the Pinus koraiensis mixed forest with climatic and non-climatic factors, stepwise linear regression analyses were performed, incorporating 14 dependent variables and the linear and quadratic components of 9 factors. All the selected new models were computed under the +2°C and +10% precipitation and +4°C and +10% precipitation scenarios. The Max Temperature of Warmest Month, Mean Temperature of Warmest Quarter and Precipitation of Wettest Month were observed to be key climatic factors controlling the stand densities and total basal areas of Pinus koraiensis mixed forest. Increased summer temperatures and precipitations strongly enhanced the stand densities and total basal areas of broadleaf trees but had little effect on Pinus koraiensis under the +2°C and +10% precipitation scenario and +4°C and +10% precipitation scenario. These results show that the Max Temperature of Warmest Month, Mean Temperature of Warmest Quarter and Precipitation of Wettest Month are key climatic factors which shape the composition and structure of Pinus koraiensis mixed forest. Although the Pinus koraiensis would persist, the current forests dominated by Pinus koraiensis in the region would all shift and become broadleaf-dominated forests due to the dramatic increase of broadleaf trees under the future global warming and increased precipitation.

Investigation of predictors affecting food mixing ability in mandibulectomy and/or glossectomy patients.

PubMed

Otomaru, Takafumi; Sumita, Yuka I; Chang, Qingan; Fueki, Kenji; Igarashi, Yoshimasa; Taniguchi, Hisashi

2009-07-01

Several previous reports have described factors that affect masticatory function. However, there are no known predictors that affect the food mixing ability of the masticatory function, and it has been impossible to predict masticatory function in mandibulectomy and/or glossectomy patients. The purpose of the present study was to develop a numerical formula that could predict the food mixing ability of the masticatory function among mandibulectomy and/or glossectomy patients. The null hypothesis of the study was that five predictors, namely mandibulectomy, mandibular continuity, number of residual mandibular teeth, occlusal units and tongue movement score, were unable to account for the mixing ability index (MAI) in mandibulectomy and/or glossectomy patients. The subjects were 20 patients who had undergone mandibulectomy and/or glossectomy. The above-described five predictors were assessed. Tongue movement was evaluated with a tongue movement test and the MAI was evaluated with a mixing ability test. Multiple regression analysis was used to examine whether the five predictors affected the MAI after prosthetic treatment. A regression equation was determined for the five predictors (R(2)=0.83; adjusted R(2)=0.77; p<0.001). The obtained regression equation could successfully account for the MAI in mandibulectomy and/or glossectomy patients.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

PubMed Central

Koerner, Tess K.; Zhang, Yang

2017-01-01

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers. PMID:28264422

Hospital financial performance: does IT governance make a difference?

PubMed

Burke, Darrell; Randeree, Ebrahim; Menachemi, Nir; Brooks, Robert G

2008-01-01

This study examined whether information technology (IT) governance, a term describing the decision authority and reporting structures of the chief information officer (CIO), is related to the financial performance of hospitals. The study was conducted using a combination of primary survey data regarding health care IT adoption and reporting structures of Florida acute care hospitals, with secondary data on hospital financial performance. Multiple regression models were used to evaluate the relationship of the 3 most commonly identified reporting structures. Outcome variables included measures of operating revenue and operating expense. All models controlled for overall IT adoption, ownership, membership in a hospital system, case mix, and hospital bed size. The results suggest that IT governance matters when it comes to hospital financial performance. Reporting to the chief financial officer brings positive outcomes; reporting to the chief executive officer has a mixed financial result; and reporting to the chief operating officer was not associated with discernible financial impact.

Perceived Risk of Burglary and Fear of Crime: Individual- and Country-Level Mixed Modeling.

PubMed

Chon, Don Soo; Wilson, Mary

2016-02-01

Given the scarcity of prior studies, the current research introduced country-level variables, along with individual-level ones, to test how they are related to an individual's perceived risk of burglary (PRB) and fear of crime (FC), separately, by using mixed-level logistic regression analyses. The analyses of 104,218 individuals, residing in 50 countries, showed that country-level poverty was positively associated with FC only. However, individual-level variables, such as prior property crime victimization and female gender, had consistently positive relationships with both PRB and FC. However, age group and socioeconomic status were inconsistent between those two models, suggesting that PRB and FC are two different concepts. Finally, no significant difference in the pattern of PRB and FC was found between a highly developed group of countries and a less developed one. © The Author(s) 2014.

Pectin methyl esterase and natural microflora of fresh mixed orange and carrot juice treated with pulsed electric fields.

PubMed

Rodrigo, D; Barbosa-Cánovas, G V; Martínez, A; Rodrigo, M

2003-12-01

The effects of pulsed electric fields (PEFs) on pectin methyl esterase (PME), molds and yeast, and total flora in fresh (nonpasteurized) mixed orange and carrot juice were studied. The PEF effect was more extensive when juices with high levels of initial PME activity were subjected to treatment and when PEF treatment (at 25 kV/cm for 340 micros) was combined with a moderate temperature (63 degrees C), with the maximum level of PME inactivation being 81.4%. These conditions produced 3.7 decimal reductions in molds and yeast and 2.4 decimal reductions in total flora. Experimental inactivation data for PME, molds and yeast, and total flora were fitted to Bigelow, Hülsheger, and Weibull inactivation models by nonlinear regression. The best fit (lowest mean square error) was obtained with the Weibull model.

The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis

PubMed Central

Malosetti, Marcos; Ribaut, Jean-Marcel; van Eeuwijk, Fred A.

2013-01-01

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay–Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as “Appendix.” PMID:23487515

Performance of the score systems Acute Physiology and Chronic Health Evaluation II and III at an interdisciplinary intensive care unit, after customization

PubMed Central

Markgraf, Rainer; Deutschinoff, Gerd; Pientka, Ludger; Scholten, Theo; Lorenz, Cristoph

2001-01-01

Background: Mortality predictions calculated using scoring scales are often not accurate in populations other than those in which the scales were developed because of differences in case-mix. The present study investigates the effect of first-level customization, using a logistic regression technique, on discrimination and calibration of the Acute Physiology and Chronic Health Evaluation (APACHE) II and III scales. Method: Probabilities of hospital death for patients were estimated by applying APACHE II and III and comparing these with observed outcomes. Using the split sample technique, a customized model to predict outcome was developed by logistic regression. The overall goodness-of-fit of the original and the customized models was assessed. Results: Of 3383 consecutive intensive care unit (ICU) admissions over 3 years, 2795 patients could be analyzed, and were split randomly into development and validation samples. The discriminative powers of APACHE II and III were unchanged by customization (areas under the receiver operating characteristic [ROC] curve 0.82 and 0.85, respectively). Hosmer-Lemeshow goodness-of-fit tests showed good calibration for APACHE II, but insufficient calibration for APACHE III. Customization improved calibration for both models, with a good fit for APACHE III as well. However, fit was different for various subgroups. Conclusions: The overall goodness-of-fit of APACHE III mortality prediction was improved significantly by customization, but uniformity of fit in different subgroups was not achieved. Therefore, application of the customized model provides no advantage, because differences in case-mix still limit comparisons of quality of care. PMID:11178223

Breaking from binaries - using a sequential mixed methods design.

PubMed

Larkin, Patricia Mary; Begley, Cecily Marion; Devane, Declan

2014-03-01

To outline the traditional worldviews of healthcare research and discuss the benefits and challenges of using mixed methods approaches in contributing to the development of nursing and midwifery knowledge. There has been much debate about the contribution of mixed methods research to nursing and midwifery knowledge in recent years. A sequential exploratory design is used as an exemplar of a mixed methods approach. The study discussed used a combination of focus-group interviews and a quantitative instrument to obtain a fuller understanding of women's experiences of childbirth. In the mixed methods study example, qualitative data were analysed using thematic analysis and quantitative data using regression analysis. Polarised debates about the veracity, philosophical integrity and motivation for conducting mixed methods research have largely abated. A mixed methods approach can contribute to a deeper, more contextual understanding of a variety of subjects and experiences; as a result, it furthers knowledge that can be used in clinical practice. The purpose of the research study should be the main instigator when choosing from an array of mixed methods research designs. Mixed methods research offers a variety of models that can augment investigative capabilities and provide richer data than can a discrete method alone. This paper offers an example of an exploratory, sequential approach to investigating women's childbirth experiences. A clear framework for the conduct and integration of the different phases of the mixed methods research process is provided. This approach can be used by practitioners and policy makers to improve practice.

College quality and hourly wages: evidence from the self-revelation model, sibling models and instrumental variables.

PubMed

Borgen, Nicolai T

2014-11-01

This paper addresses the recent discussion on confounding in the returns to college quality literature using the Norwegian case. The main advantage of studying Norway is the quality of the data. Norwegian administrative data provide information on college applications, family relations and a rich set of control variables for all Norwegian citizens applying to college between 1997 and 2004 (N = 141,319) and their succeeding wages between 2003 and 2010 (676,079 person-year observations). With these data, this paper uses a subset of the models that have rendered mixed findings in the literature in order to investigate to what extent confounding biases the returns to college quality. I compare estimates obtained using standard regression models to estimates obtained using the self-revelation model of Dale and Krueger (2002), a sibling fixed effects model and the instrumental variable model used by Long (2008). Using these methods, I consistently find increasing returns to college quality over the course of students' work careers, with positive returns only later in students' work careers. I conclude that the standard regression estimate provides a reasonable estimate of the returns to college quality. Copyright © 2014 Elsevier Inc. All rights reserved.

Study of non-Hodgkin's lymphoma mortality associated with industrial pollution in Spain, using Poisson models

PubMed Central

Ramis, Rebeca; Vidal, Enrique; García-Pérez, Javier; Lope, Virginia; Aragonés, Nuria; Pérez-Gómez, Beatriz; Pollán, Marina; López-Abente, Gonzalo

2009-01-01

Background Non-Hodgkin's lymphomas (NHLs) have been linked to proximity to industrial areas, but evidence regarding the health risk posed by residence near pollutant industries is very limited. The European Pollutant Emission Register (EPER) is a public register that furnishes valuable information on industries that release pollutants to air and water, along with their geographical location. This study sought to explore the relationship between NHL mortality in small areas in Spain and environmental exposure to pollutant emissions from EPER-registered industries, using three Poisson-regression-based mathematical models. Methods Observed cases were drawn from mortality registries in Spain for the period 1994–2003. Industries were grouped into the following sectors: energy; metal; mineral; organic chemicals; waste; paper; food; and use of solvents. Populations having an industry within a radius of 1, 1.5, or 2 kilometres from the municipal centroid were deemed to be exposed. Municipalities outside those radii were considered as reference populations. The relative risks (RRs) associated with proximity to pollutant industries were estimated using the following methods: Poisson Regression; mixed Poisson model with random provincial effect; and spatial autoregressive modelling (BYM model). Results Only proximity of paper industries to population centres (>2 km) could be associated with a greater risk of NHL mortality (mixed model: RR:1.24, 95% CI:1.09–1.42; BYM model: RR:1.21, 95% CI:1.01–1.45; Poisson model: RR:1.16, 95% CI:1.06–1.27). Spatial models yielded higher estimates. Conclusion The reported association between exposure to air pollution from the paper, pulp and board industry and NHL mortality is independent of the model used. Inclusion of spatial random effects terms in the risk estimate improves the study of associations between environmental exposures and mortality. The EPER could be of great utility when studying the effects of industrial pollution on the health of the population. PMID:19159450

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach

PubMed Central

Kneifel, Joshua; Webb, David

2016-01-01

Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF. PMID:27956756

Predicting Energy Performance of a Net-Zero Energy Building: A Statistical Approach.

PubMed

Kneifel, Joshua; Webb, David

2016-09-01

Performance-based building requirements have become more prevalent because it gives freedom in building design while still maintaining or exceeding the energy performance required by prescriptive-based requirements. In order to determine if building designs reach target energy efficiency improvements, it is necessary to estimate the energy performance of a building using predictive models and different weather conditions. Physics-based whole building energy simulation modeling is the most common approach. However, these physics-based models include underlying assumptions and require significant amounts of information in order to specify the input parameter values. An alternative approach to test the performance of a building is to develop a statistically derived predictive regression model using post-occupancy data that can accurately predict energy consumption and production based on a few common weather-based factors, thus requiring less information than simulation models. A regression model based on measured data should be able to predict energy performance of a building for a given day as long as the weather conditions are similar to those during the data collection time frame. This article uses data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF) to develop and validate a regression model to predict the energy performance of the NZERTF using two weather variables aggregated to the daily level, applies the model to estimate the energy performance of hypothetical NZERTFs located in different cities in the Mixed-Humid climate zone, and compares these estimates to the results from already existing EnergyPlus whole building energy simulations. This regression model exhibits agreement with EnergyPlus predictive trends in energy production and net consumption, but differs greatly in energy consumption. The model can be used as a framework for alternative and more complex models based on the experimental data collected from the NZERTF.

Effects of morphological Family Size for young readers.

PubMed

Perdijk, Kors; Schreuder, Robert; Baayen, R Harald; Verhoeven, Ludo

2012-09-01

Dutch children, from the second and fourth grade of primary school, were each given a visual lexical decision test on 210 Dutch monomorphemic words. After removing words not recognized by a majority of the younger group, (lexical) decisions were analysed by mixed-model regression methods to see whether morphological Family Size influenced decision times over and above several other covariates. The effect of morphological Family Size on decision time was mixed: larger families led to significantly faster decision times for the second graders but not for the fourth graders. Since facilitative effects on decision times had been found for adults, we offer a developmental account to explain the absence of an effect of Family Size on decision times for fourth graders. ©2011 The British Psychological Society.

Differentiating Tumor Progression from Pseudoprogression in Patients with Glioblastomas Using Diffusion Tensor Imaging and Dynamic Susceptibility Contrast MRI.

PubMed

Wang, S; Martinez-Lage, M; Sakai, Y; Chawla, S; Kim, S G; Alonso-Basanta, M; Lustig, R A; Brem, S; Mohan, S; Wolf, R L; Desai, A; Poptani, H

2016-01-01

Early assessment of treatment response is critical in patients with glioblastomas. A combination of DTI and DSC perfusion imaging parameters was evaluated to distinguish glioblastomas with true progression from mixed response and pseudoprogression. Forty-one patients with glioblastomas exhibiting enhancing lesions within 6 months after completion of chemoradiation therapy were retrospectively studied. All patients underwent surgery after MR imaging and were histologically classified as having true progression (>75% tumor), mixed response (25%-75% tumor), or pseudoprogression (<25% tumor). Mean diffusivity, fractional anisotropy, linear anisotropy coefficient, planar anisotropy coefficient, spheric anisotropy coefficient, and maximum relative cerebral blood volume values were measured from the enhancing tissue. A multivariate logistic regression analysis was used to determine the best model for classification of true progression from mixed response or pseudoprogression. Significantly elevated maximum relative cerebral blood volume, fractional anisotropy, linear anisotropy coefficient, and planar anisotropy coefficient and decreased spheric anisotropy coefficient were observed in true progression compared with pseudoprogression (P < .05). There were also significant differences in maximum relative cerebral blood volume, fractional anisotropy, planar anisotropy coefficient, and spheric anisotropy coefficient measurements between mixed response and true progression groups. The best model to distinguish true progression from non-true progression (pseudoprogression and mixed) consisted of fractional anisotropy, linear anisotropy coefficient, and maximum relative cerebral blood volume, resulting in an area under the curve of 0.905. This model also differentiated true progression from mixed response with an area under the curve of 0.901. A combination of fractional anisotropy and maximum relative cerebral blood volume differentiated pseudoprogression from nonpseudoprogression (true progression and mixed) with an area under the curve of 0.807. DTI and DSC perfusion imaging can improve accuracy in assessing treatment response and may aid in individualized treatment of patients with glioblastomas. © 2016 by American Journal of Neuroradiology.

Superquantile/CVaR Risk Measures: Second-Order Theory

DTIC Science & Technology

2014-07-17

order version of quantile regression . Keywords: superquantiles, conditional value-at-risk, second-order superquantiles, mixed superquan- tiles... quantile regression . 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 26 19a...second-order superquantiles is in the domain of generalized regression . We laid out in [16] a parallel methodology to that of quantile regression

Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes.

PubMed

Cook, James P; Mahajan, Anubha; Morris, Andrew P

2017-02-01

Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population stratification and relatedness through inclusion of random effects for a genetic relationship matrix. However, the utility of linear (mixed) models in the context of meta-analysis of GWAS of binary phenotypes has not been previously explored. In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, considering designs that incorporate variable case-control imbalance, confounding factors and population stratification. Our results demonstrate that linear models can be used for meta-analysis of GWAS of binary phenotypes, without loss of power, even in the presence of extreme case-control imbalance, provided that one of the following schemes is used: (i) effective sample size weighting of Z-scores or (ii) inverse-variance weighting of allelic effect sizes after conversion onto the log-odds scale. Our conclusions thus provide essential recommendations for the development of robust protocols for meta-analysis of binary phenotypes with linear models.

Evaluation of aroma enhancement for "Ecolly" dry white wines by mixed inoculation of selected Rhodotorula mucilaginosa and Saccharomyces cerevisiae.

PubMed

Wang, Xing-Chen; Li, Ai-Hua; Dizy, Marta; Ullah, Niamat; Sun, Wei-Xuan; Tao, Yong-Sheng

2017-08-01

To improve the aroma profile of Ecolly dry white wine, the simultaneous and sequential inoculations of selected Rhodotorula mucilaginosa and Saccharomyces cerevisiae were performed in wine making of this work. The two yeasts were mixed in various ratios for making the mixed inoculum. The amount of volatiles and aroma characteristics were determined the following year. Mixed fermentation improved both the varietal and fermentative aroma compound composition, especially that of (Z)-3-hexene-1-ol, nerol oxide, certain acetates and ethyls group compounds. Citrus, sweet fruit, acid fruit, berry, and floral aroma traits were enhanced by mixed fermentation; however, an animal note was introduced upon using higher amounts of R. mucilaginosa. Aroma traits were regressed with volatiles as observed by the partial least-square regression method. Analysis of correlation coefficients revealed that the aroma traits were the multiple interactions of volatile compounds, with the fermentative volatiles having more impact on aroma than varietal compounds. Copyright © 2017 Elsevier Ltd. All rights reserved.

Identifying the optimal segmentors for mass classification in mammograms

NASA Astrophysics Data System (ADS)

Zhang, Yu; Tomuro, Noriko; Furst, Jacob; Raicu, Daniela S.

2015-03-01

In this paper, we present the results of our investigation on identifying the optimal segmentor(s) from an ensemble of weak segmentors, used in a Computer-Aided Diagnosis (CADx) system which classifies suspicious masses in mammograms as benign or malignant. This is an extension of our previous work, where we used various parameter settings of image enhancement techniques to each suspicious mass (region of interest (ROI)) to obtain several enhanced images, then applied segmentation to each image to obtain several contours of a given mass. Each segmentation in this ensemble is essentially a "weak segmentor" because no single segmentation can produce the optimal result for all images. Then after shape features are computed from the segmented contours, the final classification model was built using logistic regression. The work in this paper focuses on identifying the optimal segmentor(s) from an ensemble mix of weak segmentors. For our purpose, optimal segmentors are those in the ensemble mix which contribute the most to the overall classification rather than the ones that produced high precision segmentation. To measure the segmentors' contribution, we examined weights on the features in the derived logistic regression model and computed the average feature weight for each segmentor. The result showed that, while in general the segmentors with higher segmentation success rates had higher feature weights, some segmentors with lower segmentation rates had high classification feature weights as well.

Pre-natal exposures to cocaine and alcohol and physical growth patterns to age 8 years

PubMed Central

Lumeng, Julie C.; Cabral, Howard J.; Gannon, Katherine; Heeren, Timothy; Frank, Deborah A.

2007-01-01

Two hundred and two primarily African American/Caribbean children (classified by maternal report and infant meconium as 38 heavier, 74 lighter and 89 not cocaine-exposed) were measured repeatedly from birth to age 8 years to assess whether there is an independent effect of prenatal cocaine exposure on physical growth patterns. Children with fetal alcohol syndrome identifiable at birth were excluded. At birth, cocaine and alcohol exposures were significantly and independently associated with lower weight, length and head circumference in cross-sectional multiple regression analyses. The relationship over time of pre-natal exposures to weight, height, and head circumference was then examined by multiple linear regression using mixed linear models including covariates: child’s gestational age, gender, ethnicity, age at assessment, current caregiver, birth mother’s use of alcohol, marijuana and tobacco during the pregnancy and pre-pregnancy weight (for child’s weight) and height (for child’s height and head circumference). The cocaine effects did not persist beyond infancy in piecewise linear mixed models, but a significant and independent negative effect of pre-natal alcohol exposure persisted for weight, height, and head circumference. Catch-up growth in cocaine-exposed infants occurred primarily by 6 months of age for all growth parameters, with some small fluctuations in growth rates in the preschool age range but no detectable differences between heavier versus unexposed nor lighter versus unexposed thereafter. PMID:17412558

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Quantifying the safety effects of horizontal curves on two-way, two-lane rural roads.

PubMed

Gooch, Jeffrey P; Gayah, Vikash V; Donnell, Eric T

2016-07-01

The objective of this study is to quantify the safety performance of horizontal curves on two-way, two-lane rural roads relative to tangent segments. Past research is limited by small samples sizes, outdated statistical evaluation methods, and unreported standard errors. This study overcomes these drawbacks by using the propensity scores-potential outcomes framework. The impact of adjacent curves on horizontal curve safety is also explored using a cross-sectional regression model of only horizontal curves. The models estimated in the present study used eight years of crash data (2005-2012) obtained from over 10,000 miles of state-owned two-lane rural roads in Pennsylvania. These data included information on roadway geometry (e.g., horizontal curvature, lane width, and shoulder width), traffic volume, roadside hazard rating, and the presence of various low-cost safety countermeasures (e.g., centerline and shoulder rumble strips, curve and intersection warning pavement markings, and aggressive driving pavement dots). Crash prediction is performed by means of mixed effects negative binomial regression using the explanatory variables noted previously, as well as attributes of adjacent horizontal curves. The results indicate that both the presence of a horizontal curve and its degree of curvature must be considered when predicting the frequency of total crashes on horizontal curves. Both are associated with an increase in crash frequency, which is consistent with previous findings in the literature. Mixed effects negative binomial regression models for total crash frequency on horizontal curves indicate that the distance to adjacent curves is not statistically significant. However, the degree of curvature of adjacent curves in close proximity (within 0.75 miles) was found to be statistically significant and negatively correlated with crash frequency on the subject curve. This is logical, as drivers exiting a sharp curve are likely to be driving slower and with more awareness as they approach the next horizontal curve. Copyright © 2016 Elsevier Ltd. All rights reserved.

Breast Radiotherapy with Mixed Energy Photons; a Model for Optimal Beam Weighting.

PubMed

Birgani, Mohammadjavad Tahmasebi; Fatahiasl, Jafar; Hosseini, Seyed Mohammad; Bagheri, Ali; Behrooz, Mohammad Ali; Zabiehzadeh, Mansour; Meskani, Reza; Gomari, Maryam Talaei

2015-01-01

Utilization of high energy photons (>10 MV) with an optimal weight using a mixed energy technique is a practical way to generate a homogenous dose distribution while maintaining adequate target coverage in intact breast radiotherapy. This study represents a model for estimation of this optimal weight for day to day clinical usage. For this purpose, treatment planning computed tomography scans of thirty-three consecutive early stage breast cancer patients following breast conservation surgery were analyzed. After delineation of the breast clinical target volume (CTV) and placing opposed wedge paired isocenteric tangential portals, dosimeteric calculations were conducted and dose volume histograms (DVHs) were generated, first with pure 6 MV photons and then these calculations were repeated ten times with incorporating 18 MV photons (ten percent increase in weight per step) in each individual patient. For each calculation two indexes including maximum dose in the breast CTV (Dmax) and the volume of CTV which covered with 95% Isodose line (VCTV, 95%IDL) were measured according to the DVH data and then normalized values were plotted in a graph. The optimal weight of 18 MV photons was defined as the intersection point of Dmax and VCTV, 95%IDL graphs. For creating a model to predict this optimal weight multiple linear regression analysis was used based on some of the breast and tangential field parameters. The best fitting model for prediction of 18 MV photons optimal weight in breast radiotherapy using mixed energy technique, incorporated chest wall separation plus central lung distance (Adjusted R2=0.776). In conclusion, this study represents a model for the estimation of optimal beam weighting in breast radiotherapy using mixed photon energy technique for routine day to day clinical usage.

Open-target sparse sensing of biological agents using DNA microarray

PubMed Central

2011-01-01

Background Current biosensors are designed to target and react to specific nucleic acid sequences or structural epitopes. These 'target-specific' platforms require creation of new physical capture reagents when new organisms are targeted. An 'open-target' approach to DNA microarray biosensing is proposed and substantiated using laboratory generated data. The microarray consisted of 12,900 25 bp oligonucleotide capture probes derived from a statistical model trained on randomly selected genomic segments of pathogenic prokaryotic organisms. Open-target detection of organisms was accomplished using a reference library of hybridization patterns for three test organisms whose DNA sequences were not included in the design of the microarray probes. Results A multivariate mathematical model based on the partial least squares regression (PLSR) was developed to detect the presence of three test organisms in mixed samples. When all 12,900 probes were used, the model correctly detected the signature of three test organisms in all mixed samples (mean(R2)) = 0.76, CI = 0.95), with a 6% false positive rate. A sampling algorithm was then developed to sparsely sample the probe space for a minimal number of probes required to capture the hybridization imprints of the test organisms. The PLSR detection model was capable of correctly identifying the presence of the three test organisms in all mixed samples using only 47 probes (mean(R2)) = 0.77, CI = 0.95) with nearly 100% specificity. Conclusions We conceived an 'open-target' approach to biosensing, and hypothesized that a relatively small, non-specifically designed, DNA microarray is capable of identifying the presence of multiple organisms in mixed samples. Coupled with a mathematical model applied to laboratory generated data, and sparse sampling of capture probes, the prototype microarray platform was able to capture the signature of each organism in all mixed samples with high sensitivity and specificity. It was demonstrated that this new approach to biosensing closely follows the principles of sparse sensing. PMID:21801424

The long-solved problem of the best-fit straight line: application to isotopic mixing lines

NASA Astrophysics Data System (ADS)

Wehr, Richard; Saleska, Scott R.

2017-01-01

It has been almost 50 years since York published an exact and general solution for the best-fit straight line to independent points with normally distributed errors in both x and y. York's solution is highly cited in the geophysical literature but almost unknown outside of it, so that there has been no ebb in the tide of books and papers wrestling with the problem. Much of the post-1969 literature on straight-line fitting has sown confusion not merely by its content but by its very existence. The optimal least-squares fit is already known; the problem is already solved. Here we introduce the non-specialist reader to York's solution and demonstrate its application in the interesting case of the isotopic mixing line, an analytical tool widely used to determine the isotopic signature of trace gas sources for the study of biogeochemical cycles. The most commonly known linear regression methods - ordinary least-squares regression (OLS), geometric mean regression (GMR), and orthogonal distance regression (ODR) - have each been recommended as the best method for fitting isotopic mixing lines. In fact, OLS, GMR, and ODR are all special cases of York's solution that are valid only under particular measurement conditions, and those conditions do not hold in general for isotopic mixing lines. Using Monte Carlo simulations, we quantify the biases in OLS, GMR, and ODR under various conditions and show that York's general - and convenient - solution is always the least biased.

Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

NASA Astrophysics Data System (ADS)

Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

2016-11-01

The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

Risk adjustment models for short-term outcomes after surgical resection for oesophagogastric cancer.

PubMed

Fischer, C; Lingsma, H; Hardwick, R; Cromwell, D A; Steyerberg, E; Groene, O

2016-01-01

Outcomes for oesophagogastric cancer surgery are compared with the aim of benchmarking quality of care. Adjusting for patient characteristics is crucial to avoid biased comparisons between providers. The study objective was to develop a case-mix adjustment model for comparing 30- and 90-day mortality and anastomotic leakage rates after oesophagogastric cancer resections. The study reviewed existing models, considered expert opinion and examined audit data in order to select predictors that were consequently used to develop a case-mix adjustment model for the National Oesophago-Gastric Cancer Audit, covering England and Wales. Models were developed on patients undergoing surgical resection between April 2011 and March 2013 using logistic regression. Model calibration and discrimination was quantified using a bootstrap procedure. Most existing risk models for oesophagogastric resections were methodologically weak, outdated or based on detailed laboratory data that are not generally available. In 4882 patients with oesophagogastric cancer used for model development, 30- and 90-day mortality rates were 2·3 and 4·4 per cent respectively, and 6·2 per cent of patients developed an anastomotic leak. The internally validated models, based on predictors selected from the literature, showed moderate discrimination (area under the receiver operating characteristic (ROC) curve 0·646 for 30-day mortality, 0·664 for 90-day mortality and 0·587 for anastomotic leakage) and good calibration. Based on available data, three case-mix adjustment models for postoperative outcomes in patients undergoing curative surgery for oesophagogastric cancer were developed. These models should be used for risk adjustment when assessing hospital performance in the National Health Service, and tested in other large health systems. © 2015 BJS Society Ltd Published by John Wiley & Sons Ltd.

The "Nursing Home Compare" measure of urinary/fecal incontinence: cross-sectional variation, stability over time, and the impact of case mix.

PubMed

Li, Yue; Schnelle, John; Spector, William D; Glance, Laurent G; Mukamel, Dana B

2010-02-01

To assess the impact of facility case mix on cross-sectional variations and short-term stability of the "Nursing Home Compare" incontinence quality measure (QM) and to determine whether multivariate risk adjustment can minimize such impacts. Retrospective analyses of the 2005 national minimum data set (MDS) that included approximately 600,000 long-term care residents in over 10,000 facilities in each quarterly sample. Mixed logistic regression was used to construct the risk-adjusted QM (nonshrinkage estimator). Facility-level ordinary least-squares models and adjusted R(2) were used to estimate the impact of case mix on cross-sectional and short-term longitudinal variations of currently published and risk-adjusted QMs. At least 50 percent of the cross-sectional variation and 25 percent of the short-term longitudinal variation of the published QM are explained by facility case mix. In contrast, the cross-sectional and short-term longitudinal variations of the risk-adjusted QM are much less susceptible to case-mix variations (adjusted R(2)<0.10), even for facilities with more extreme or more unstable outcome. Current "Nursing Home Compare" incontinence QM reflects considerable case-mix variations across facilities and over time, and therefore it may be biased. This issue can be largely addressed by multivariate risk adjustment using risk factors available in the MDS.

A novel approach to mixing qualitative and quantitative methods in HIV and STI prevention research.

PubMed

Penman-Aguilar, Ana; Macaluso, Maurizio; Peacock, Nadine; Snead, M Christine; Posner, Samuel F

2014-04-01

Mixed-method designs are increasingly used in sexually transmitted infection (STI) and HIV prevention research. The authors designed a mixedmethod approach and applied it to estimate and evaluate a predictor of continued female condom use (6+ uses, among those who used it at least once) in a 6-month prospective cohort study. The analysis included 402 women who received an intervention promoting use of female and male condoms for STI prevention and completed monthly quantitative surveys; 33 also completed a semistructured qualitative interview. The authors identified a qualitative theme (couples' female condom enjoyment [CFCE]), applied discriminant analysis techniques to estimate CFCE for all participants, and added CFCE to a multivariable logistic regression model of continued female condom use. CFCE related to comfort, naturalness, pleasure, feeling protected, playfulness, ease of use, intimacy, and feeling in control of protection. CFCE was associated with continued female condom use (adjusted odds ratio: 2.8, 95% confidence interval: 1.4-5.6) and significantly improved model fit (p < .001). CFCE predicted continued female condom use. Mixed-method approaches for "scaling up" qualitative findings from small samples to larger numbers of participants can benefit HIV and STI prevention research.

Bus accident analysis of routes with/without bus priority.

PubMed

Goh, Kelvin Chun Keong; Currie, Graham; Sarvi, Majid; Logan, David

2014-04-01

This paper summarises findings on road safety performance and bus-involved accidents in Melbourne along roads where bus priority measures had been applied. Results from an empirical analysis of the accident types revealed significant reduction in the proportion of accidents involving buses hitting stationary objects and vehicles, which suggests the effect of bus priority in addressing manoeuvrability issues for buses. A mixed-effects negative binomial (MENB) regression and back-propagation neural network (BPNN) modelling of bus accidents considering wider influences on accident rates at a route section level also revealed significant safety benefits when bus priority is provided. Sensitivity analyses done on the BPNN model showed general agreement in the predicted accident frequency between both models. The slightly better performance recorded by the MENB model results suggests merits in adopting a mixed effects modelling approach for accident count prediction in practice given its capability to account for unobserved location and time-specific factors. A major implication of this research is that bus priority in Melbourne's context acts to improve road safety and should be a major consideration for road management agencies when implementing bus priority and road schemes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Inverse sampling regression for pooled data.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Eskridge, Kent; Crossa, José

2017-06-01

Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing precise estimates. In this paper, we developed a mixed-effect group testing regression that is useful when the data-collecting process is performed using inverse sampling. This model allows including covariate information at the individual level to incorporate heterogeneity among individuals and identify which covariates are associated with positive individuals. We present an approach to fit this model using maximum likelihood and we performed a simulation study to evaluate the quality of the estimates. Based on the simulation study, we found that the proposed regression method for inverse sampling with group testing produces parameter estimates with low bias when the pre-specified number of positive pools (r) to stop the sampling process is at least 10 and the number of clusters in the sample is also at least 10. We performed an application with real data and we provide an NLMIXED code that researchers can use to implement this method.

[LONG-TERM SURVIVAL OF DUAL DISORDERS PATIENTS AFTER MIXED CARE IN DUAL DISORDERS AND STANDARD WARDS VERSUS CARE ONLY IN DUAL DISORDERS WARD].

PubMed

Gimelfarb, Yuri; Wolf, Aviva; Ben-Tzarfati, Mashit

2017-01-01

Dual disorders (co-occurring mental illness and substance abuse disorders in the same person) are extremely common among patients receiving mental health services. Integrated treatment has been proposed as the standard of care and it describes a flexible combination of treatments from the mental health and addiction fields that are blended together in the therapy. Scientific evidence for survival of dual disorders patients (DDPs), who had integrated dual disorders inpatient care, is lacking. To determine the long term survival rates following integrated care (Integrated Dual Diagnosis Treatment Ward [IDDTW] only) versus mixed care (IDDTW and psychiatric wards) during the life-time of DDPs. The charts of 333 subjects admitted to IDDTW during the period January 2002 - June 2006 were assessed at least 8 years after the first admission. Psychiatric diagnoses have been established and grouped according to international classification of diseases and health-related problems -10th edition (ICD-10). The Kaplan-Meier survival analysis was used to estimate the cumulative survival rates in all the subpopulations, and the predictive values of different variables were assessed by Cox proportional-hazards regression model. The total all-cause 12-year, unadjusted mortality was 21.1% in integrated care versus 24.6% in mixed care (p<.68). The Cox regression was not revealed for integrated care as a predictive factor for all-cause mortality. The findings showed that there was no consistent evidence to support integrated inpatient care over mixed care, as measured by long-term survival. More studies are required in order to address the challenges posed in the treatment of DDPs.

Laboratory Headphone Studies of Human Response to Low-Amplitude Sonic Booms and Rattle Heard Indoors

NASA Technical Reports Server (NTRS)

Loubeau, Alexandra; Sullivan, Brenda M.; Klos, Jacob; Rathsam, Jonathan; Gavin, Joseph R.

2013-01-01

Human response to sonic booms heard indoors is affected by the generation of contact-induced rattle noise. The annoyance caused by sonic boom-induced rattle noise was studied in a series of psychoacoustics tests. Stimuli were divided into three categories and presented in three different studies: isolated rattles at the same calculated Perceived Level (PL), sonic booms combined with rattles with the mixed sound at a single PL, and sonic booms combined with rattles with the mixed sound at three different PL. Subjects listened to sounds over headphones and were asked to report their annoyance. Annoyance to different rattles was shown to vary significantly according to rattle object size. In addition, the combination of low-amplitude sonic booms and rattles can be more annoying than the sonic boom alone. Correlations and regression analyses for the combined sonic boom and rattle sounds identified the Moore and Glasberg Stationary Loudness (MGSL) metric as a primary predictor of annoyance for the tested sounds. Multiple linear regression models were developed to describe annoyance to the tested sounds, and simplifications for applicability to a wider range of sounds are presented.

Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches' knowledge production function: a mixed GWR approach

NASA Astrophysics Data System (ADS)

Kang, Dongwoo; Dall'erba, Sandy

2016-04-01

Griliches' knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors' previous work (Kang and Dall'erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county's innovation capacity and suggest policy implications for regional innovation strategies.

A comparison of regression methods for model selection in individual-based landscape genetic analysis.

PubMed

Shirk, Andrew J; Landguth, Erin L; Cushman, Samuel A

2018-01-01

Anthropogenic migration barriers fragment many populations and limit the ability of species to respond to climate-induced biome shifts. Conservation actions designed to conserve habitat connectivity and mitigate barriers are needed to unite fragmented populations into larger, more viable metapopulations, and to allow species to track their climate envelope over time. Landscape genetic analysis provides an empirical means to infer landscape factors influencing gene flow and thereby inform such conservation actions. However, there are currently many methods available for model selection in landscape genetics, and considerable uncertainty as to which provide the greatest accuracy in identifying the true landscape model influencing gene flow among competing alternative hypotheses. In this study, we used population genetic simulations to evaluate the performance of seven regression-based model selection methods on a broad array of landscapes that varied by the number and type of variables contributing to resistance, the magnitude and cohesion of resistance, as well as the functional relationship between variables and resistance. We also assessed the effect of transformations designed to linearize the relationship between genetic and landscape distances. We found that linear mixed effects models had the highest accuracy in every way we evaluated model performance; however, other methods also performed well in many circumstances, particularly when landscape resistance was high and the correlation among competing hypotheses was limited. Our results provide guidance for which regression-based model selection methods provide the most accurate inferences in landscape genetic analysis and thereby best inform connectivity conservation actions. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland.

PubMed

Kolasa-Wiecek, Alicja

2015-04-01

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO2 emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH4 emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R2=0.90. For N2O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N2O emission is the peat and wood fuel consumption. Copyright © 2015. Published by Elsevier B.V.

Discovering human germ cell mutagens with whole genome sequencing: Insights from power calculations reveal the importance of controlling for between-family variability.

PubMed

Webster, R J; Williams, A; Marchetti, F; Yauk, C L

2018-07-01

Mutations in germ cells pose potential genetic risks to offspring. However, de novo mutations are rare events that are spread across the genome and are difficult to detect. Thus, studies in this area have generally been under-powered, and no human germ cell mutagen has been identified. Whole Genome Sequencing (WGS) of human pedigrees has been proposed as an approach to overcome these technical and statistical challenges. WGS enables analysis of a much wider breadth of the genome than traditional approaches. Here, we performed power analyses to determine the feasibility of using WGS in human families to identify germ cell mutagens. Different statistical models were compared in the power analyses (ANOVA and multiple regression for one-child families, and mixed effect model sampling between two to four siblings per family). Assumptions were made based on parameters from the existing literature, such as the mutation-by-paternal age effect. We explored two scenarios: a constant effect due to an exposure that occurred in the past, and an accumulating effect where the exposure is continuing. Our analysis revealed the importance of modeling inter-family variability of the mutation-by-paternal age effect. Statistical power was improved by models accounting for the family-to-family variability. Our power analyses suggest that sufficient statistical power can be attained with 4-28 four-sibling families per treatment group, when the increase in mutations ranges from 40 to 10% respectively. Modeling family variability using mixed effect models provided a reduction in sample size compared to a multiple regression approach. Much larger sample sizes were required to detect an interaction effect between environmental exposures and paternal age. These findings inform study design and statistical modeling approaches to improve power and reduce sequencing costs for future studies in this area. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

Contributions of Kansas rangeland burning to ambient O3: Analysis of data from 2001 to 2016.

PubMed

Liu, Zifei; Liu, Yang; Murphy, James P; Maghirang, Ronaldo

2018-03-15

Prescribed range/pasture burning is a common practice in Kansas to enhance the nutritional value of native grasses and control invading weeds, trees, and brush. A major concern associated with the burning is the contribution of smoke to elevated ground level ambient ozone (O 3 ). The objective of this study is to estimate contributions of Kansas rangeland burning to ambient O 3 mixing ratios through regression analysis (1) between observed O 3 data and available satellite burn activity data from 2001 to 2016; and (2) between observed O 3 data and the smoke contributions to PM 2.5 which were resolved from receptor modeling. Positive correlations were observed between ambient O 3 levels and the acres burned each year estimated from satellite imagery. When burned acres in April were larger than or equal to 1.9 million, O 3 >70ppb occurred at least at one of the ten monitoring sites in Kansas. Statistical regression models of daily maximum 8-hour O 3 mixing ratios were developed at each of the ten monitoring sites using meteorological predictors. The O 3 model residuals that were not explained by the meteorological effect models were affected by PM 2.5 contributors including sulfate/industrial sources and emissions that generated secondary organic particles, such as rangeland burning, which were derived from receptor modeling. The average O 3 model residual on the high O 3 days in April was 21±9ppb, which was likely associated with smoke emissions from burning. Research will continue to obtain daily satellite burn activity data and to correlate burn data with daily O 3 data, so that modeling of O 3 levels can be improved under influences of daily burn activities. Less frequency of high O 3 days was observed in April since 2011, which may be partly due to implementation of the Flint Hills Smoke Management Plan which promoted better timing of burns. Copyright © 2017 Elsevier B.V. All rights reserved.

KMgene: a unified R package for gene-based association analysis for complex traits.

PubMed

Yan, Qi; Fang, Zhou; Chen, Wei; Stegle, Oliver

2018-02-09

In this report, we introduce an R package KMgene for performing gene-based association tests for familial, multivariate or longitudinal traits using kernel machine (KM) regression under a generalized linear mixed model (GLMM) framework. Extensive simulations were performed to evaluate the validity of the approaches implemented in KMgene. http://cran.r-project.org/web/packages/KMgene. qi.yan@chp.edu or wei.chen@chp.edu. Supplementary data are available at Bioinformatics online. © The Author(s) 2018. Published by Oxford University Press.

Regression analysis of mixed recurrent-event and panel-count data

PubMed Central

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L.

2014-01-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20, 1–42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. PMID:24648408

Analyzing source apportioned methane in northern California during Discover-AQ-CA using airborne measurements and model simulations

NASA Astrophysics Data System (ADS)

Johnson, Matthew S.; Yates, Emma L.; Iraci, Laura T.; Loewenstein, Max; Tadić, Jovan M.; Wecht, Kevin J.; Jeong, Seongeun; Fischer, Marc L.

2014-12-01

This study analyzes source apportioned methane (CH4) emissions and atmospheric mixing ratios in northern California during the Discover-AQ-CA field campaign using airborne measurement data and model simulations. Source apportioned CH4 emissions from the Emissions Database for Global Atmospheric Research (EDGAR) version 4.2 were applied in the 3-D chemical transport model GEOS-Chem and analyzed using airborne measurements taken as part of the Alpha Jet Atmospheric eXperiment over the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV). During the time period of the Discover-AQ-CA field campaign EDGAR inventory CH4 emissions were ∼5.30 Gg day-1 (Gg = 1.0 × 109 g) (equating to ∼1.90 × 103 Gg yr-1) for all of California. According to EDGAR, the SFBA and northern SJV region contributes ∼30% of total CH4 emissions from California. Source apportionment analysis during this study shows that CH4 mixing ratios over this area of northern California are largely influenced by global emissions from wetlands and local/global emissions from gas and oil production and distribution, waste treatment processes, and livestock management. Model simulations, using EDGAR emissions, suggest that the model under-estimates CH4 mixing ratios in northern California (average normalized mean bias (NMB) = -5.2% and linear regression slope = 0.20). The largest negative biases in the model were calculated on days when large amounts of CH4 were measured over local emission sources and atmospheric CH4 mixing ratios reached values >2.5 parts per million. Sensitivity emission studies conducted during this research suggest that local emissions of CH4 from livestock management processes are likely the primary source of the negative model bias. These results indicate that a variety, and larger quantity, of measurement data needs to be obtained and additional research is necessary to better quantify source apportioned CH4 emissions in California.

Analyzing source apportioned methane in northern California during Discover-AQ-CA using airborne measurements and model simulations

DOE PAGES

Johnson, Matthew S.; Yates, Emma L.; Iraci, Laura T.; ...

2014-12-01

This study analyzes source apportioned methane (CH 4) emissions and atmospheric mixing ratios in northern California during the Discover-AQ-CA field campaign using airborne measurement data and model simulations. Source apportioned CH 4 emissions from the Emissions Database for Global Atmospheric Research (EDGAR) version 4.2 were applied in the 3-D chemical transport model GEOS-Chem and analyzed using airborne measurements taken as part of the Alpha Jet Atmospheric eXperiment over the San Francisco Bay Area (SFBA) and northern San Joaquin Valley (SJV). During the time period of the Discover-AQ-CA field campaign EDGAR inventory CH 4 emissions were ~5.30 Gg day –1 (Ggmore » = 1.0 × 10 9 g) (equating to ~1.90 × 10 3 Gg yr –1) for all of California. According to EDGAR, the SFBA and northern SJV region contributes ~30% of total CH 4 emissions from California. Source apportionment analysis during this study shows that CH 4 mixing ratios over this area of northern California are largely influenced by global emissions from wetlands and local/global emissions from gas and oil production and distribution, waste treatment processes, and livestock management. Model simulations, using EDGAR emissions, suggest that the model under-estimates CH 4 mixing ratios in northern California (average normalized mean bias (NMB) = –5.2% and linear regression slope = 0.20). The largest negative biases in the model were calculated on days when large amounts of CH 4 were measured over local emission sources and atmospheric CH 4 mixing ratios reached values >2.5 parts per million. Sensitivity emission studies conducted during this research suggest that local emissions of CH 4 from livestock management processes are likely the primary source of the negative model bias. These results indicate that a variety, and larger quantity, of measurement data needs to be obtained and additional research is necessary to better quantify source apportioned CH 4 emissions in California.« less

Using empirical Bayes predictors from generalized linear mixed models to test and visualize associations among longitudinal outcomes.

PubMed

Mikulich-Gilbertson, Susan K; Wagner, Brandie D; Grunwald, Gary K; Riggs, Paula D; Zerbe, Gary O

2018-01-01

Medical research is often designed to investigate changes in a collection of response variables that are measured repeatedly on the same subjects. The multivariate generalized linear mixed model (MGLMM) can be used to evaluate random coefficient associations (e.g. simple correlations, partial regression coefficients) among outcomes that may be non-normal and differently distributed by specifying a multivariate normal distribution for their random effects and then evaluating the latent relationship between them. Empirical Bayes predictors are readily available for each subject from any mixed model and are observable and hence, plotable. Here, we evaluate whether second-stage association analyses of empirical Bayes predictors from a MGLMM, provide a good approximation and visual representation of these latent association analyses using medical examples and simulations. Additionally, we compare these results with association analyses of empirical Bayes predictors generated from separate mixed models for each outcome, a procedure that could circumvent computational problems that arise when the dimension of the joint covariance matrix of random effects is large and prohibits estimation of latent associations. As has been shown in other analytic contexts, the p-values for all second-stage coefficients that were determined by naively assuming normality of empirical Bayes predictors provide a good approximation to p-values determined via permutation analysis. Analyzing outcomes that are interrelated with separate models in the first stage and then associating the resulting empirical Bayes predictors in a second stage results in different mean and covariance parameter estimates from the maximum likelihood estimates generated by a MGLMM. The potential for erroneous inference from using results from these separate models increases as the magnitude of the association among the outcomes increases. Thus if computable, scatterplots of the conditionally independent empirical Bayes predictors from a MGLMM are always preferable to scatterplots of empirical Bayes predictors generated by separate models, unless the true association between outcomes is zero.

Developing a risk stratification tool for audit of outcome after surgery for head and neck squamous cell carcinoma.

PubMed

Tighe, David F; Thomas, Alan J; Sassoon, Isabel; Kinsman, Robin; McGurk, Mark

2017-07-01

Patients treated surgically for head and neck squamous cell carcinoma (HNSCC) represent a heterogeneous group. Adjusting for patient case mix and complexity of surgery is essential if reporting outcomes represent surgical performance and quality of care. A case note audit totaling 1075 patients receiving 1218 operations done for HNSCC in 4 cancer networks was completed. Logistic regression, decision tree analysis, an artificial neural network, and Naïve Bayes Classifier were used to adjust for patient case-mix using pertinent preoperative variables. Thirty-day complication rates varied widely (34%-51%; P < .015) between units. The predictive models allowed risk stratification. The artificial neural network demonstrated the best predictive performance (area under the curve [AUC] 0.85). Early postoperative complications are a measurable outcome that can be used to benchmark surgical performance and quality of care. Surgical outcome reporting in national clinical audits should be taking account of the patient case mix. © 2017 Wiley Periodicals, Inc.

Evaluation of the effect of yellow konjac flour-κ-carrageenan mixed gels and red koji rice extracts on the properties of restructured meat using response surface methodology.

PubMed

Widjanarko, Simon Bambang; Amalia, Qory; Hermanto, Mochamad Bagus; Mubarok, Ahmad Zaki

2018-05-01

In the present study, the effect of two independent variables, yellow konjac flour-κ-carrageenan (KFC) mixed gels and red koji rice (RKR) extracts for the development of restructured meat product, was investigated using central composite design of response surface methodology (RSM). The assessed physical characteristics were hardness, water holding capacity (WHC), and color (° hue ) of the restructured meat products. The second order regression models with high R 2 value were significantly fitted to predict the changes in hardness, WHC and color. The results showed that the predicted optimum formula of restructured meat were the addition of KFC mixed gels at 10.21% and RKR extracts at 6.11%. The experiments results validate these optimum formula and found to be not statistically different at 5% level. Thus, the RSM was successfully employed and can be used to optimize the formulation of restructured meat.

U.S. Citizen Children of Undocumented Parents: The Link Between State Immigration Policy and the Health of Latino Children.

PubMed

Vargas, Edward D; Ybarra, Vickie D

2017-08-01

We examine Latino citizen children in mixed-status families and how their physical health status compares to their U.S. citizen, co-ethnic counterparts. We also examine Latino parents' perceptions of state immigration policy and its implications for child health status. Using the 2015 Latino National Health and Immigration Survey (n = 1493), we estimate a series of multivariate ordered logistic regression models with mixed-status family and perceptions of state immigration policy as primary predictors. We find that mixed-status families report worse physical health for their children as compared to their U.S. citizen co-ethnics. We also find that parental perceptions of their states' immigration status further exacerbate health disparities between families. These findings have implications for scholars and policy makers interested in immigrant health, family wellbeing, and health disparities in complex family structures. They contribute to the scholarship on Latino child health and on the erosion of the Latino immigrant health advantage.

U.S. Citizen Children of Undocumented Parents: The Link Between State Immigration Policy and the Health of Latino Children

PubMed Central

Vargas, Edward D.; Ybarra, Vickie D.

2016-01-01

Background We examine Latino citizen children in mixed-status families and how their physical health status compares to their U.S. citizen, co-ethnic counterparts. We also examine Latino parents’ perceptions of state immigration policy and its implications for child health status. Methods Using the 2015 Latino National Health and Immigration Survey (n=1493), we estimate a series of multivariate ordered logistic regression models with mixed-status family and perceptions of state immigration policy as primary predictors. Results We find that mixed-status families report worse physical health for their children as compared to their U.S. citizen co-ethnics. We also find that parental perceptions of their states’ immigration status further exacerbate health disparities between families. Discussion These findings have implications for scholars and policy makers interested in immigrant health, family wellbeing, and health disparities in complex family structures. They contribute to the scholarship on Latino child health and on the erosion of the Latino immigrant health advantage. PMID:27435476

Self-declared stock ownership and association with positive trial outcome in randomized controlled trials with binary outcomes published in general medical journals: a cross-sectional study.

PubMed

Falk Delgado, Alberto; Falk Delgado, Anna

2017-07-26

Describe the prevalence and types of conflicts of interest (COI) in published randomized controlled trials (RCTs) in general medical journals with a binary primary outcome and assess the association between conflicts of interest and favorable outcome. Parallel-group RCTs with a binary primary outcome published in three general medical journals during 2013-2015 were identified. COI type, funding source, and outcome were extracted. Binomial logistic regression model was performed to assess association between COI and funding source with outcome. A total of 509 consecutive parallel-group RCTs were included in the study. COI was reported in 74% in mixed funded RCTs and in 99% in for-profit funded RCTs. Stock ownership was reported in none of the non-profit RCTs, in 7% of mixed funded RCTs, and in 50% of for-profit funded RCTs. Mixed-funded RCTs had employees from the funding company in 11% and for-profit RCTs in 76%. Multivariable logistic regression revealed that stock ownership in the funding company among any of the authors was associated with a favorable outcome (odds ratio = 3.53; 95% confidence interval = 1.59-7.86; p < 0.01). COI in for-profit funded RCTs is extensive, because the factors related to COI are not fully independent, a multivariable analysis should be cautiously interpreted. However, after multivariable adjustment only stock ownership from the funding company among authors is associated with a favorable outcome.

The association of health-related fitness with indicators of academic performance in Texas schools.

PubMed

Welk, Gregory J; Jackson, Allen W; Morrow, James R; Haskell, William H; Meredith, Marilu D; Cooper, Kenneth H

2010-09-01

This study examined the associations between indicators of health-related physical fitness (cardiovascular fitness and body mass index) and academic performance (Texas Assessment of Knowledge and Skills). Partial correlations were generally stronger for cardiovascular fitness than body mass index and consistently stronger in the middle school grades. Mixed-model regression analyses revealed modest associations between fitness and academic achievement after controlling for potentially confounding variables. The effects of fitness on academic achievement were positive but small. A separate logistic regression analysis indicated that higher fitness rates increased the odds of schools achieving exemplary/recognized school status within the state. School fitness attainment is an indicator of higher performing schools. Direction of causality cannot be inferred due to the cross-sectional nature of the data.

Seasonal and Regional Variability in North Pacific Upper-Ocean Turbulence

NASA Astrophysics Data System (ADS)

Najjar, R.; Creedon, R.; Cronin, M. F.

2016-02-01

Turbulent diffusion at marine mixed layer base (MLB) plays a fundamental role in the transport of energy between the upper and abyssal ocean. Recent investigations of North Pacific mooring data at Ocean Climate Stations (OCS) Papa (50.1N,144.9W) and KEO (32.3N,144.6E) suggest seasonal and regional variability in thermal diffusivity (κT). In this investigation, it is hypothesized that these observed differences in κT are directly associated with synoptic variability in net surface heat flux (Q0), surface wind stress (τ), mixed layer depth (h), and density stratification at MLB (∂zσ|-h). To test this hypothesis, daily-averaged time series of κT are regressed against those of Q0, τ, h, and ∂zσ|-h at both Papa and KEO over a six year time period (2007-2013). Seasonality of each time series is removed before regression to capture synoptic variability of each variable. Preliminary results of the regression analysis suggest statistically significant correlations between κT and all forcing parameters at both mooring sites. These correlations have well-determined orders of magnitude and signs consistent with the hypothesis. As a result, differences in κT between Papa and KEO may be recast in terms of differences in their correlation coefficients. In order to continue investigation of these parameters and their effects on mean seasonal differences between the two regions, these results will be compared with turbulence predicted by the K-Profile Parameterization ocean turbulence model.

Relationship Between Column-Density and Surface Mixing Ratio: Statistical Analysis of O3 and NO2 Data from the July 2011 Maryland DISCOVER-AQ Mission

NASA Technical Reports Server (NTRS)

Flynn, Clare; Pickering, Kenneth E.; Crawford, James H.; Lamsol, Lok; Krotkov, Nickolay; Herman, Jay; Weinheimer, Andrew; Chen, Gao; Liu, Xiong; Szykman, James;

A longitudinal analysis of the influence of the neighborhood built environment on walking for transportation: the RESIDE study.

PubMed

Knuiman, Matthew W; Christian, Hayley E; Divitini, Mark L; Foster, Sarah A; Bull, Fiona C; Badland, Hannah M; Giles-Corti, Billie

2014-09-01

The purpose of the present analysis was to use longitudinal data collected over 7 years (from 4 surveys) in the Residential Environments (RESIDE) Study (Perth, Australia, 2003-2012) to more carefully examine the relationship of neighborhood walkability and destination accessibility with walking for transportation that has been seen in many cross-sectional studies. We compared effect estimates from 3 types of logistic regression models: 2 that utilize all available data (a population marginal model and a subject-level mixed model) and a third subject-level conditional model that exclusively uses within-person longitudinal evidence. The results support the evidence that neighborhood walkability (especially land-use mix and street connectivity), local access to public transit stops, and variety in the types of local destinations are important determinants of walking for transportation. The similarity of subject-level effect estimates from logistic mixed models and those from conditional logistic models indicates that there is little or no bias from uncontrolled time-constant residential preference (self-selection) factors; however, confounding by uncontrolled time-varying factors, such as health status, remains a possibility. These findings provide policy makers and urban planners with further evidence that certain features of the built environment may be important in the design of neighborhoods to increase walking for transportation and meet the health needs of residents. © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Adjusted adaptive Lasso for covariate model-building in nonlinear mixed-effect pharmacokinetic models.

PubMed

Haem, Elham; Harling, Kajsa; Ayatollahi, Seyyed Mohammad Taghi; Zare, Najaf; Karlsson, Mats O

2017-02-01

One important aim in population pharmacokinetics (PK) and pharmacodynamics is identification and quantification of the relationships between the parameters and covariates. Lasso has been suggested as a technique for simultaneous estimation and covariate selection. In linear regression, it has been shown that Lasso possesses no oracle properties, which means it asymptotically performs as though the true underlying model was given in advance. Adaptive Lasso (ALasso) with appropriate initial weights is claimed to possess oracle properties; however, it can lead to poor predictive performance when there is multicollinearity between covariates. This simulation study implemented a new version of ALasso, called adjusted ALasso (AALasso), to take into account the ratio of the standard error of the maximum likelihood (ML) estimator to the ML coefficient as the initial weight in ALasso to deal with multicollinearity in non-linear mixed-effect models. The performance of AALasso was compared with that of ALasso and Lasso. PK data was simulated in four set-ups from a one-compartment bolus input model. Covariates were created by sampling from a multivariate standard normal distribution with no, low (0.2), moderate (0.5) or high (0.7) correlation. The true covariates influenced only clearance at different magnitudes. AALasso, ALasso and Lasso were compared in terms of mean absolute prediction error and error of the estimated covariate coefficient. The results show that AALasso performed better in small data sets, even in those in which a high correlation existed between covariates. This makes AALasso a promising method for covariate selection in nonlinear mixed-effect models.

Semi-idealized modeling of lightning initiation related to vertical air motion and cloud microphysics

NASA Astrophysics Data System (ADS)

Wang, Fei; Zhang, Yijun; Zheng, Dong; Xu, Liangtao; Zhang, Wenjuan; Meng, Qing

2017-10-01

A three-dimensional charge-discharge numerical model is used, in a semi-idealized mode, to simulate a thunder-storm cell. Characteristics of the graupel microphysics and vertical air motion associated with the lightning initiation are revealed, which could be useful in retrieving charge strength during lightning when no charge-discharge model is available. The results show that the vertical air motion at the lightning initiation sites ( W ini) has a cubic polynomial correlation with the maximum updraft of the storm cell ( W cell-max), with the adjusted regression coefficient R 2 of approximately 0.97. Meanwhile, the graupel mixing ratio at the lightning initiation sites ( q g-ini) has a linear correlation with the maximum graupel mixing ratio of the storm cell ( q g-cell-max) and the initiation height ( z ini), with the coefficients being 0.86 and 0.85, respectively. These linear correlations are more significant during the middle and late stages of lightning activity. A zero-charge zone, namely, the area with very low net charge density between the main positive and negative charge layers, appears above the area of q g-cell-max and below the upper edge of the graupel region, and is found to be an important area for lightning initiation. Inside the zero-charge zone, large electric intensity forms, and the ratio of q ice (ice crystal mixing ratio) to q g (graupel mixing ratio) illustrates an exponential relationship to q g-ini. These relationships provide valuable clues to more accurately locating the high-risk area of lightning initiation in thunderstorms when only dual-polarization radar data or outputs from numerical models without charging/discharging schemes are available. The results can also help understand the environmental conditions at lightning initiation sites.

A Bayesian methodological framework for accommodating interannual variability of nutrient loading with the SPARROW model

NASA Astrophysics Data System (ADS)

Wellen, Christopher; Arhonditsis, George B.; Labencki, Tanya; Boyd, Duncan

2012-10-01

Regression-type, hybrid empirical/process-based models (e.g., SPARROW, PolFlow) have assumed a prominent role in efforts to estimate the sources and transport of nutrient pollution at river basin scales. However, almost no attempts have been made to explicitly accommodate interannual nutrient loading variability in their structure, despite empirical and theoretical evidence indicating that the associated source/sink processes are quite variable at annual timescales. In this study, we present two methodological approaches to accommodate interannual variability with the Spatially Referenced Regressions on Watershed attributes (SPARROW) nonlinear regression model. The first strategy uses the SPARROW model to estimate a static baseline load and climatic variables (e.g., precipitation) to drive the interannual variability. The second approach allows the source/sink processes within the SPARROW model to vary at annual timescales using dynamic parameter estimation techniques akin to those used in dynamic linear models. Model parameterization is founded upon Bayesian inference techniques that explicitly consider calibration data and model uncertainty. Our case study is the Hamilton Harbor watershed, a mixed agricultural and urban residential area located at the western end of Lake Ontario, Canada. Our analysis suggests that dynamic parameter estimation is the more parsimonious of the two strategies tested and can offer insights into the temporal structural changes associated with watershed functioning. Consistent with empirical and theoretical work, model estimated annual in-stream attenuation rates varied inversely with annual discharge. Estimated phosphorus source areas were concentrated near the receiving water body during years of high in-stream attenuation and dispersed along the main stems of the streams during years of low attenuation, suggesting that nutrient source areas are subject to interannual variability.

The long-solved problem of the best-fit straight line: Application to isotopic mixing lines

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

Wehr, Richard; Saleska, Scott R.

It has been almost 50 years since York published an exact and general solution for the best-fit straight line to independent points with normally distributed errors in both x and y. York's solution is highly cited in the geophysical literature but almost unknown outside of it, so that there has been no ebb in the tide of books and papers wrestling with the problem. Much of the post-1969 literature on straight-line fitting has sown confusion not merely by its content but by its very existence. The optimal least-squares fit is already known; the problem is already solved. Here we introducemore » the non-specialist reader to York's solution and demonstrate its application in the interesting case of the isotopic mixing line, an analytical tool widely used to determine the isotopic signature of trace gas sources for the study of biogeochemical cycles. The most commonly known linear regression methods – ordinary least-squares regression (OLS), geometric mean regression (GMR), and orthogonal distance regression (ODR) – have each been recommended as the best method for fitting isotopic mixing lines. In fact, OLS, GMR, and ODR are all special cases of York's solution that are valid only under particular measurement conditions, and those conditions do not hold in general for isotopic mixing lines. Here, using Monte Carlo simulations, we quantify the biases in OLS, GMR, and ODR under various conditions and show that York's general – and convenient – solution is always the least biased.« less

The long-solved problem of the best-fit straight line: Application to isotopic mixing lines

DOE PAGES

Wehr, Richard; Saleska, Scott R.

2017-01-03

It has been almost 50 years since York published an exact and general solution for the best-fit straight line to independent points with normally distributed errors in both x and y. York's solution is highly cited in the geophysical literature but almost unknown outside of it, so that there has been no ebb in the tide of books and papers wrestling with the problem. Much of the post-1969 literature on straight-line fitting has sown confusion not merely by its content but by its very existence. The optimal least-squares fit is already known; the problem is already solved. Here we introducemore » the non-specialist reader to York's solution and demonstrate its application in the interesting case of the isotopic mixing line, an analytical tool widely used to determine the isotopic signature of trace gas sources for the study of biogeochemical cycles. The most commonly known linear regression methods – ordinary least-squares regression (OLS), geometric mean regression (GMR), and orthogonal distance regression (ODR) – have each been recommended as the best method for fitting isotopic mixing lines. In fact, OLS, GMR, and ODR are all special cases of York's solution that are valid only under particular measurement conditions, and those conditions do not hold in general for isotopic mixing lines. Here, using Monte Carlo simulations, we quantify the biases in OLS, GMR, and ODR under various conditions and show that York's general – and convenient – solution is always the least biased.« less

Calculated quantum yield of photosynthesis of phytoplankton in the Marine Light-Mixed Layers (59 deg N, 21 deg W)

NASA Technical Reports Server (NTRS)

Carder, K. L.; Lee, Z. P.; Marra, John; Steward, R. G.; Perry, M. J.

1995-01-01

The quantum yield of photosynthesis (mol C/mol photons) was calculated at six depths for the waters of the Marine Light-Mixed Layer (MLML) cruise of May 1991. As there were photosynthetically available radiation (PAR) but no spectral irradiance measurements for the primary production incubations, three ways are presented here for the calculation of the absorbed photons (AP) by phytoplankton for the purpose of calculating phi. The first is based on a simple, nonspectral model; the second is based on a nonlinear regression using measured PAR values with depth; and the third is derived through remote sensing measurements. We show that the results of phi calculated using the nonlinear regreesion method and those using remote sensing are in good agreement with each other, and are consistent with the reported values of other studies. In deep waters, however, the simple nonspectral model may cause quantum yield values much higher than theoretically possible.

Nurse staffing patterns and hospital efficiency in the United States.

PubMed

Bloom, J R; Alexander, J A; Nuchols, B A

1997-01-01

The objective of this exploratory study was to assess the effects of four nurse staffing patterns on the efficiency of patient care delivery in the hospital: registered nurses (RNs) from temporary agencies; part-time career RNs; RN rich skill mix; and organizationally experienced RNs. Using Transaction Cost Analysis, four regression models were specified to consider the effect of these staffing plans on personnel and benefit costs and on non-personnel operating costs. A number of additional variables were also included in the models to control for the effect of other organization and environmental determinants of hospital costs. Use of career part-time RNs and experienced staff reduced both personnel and benefit costs, as well as total non-personnel operating costs, while the use of temporary agencies for RNs increased non-personnel operating costs. An RN rich skill mix was not related to either measure of hospital costs. These findings provide partial support of the theory. Implications of our findings for future research on hospital management are discussed.

Quantification of polyhydroxyalkanoates in mixed and pure cultures biomass by Fourier transform infrared spectroscopy: comparison of different approaches.

PubMed

Isak, I; Patel, M; Riddell, M; West, M; Bowers, T; Wijeyekoon, S; Lloyd, J

2016-08-01

Fourier transform infrared (FTIR) spectroscopy was used in this study for the rapid quantification of polyhydroxyalkanoates (PHA) in mixed and pure culture bacterial biomass. Three different statistical analysis methods (regression, partial least squares (PLS) and nonlinear) were applied to the FTIR data and the results were plotted against the PHA values measured with the reference gas chromatography technique. All methods predicted PHA content in mixed culture biomass with comparable efficiency, indicated by similar residuals values. The PHA in these cultures ranged from low to medium concentration (0-44 wt% of dried biomass content). However, for the analysis of the combined mixed and pure culture biomass with PHA concentration ranging from low to high (0-93% of dried biomass content), the PLS method was most efficient. This paper reports, for the first time, the use of a single calibration model constructed with a combination of mixed and pure cultures covering a wide PHA range, for predicting PHA content in biomass. Currently no one universal method exists for processing FTIR data for polyhydroxyalkanoates (PHA) quantification. This study compares three different methods of analysing FTIR data for quantification of PHAs in biomass. A new data-processing approach was proposed and the results were compared against existing literature methods. Most publications report PHA quantification of medium range in pure culture. However, in our study we encompassed both mixed and pure culture biomass containing a broader range of PHA in the calibration curve. The resulting prediction model is useful for rapid quantification of a wider range of PHA content in biomass. © 2016 The Society for Applied Microbiology.

Multilevel modeling and panel data analysis in educational research (Case study: National examination data senior high school in West Java)

NASA Astrophysics Data System (ADS)

Zulvia, Pepi; Kurnia, Anang; Soleh, Agus M.

2017-03-01

Individual and environment are a hierarchical structure consist of units grouped at different levels. Hierarchical data structures are analyzed based on several levels, with the lowest level nested in the highest level. This modeling is commonly call multilevel modeling. Multilevel modeling is widely used in education research, for example, the average score of National Examination (UN). While in Indonesia UN for high school student is divided into natural science and social science. The purpose of this research is to develop multilevel and panel data modeling using linear mixed model on educational data. The first step is data exploration and identification relationships between independent and dependent variable by checking correlation coefficient and variance inflation factor (VIF). Furthermore, we use a simple model approach with highest level of the hierarchy (level-2) is regency/city while school is the lowest of hierarchy (level-1). The best model was determined by comparing goodness-of-fit and checking assumption from residual plots and predictions for each model. Our finding that for natural science and social science, the regression with random effects of regency/city and fixed effects of the time i.e multilevel model has better performance than the linear mixed model in explaining the variability of the dependent variable, which is the average scores of UN.

Further comments on sensitivities, parameter estimation, and sampling design in one-dimensional analysis of solute transport in porous media

USGS Publications Warehouse

Knopman, Debra S.; Voss, Clifford I.

1988-01-01

Sensitivities of solute concentration to parameters associated with first-order chemical decay, boundary conditions, initial conditions, and multilayer transport are examined in one-dimensional analytical models of transient solute transport in porous media. A sensitivity is a change in solute concentration resulting from a change in a model parameter. Sensitivity analysis is important because minimum information required in regression on chemical data for the estimation of model parameters by regression is expressed in terms of sensitivities. Nonlinear regression models of solute transport were tested on sets of noiseless observations from known models that exceeded the minimum sensitivity information requirements. Results demonstrate that the regression models consistently converged to the correct parameters when the initial sets of parameter values substantially deviated from the correct parameters. On the basis of the sensitivity analysis, several statements may be made about design of sampling for parameter estimation for the models examined: (1) estimation of parameters associated with solute transport in the individual layers of a multilayer system is possible even when solute concentrations in the individual layers are mixed in an observation well; (2) when estimating parameters in a decaying upstream boundary condition, observations are best made late in the passage of the front near a time chosen by adding the inverse of an hypothesized value of the source decay parameter to the estimated mean travel time at a given downstream location; (3) estimation of a first-order chemical decay parameter requires observations to be made late in the passage of the front, preferably near a location corresponding to a travel time of √2 times the half-life of the solute; and (4) estimation of a parameter relating to spatial variability in an initial condition requires observations to be made early in time relative to passage of the solute front.

An empirical model for dissolution profile and its application to floating dosage forms.

PubMed

Weiss, Michael; Kriangkrai, Worawut; Sungthongjeen, Srisagul

2014-06-02

A sum of two inverse Gaussian functions is proposed as a highly flexible empirical model for fitting of in vitro dissolution profiles. The model was applied to quantitatively describe theophylline release from effervescent multi-layer coated floating tablets containing different amounts of the anti-tacking agents talc or glyceryl monostearate. Model parameters were estimated by nonlinear regression (mixed-effects modeling). The estimated parameters were used to determine the mean dissolution time, as well as to reconstruct the time course of release rate for each formulation, whereby the fractional release rate can serve as a diagnostic tool for classification of dissolution processes. The approach allows quantification of dissolution behavior and could provide additional insights into the underlying processes. Copyright © 2014 Elsevier B.V. All rights reserved.

Crop area estimation based on remotely-sensed data with an accurate but costly subsample

NASA Technical Reports Server (NTRS)

Gunst, R. F.

1985-01-01

Research activities conducted under the auspices of National Aeronautics and Space Administration Cooperative Agreement NCC 9-9 are discussed. During this contract period research efforts are concentrated in two primary areas. The first are is an investigation of the use of measurement error models as alternatives to least squares regression estimators of crop production or timber biomass. The secondary primary area of investigation is on the estimation of the mixing proportion of two-component mixture models. This report lists publications, technical reports, submitted manuscripts, and oral presentation generated by these research efforts. Possible areas of future research are mentioned.

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.

Binomial outcomes in dataset with some clusters of size two: can the dependence of twins be accounted for? A simulation study comparing the reliability of statistical methods based on a dataset of preterm infants.

PubMed

Sauzet, Odile; Peacock, Janet L

2017-07-20

The analysis of perinatal outcomes often involves datasets with some multiple births. These are datasets mostly formed of independent observations and a limited number of clusters of size two (twins) and maybe of size three or more. This non-independence needs to be accounted for in the statistical analysis. Using simulated data based on a dataset of preterm infants we have previously investigated the performance of several approaches to the analysis of continuous outcomes in the presence of some clusters of size two. Mixed models have been developed for binomial outcomes but very little is known about their reliability when only a limited number of small clusters are present. Using simulated data based on a dataset of preterm infants we investigated the performance of several approaches to the analysis of binomial outcomes in the presence of some clusters of size two. Logistic models, several methods of estimation for the logistic random intercept models and generalised estimating equations were compared. The presence of even a small percentage of twins means that a logistic regression model will underestimate all parameters but a logistic random intercept model fails to estimate the correlation between siblings if the percentage of twins is too small and will provide similar estimates to logistic regression. The method which seems to provide the best balance between estimation of the standard error and the parameter for any percentage of twins is the generalised estimating equations. This study has shown that the number of covariates or the level two variance do not necessarily affect the performance of the various methods used to analyse datasets containing twins but when the percentage of small clusters is too small, mixed models cannot capture the dependence between siblings.

Genomic similarity and kernel methods I: advancements by building on mathematical and statistical foundations.

PubMed

Schaid, Daniel J

2010-01-01

Measures of genomic similarity are the basis of many statistical analytic methods. We review the mathematical and statistical basis of similarity methods, particularly based on kernel methods. A kernel function converts information for a pair of subjects to a quantitative value representing either similarity (larger values meaning more similar) or distance (smaller values meaning more similar), with the requirement that it must create a positive semidefinite matrix when applied to all pairs of subjects. This review emphasizes the wide range of statistical methods and software that can be used when similarity is based on kernel methods, such as nonparametric regression, linear mixed models and generalized linear mixed models, hierarchical models, score statistics, and support vector machines. The mathematical rigor for these methods is summarized, as is the mathematical framework for making kernels. This review provides a framework to move from intuitive and heuristic approaches to define genomic similarities to more rigorous methods that can take advantage of powerful statistical modeling and existing software. A companion paper reviews novel approaches to creating kernels that might be useful for genomic analyses, providing insights with examples [1]. Copyright © 2010 S. Karger AG, Basel.

Utility Estimates of Disease-Specific Health States in Prostate Cancer from Three Different Perspectives.

PubMed

Gries, Katharine S; Regier, Dean A; Ramsey, Scott D; Patrick, Donald L

2017-06-01

To develop a statistical model generating utility estimates for prostate cancer specific health states, using preference weights derived from the perspectives of prostate cancer patients, men at risk for prostate cancer, and society. Utility estimate values were calculated using standard gamble (SG) methodology. Study participants valued 18 prostate-specific health states with the five attributes: sexual function, urinary function, bowel function, pain, and emotional well-being. Appropriateness of model (linear regression, mixed effects, or generalized estimating equation) to generate prostate cancer utility estimates was determined by paired t-tests to compare observed and predicted values. Mixed-corrected standard SG utility estimates to account for loss aversion were calculated based on prospect theory. 132 study participants assigned values to the health states (n = 40 men at risk for prostate cancer; n = 43 men with prostate cancer; n = 49 general population). In total, 792 valuations were elicited (six health states for each 132 participants). The most appropriate model for the classification system was a mixed effects model; correlations between the mean observed and predicted utility estimates were greater than 0.80 for each perspective. Developing a health-state classification system with preference weights for three different perspectives demonstrates the relative importance of main effects between populations. The predicted values for men with prostate cancer support the hypothesis that patients experiencing the disease state assign higher utility estimates to health states and there is a difference in valuations made by patients and the general population.

Child support and mixed-status families an analysis using the Fragile Families and Child Wellbeing Study.

PubMed

Xu, Lanlan; Pirog, Maureen A; Vargas, Edward D

2016-11-01

A large body of literature documents the importance of child support for children's wellbeing, though little is known about the child support behaviors of mixed-status families, a large and rapidly growing population in the United States. In this paper, we use data from the Fragile Families and Child Wellbeing Study to investigate the impact of citizenship status on formal and informal child support transfers among a nationally representative sample of parents who have citizen children. Probit regression models and propensity score matching (PSM) estimators show that mixed-status families are significantly less likely to have child support orders and child support receipt compared to their citizen counterparts. We found that mothers' knowledge of the child support system increases the probability of establishing paternity. However, cultural differences in knowledge of and perception about the U.S. child support system between mixed-status families and citizen families do not have an impact on the probability of getting a child support order, child support receipt, or in-kind child support. Rather, institutional factors such as collaborations between welfare agencies and child support enforcement agencies as well as state child support enforcement efforts have a significant impact on formal child support outcomes. The results are robust against different model specifications, measure constructions, and use of datasets. These findings have important policy implications for policy makers and researchers interested in reducing child poverty in complex family structures and underscore the need to revisit child support policies for mixed-status families. Copyright © 2016 Elsevier Inc. All rights reserved.

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

PubMed Central

Yang, Yingbao; Li, Xiaolong; Pan, Xin; Zhang, Yong; Cao, Chen

2017-01-01

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions. PMID:28368301

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.

A low-cost, tablet-based option for prehospital neurologic assessment: The iTREAT Study.

PubMed

Chapman Smith, Sherita N; Govindarajan, Prasanthi; Padrick, Matthew M; Lippman, Jason M; McMurry, Timothy L; Resler, Brian L; Keenan, Kevin; Gunnell, Brian S; Mehndiratta, Prachi; Chee, Christina Y; Cahill, Elizabeth A; Dietiker, Cameron; Cattell-Gordon, David C; Smith, Wade S; Perina, Debra G; Solenski, Nina J; Worrall, Bradford B; Southerland, Andrew M

2016-07-05

In this 2-center study, we assessed the technical feasibility and reliability of a low cost, tablet-based mobile telestroke option for ambulance transport and hypothesized that the NIH Stroke Scale (NIHSS) could be performed with similar reliability between remote and bedside examinations. We piloted our mobile telemedicine system in 2 geographic regions, central Virginia and the San Francisco Bay Area, utilizing commercial cellular networks for videoconferencing transmission. Standardized patients portrayed scripted stroke scenarios during ambulance transport and were evaluated by independent raters comparing bedside to remote mobile telestroke assessments. We used a mixed-effects regression model to determine intraclass correlation of the NIHSS between bedside and remote examinations (95% confidence interval). We conducted 27 ambulance runs at both sites and successfully completed the NIHSS for all prehospital assessments without prohibitive technical interruption. The mean difference between bedside (face-to-face) and remote (video) NIHSS scores was 0.25 (1.00 to -0.50). Overall, correlation of the NIHSS between bedside and mobile telestroke assessments was 0.96 (0.92-0.98). In the mixed-effects regression model, there were no statistically significant differences accounting for method of evaluation or differences between sites. Utilizing a low-cost, tablet-based platform and commercial cellular networks, we can reliably perform prehospital neurologic assessments in both rural and urban settings. Further research is needed to establish the reliability and validity of prehospital mobile telestroke assessment in live patients presenting with acute neurologic symptoms. © 2016 American Academy of Neurology.

A low-cost, tablet-based option for prehospital neurologic assessment

PubMed Central

Chapman Smith, Sherita N.; Govindarajan, Prasanthi; Padrick, Matthew M.; Lippman, Jason M.; McMurry, Timothy L.; Resler, Brian L.; Keenan, Kevin; Gunnell, Brian S.; Mehndiratta, Prachi; Chee, Christina Y.; Cahill, Elizabeth A.; Dietiker, Cameron; Cattell-Gordon, David C.; Smith, Wade S.; Perina, Debra G.; Solenski, Nina J.; Worrall, Bradford B.

2016-01-01

Objectives: In this 2-center study, we assessed the technical feasibility and reliability of a low cost, tablet-based mobile telestroke option for ambulance transport and hypothesized that the NIH Stroke Scale (NIHSS) could be performed with similar reliability between remote and bedside examinations. Methods: We piloted our mobile telemedicine system in 2 geographic regions, central Virginia and the San Francisco Bay Area, utilizing commercial cellular networks for videoconferencing transmission. Standardized patients portrayed scripted stroke scenarios during ambulance transport and were evaluated by independent raters comparing bedside to remote mobile telestroke assessments. We used a mixed-effects regression model to determine intraclass correlation of the NIHSS between bedside and remote examinations (95% confidence interval). Results: We conducted 27 ambulance runs at both sites and successfully completed the NIHSS for all prehospital assessments without prohibitive technical interruption. The mean difference between bedside (face-to-face) and remote (video) NIHSS scores was 0.25 (1.00 to −0.50). Overall, correlation of the NIHSS between bedside and mobile telestroke assessments was 0.96 (0.92–0.98). In the mixed-effects regression model, there were no statistically significant differences accounting for method of evaluation or differences between sites. Conclusions: Utilizing a low-cost, tablet-based platform and commercial cellular networks, we can reliably perform prehospital neurologic assessments in both rural and urban settings. Further research is needed to establish the reliability and validity of prehospital mobile telestroke assessment in live patients presenting with acute neurologic symptoms. PMID:27281534

Investigation on the effect of diaphragm on the combustion characteristics of solid-fuel ramjet

NASA Astrophysics Data System (ADS)

Gong, Lunkun; Chen, Xiong; Yang, Haitao; Li, Weixuan; Zhou, Changsheng

2017-10-01

The flow field characteristics and the regression rate distribution of solid-fuel ramjet with three-hole diaphragm were investigated by numerical and experimental methods. The experimental data were obtained by burning high-density polyethylene using a connected-pipe facility to validate the numerical model and analyze the combustion efficiency of the solid-fuel ramjet. The three-dimensional code developed in the present study adopted three-order MUSCL and central difference schemes, AUSMPW + flux vector splitting method, and second-order moment turbulence-chemistry model, together with k-ω shear stress transport (SST) turbulence model. The solid fuel surface temperature was calculated with fluid-solid heat coupling method. The numerical results show that strong circumferential flow exists in the region upstream of the diaphragm. The diaphragm can enhance the regression rate of the solid fuel in the region downstream of the diaphragm significantly, which mainly results from the increase of turbulent viscosity. As the diaphragm port area decreases, the regression rate of the solid fuel downstream of the diaphragm increases. The diaphragm can result in more sufficient mixing between the incoming air and fuel pyrolysis gases, while inevitably producing some pressure loss. The experimental results indicate that the effect of the diaphragm on the combustion efficiency of hydrocarbon fuels is slightly negative. It is conjectured that the diaphragm may have some positive effects on the combustion efficiency of the solid fuel with metal particles.

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.

Modeling recall memory for emotional objects in Alzheimer's disease.

PubMed

Sundstrøm, Martin

2011-07-01

To examine whether emotional memory (EM) of objects with self-reference in Alzheimer's disease (AD) can be modeled with binomial logistic regression in a free recall and an object recognition test to predict EM enhancement. Twenty patients with AD and twenty healthy controls were studied. Six objects (three presented as gifts) were shown to each participant. Ten minutes later, a free recall and a recognition test were applied. The recognition test had target-objects mixed with six similar distracter objects. Participants were asked to name any object in the recall test and identify each object in the recognition test as known or unknown. The total of gift objects recalled in AD patients (41.6%) was larger than neutral objects (13.3%) and a significant EM recall effect for gifts was found (Wilcoxon: p < .003). EM was not found for recognition in AD patients due to a ceiling effect. Healthy older adults scored overall higher in recall and recognition but showed no EM enhancement due to a ceiling effect. A logistic regression showed that likelihood of emotional recall memory can be modeled as a function of MMSE score (p < .014) and object status (p < .0001) as gift or non-gift. Recall memory was enhanced in AD patients for emotional objects indicating that EM in mild to moderate AD although impaired can be provoked with strong emotional load. The logistic regression model suggests that EM declines with the progression of AD rather than disrupts and may be a useful tool for evaluating magnitude of emotional load.

Sociodemographic Factors Associated With Changes in Successful Aging in Spain: A Follow-Up Study.

PubMed

Domènech-Abella, Joan; Perales, Jaime; Lara, Elvira; Moneta, Maria Victoria; Izquierdo, Ana; Rico-Uribe, Laura Alejandra; Mundó, Jordi; Haro, Josep Maria

2017-06-01

Successful aging (SA) refers to maintaining well-being in old age. Several definitions or models of SA exist (biomedical, psychosocial, and mixed). We examined the longitudinal association between various SA models and sociodemographic factors, and analyzed the patterns of change within these models. This was a nationally representative follow-up in Spain including 3,625 individuals aged ≥50 years. Some 1,970 individuals were interviewed after 3 years. Linear regression models were used to analyze the survey data. Age, sex, and occupation predicted SA in the biomedical model, while marital status, educational level, and urbanicity predicted SA in the psychosocial model. The remaining models included different sets of these predictors as significant. In the psychosocial model, individuals tended to improve over time but this was not the case in the biomedical model. The biomedical and psychosocial components of SA need to be addressed specifically to achieve the best aging trajectories.

Modeling indoor particulate exposures in inner city school classrooms

PubMed Central

Gaffin, Jonathan M.; Petty, Carter R.; Hauptman, Marissa; Kang, Choong-Min; Wolfson, Jack M.; Awad, Yara Abu; Di, Qian; Lai, Peggy S.; Sheehan, William J.; Baxi, Sachin; Coull, Brent A.; Schwartz, Joel D.; Gold, Diane R.; Koutrakis, Petros; Phipatanakul, Wanda

2016-01-01

Outdoor air pollution penetrates buildings and contributes to total indoor exposures. We investigated the relationship of indoor to outdoor particulate matter in inner-city school classrooms. The School Inner City Asthma Study investigates the effect of classroom-based environmental exposures on students with asthma in the northeast United States. Mixed-effects linear models were used to determine the relationships between indoor PM2.5 and BC and their corresponding outdoor concentrations, and to develop a model for predicting exposures to these pollutants. The indoor-outdoor sulfur ratio was used as an infiltration factor of outdoor fine particles. Weeklong concentrations of PM2.5 and BC in 199 samples from 136 classrooms (30 school buildings) were compared to those measured at a central monitoring site averaged over the same timeframe. Mixed effects regression models found significant random intercept and slope effects, which indicate that: 1) there are important PM2.5 sources in classrooms; 2) the penetration of outdoor PM2.5 particles varies by school, and 3) the site-specific outside PM2.5 levels (inferred by the models) differ from those observed at the central monitor site. Similar results were found for BC except for lack of indoor sources. The fitted predictions from the sulfur-adjusted models were moderately predictive of observed indoor pollutant levels (Out of sample correlations: PM2.5: r2 = 0.68, BC; r2 = 0.61). Our results suggest that PM2.5 has important classroom sources, which vary by school. Furthermore, using these mixed effects models, classroom exposures can be accurately predicted for dates when central site measures are available but indoor measures are not available. PMID:27599884

The Calibration of AVHRR/3 Visible Dual Gain Using Meteosat-8 as a MODIS Calibration Transfer Medium

NASA Technical Reports Server (NTRS)

Avey, Lance; Garber, Donald; Nguyen, Louis; Minnis, Patrick

2007-01-01

This viewgraph presentation reviews the NOAA-17 AVHRR visible channels calibrated against MET-8/MODIS using dual gain regression methods. The topics include: 1) Motivation; 2) Methodology; 3) Dual Gain Regression Methods; 4) Examples of Regression methods; 5) AVHRR/3 Regression Strategy; 6) Cross-Calibration Method; 7) Spectral Response Functions; 8) MET8/NOAA-17; 9) Example of gain ratio adjustment; 10) Effect of mixed low/high count FOV; 11) Monitor dual gains over time; and 12) Conclusions

Self-rated health: small area large area comparisons amongst older adults at the state, district and sub-district level in India.

PubMed

Hirve, Siddhivinayak; Vounatsou, Penelope; Juvekar, Sanjay; Blomstedt, Yulia; Wall, Stig; Chatterji, Somnath; Ng, Nawi

2014-03-01

We compared prevalence estimates of self-rated health (SRH) derived indirectly using four different small area estimation methods for the Vadu (small) area from the national Study on Global AGEing (SAGE) survey with estimates derived directly from the Vadu SAGE survey. The indirect synthetic estimate for Vadu was 24% whereas the model based estimates were 45.6% and 45.7% with smaller prediction errors and comparable to the direct survey estimate of 50%. The model based techniques were better suited to estimate the prevalence of SRH than the indirect synthetic method. We conclude that a simplified mixed effects regression model can produce valid small area estimates of SRH. © 2013 Published by Elsevier Ltd.

METHAMPHETAMINE USE AMONG RURAL WHITE AND NATIVE AMERICAN ADOLESCENTS: AN APPLICATION OF THE STRESS PROCESS MODEL

PubMed Central

Eitle, David J.; McNulty Eitle, Tamela

2016-01-01

Methamphetamine use has been identified as having significant adverse health consequences, yet we know little about the correlates of its use. Additionally, research has found that Native Americans are at the highest risk for methamphetamine use. Our exploratory study, informed by the stress process model, examines stress and stress buffering factors associated with methamphetamine use among a cross-sectional sample of rural white and Native American adolescents (n=573). Results of logistic regression analyses revealed mixed support for the stress process model; while stress exposure and family methamphetamine use predicted past year methamphetamine use, the inclusion of these variables failed to attenuate the association between race and past year use. PMID:25445505

Patient satisfaction with ambulatory care in Germany: effects of patient- and medical practice-related factors.

PubMed

Auras, Silke; Ostermann, Thomas; de Cruppé, Werner; Bitzer, Eva-Maria; Diel, Franziska; Geraedts, Max

2016-12-01

The study aimed to illustrate the effect of the patients' sex, age, self-rated health and medical practice specialization on patient satisfaction. Secondary analysis of patient survey data using multilevel analysis (generalized linear mixed model, medical practice as random effect) using a sequential modelling strategy. We examined the effects of the patients' sex, age, self-rated health and medical practice specialization on four patient satisfaction dimensions: medical practice organization, information, interaction, professional competence. The study was performed in 92 German medical practices providing ambulatory care in general medicine, internal medicine or gynaecology. In total, 9888 adult patients participated in a patient survey using the validated 'questionnaire on satisfaction with ambulatory care-quality from the patient perspective [ZAP]'. We calculated four models for each satisfaction dimension, revealing regression coefficients with 95% confidence intervals (CIs) for all independent variables, and using Wald Chi-Square statistic for each modelling step (model validity) and LR-Tests to compare the models of each step with the previous model. The patients' sex and age had a weak effect (maximum regression coefficient 1.09, CI 0.39; 1.80), and the patients' self-rated health had the strongest positive effect (maximum regression coefficient 7.66, CI 6.69; 8.63) on satisfaction ratings. The effect of medical practice specialization was heterogeneous. All factors studied, specifically the patients' self-rated health, affected patient satisfaction. Adjustment should always be considered because it improves the comparability of patient satisfaction in medical practices with atypically varying patient populations and increases the acceptance of comparisons. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; MöLg, Thomas; Marzeion, Ben; Kaser, Georg

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) provide temporally high-resolution, yet short-term, atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

Regression analysis of mixed recurrent-event and panel-count data.

PubMed

Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L

2014-07-01

In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20: , 1-42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Developing a case mix classification for child and adolescent mental health services: the influence of presenting problems, complexity factors and service providers on number of appointments.

PubMed

Martin, Peter; Davies, Roger; Macdougall, Amy; Ritchie, Benjamin; Vostanis, Panos; Whale, Andy; Wolpert, Miranda

2017-09-01

Case-mix classification is a focus of international attention in considering how best to manage and fund services, by providing a basis for fairer comparison of resource utilization. Yet there is little evidence of the best ways to establish case mix for child and adolescent mental health services (CAMHS). To develop a case mix classification for CAMHS that is clinically meaningful and predictive of number of appointments attended and to investigate the influence of presenting problems, context and complexity factors and provider variation. We analysed 4573 completed episodes of outpatient care from 11 English CAMHS. Cluster analysis, regression trees and a conceptual classification based on clinical best practice guidelines were compared regarding their ability to predict number of appointments, using mixed effects negative binomial regression. The conceptual classification is clinically meaningful and did as well as data-driven classifications in accounting for number of appointments. There was little evidence for effects of complexity or context factors, with the possible exception of school attendance problems. Substantial variation in resource provision between providers was not explained well by case mix. The conceptually-derived classification merits further testing and development in the context of collaborative decision making.

Solubility of pharmaceuticals: A comparison between SciPharma, a PC-SAFT-based approach, and NRTL-SAC

NASA Astrophysics Data System (ADS)

Bouillot, Baptiste; Spyriouni, Theodora; Teychené, Sébastien; Biscans, Béatrice

2017-04-01

The solubility of seven pharmaceutical compounds (paracetamol, benzoic acid, 4-aminobenzoic acid, salicylic acid, ibuprofen, naproxen and temazepam) in pure and mixed solvents as a function of temperature is calculated with SciPharma, a semi-empirical approach based on PC-SAFT, and the NRTL-SAC model. To conduct a fair comparison between the approaches, the parameters of the compounds were regressed against the same solubility data, chosen to account for hydrophilic, polar and hydrophobic interactions. Only these solubility data were used by both models for predicting solubility in other pure and mixed solvents for which experimental data were available for comparison. A total of 386 pure solvent data points were used for the comparison comprising one or more temperatures per solvent. SciPharma is found to be more accurate than NRTL-SAC on the pure solvent data used especially in the description of the temperature dependence. This is due to the appropriate parameterization of the pharmaceuticals and the temperature-dependent description of the activity coefficient in PC-SAFT. The solubility in mixed solvents is predicted satisfactorily with SciPharma. NRTL-SAC tends to overestimate the solubility in aqueous solutions of alcohols or shows invariable solubility with composition in other cases.

Association between percutaneous hemodynamic support device and survival from cardiac arrest in the state of Michigan.

PubMed

Pressman, Andrew; Sawyer, Kelly N; Devlin, William; Swor, Robert

2018-05-01

The role of circulatory support in the post-cardiac arrest period remains controversial. Our objective was to investigate the association between treatment with a percutaneous hemodynamic support device and outcome after admission for cardiac arrest. We performed a retrospective study of adult patients with admission diagnosis of cardiac arrest or ventricular fibrillation (VF) from the Michigan Inpatient Database, treated between July 1, 2010, and June 30, 2013. Patient demographics, clinical characteristics, treatments, and disposition were electronically abstracted based on ICD-9 codes at the hospital level. Mixed-effects logistic regression models were fit to test the effect of percutaneous hemodynamic support device defined as either percutaneous left ventricular assist device (pLVAD) or intra-aortic balloon pump (IABP) on survival. These models controlled for age, sex, VF, myocardial infarction (MI), and cardiogenic shock with hospital modeled as a random effect. A total of 103 hospitals contributed 4393 patients for analysis, predominately male (58.8%) with a mean age of 64.1years (SD 15.5). On univariate analysis, younger age, male sex, VF as the initial rhythm, acute MI, percutaneous coronary intervention, percutaneous hemodynamic support device, and absence of cardiogenic shock were associated with survival to discharge (each p<0.001). Mixed-effects logistic regressions revealed use of percutaneous hemodynamic support device was significantly associated with survival among all patients (OR 1.8 (1.28-2.54)), and especially in those with acute MI (OR 1.95 (1.31-2.93)) or cardiogenic shock (OR 1.96 (1.29-2.98)). Treatment with percutaneous hemodynamic support device in the post-arrest period may provide left ventricular support and improve outcome. Copyright © 2017 Elsevier Inc. All rights reserved.

Estimation of evaporation from equilibrium diurnal boundary layer humidity

NASA Astrophysics Data System (ADS)

Salvucci, G.; Rigden, A. J.; Li, D.; Gentine, P.

2017-12-01

Simplified conceptual models of the convective boundary layer as a well mixed profile of potential temperature (theta) and specific humidity (q) impinging on an initially stably stratified linear potential temperature profile have a long history in atmospheric sciences. These one dimensional representations of complex mixing are useful for gaining insights into land-atmosphere interactions and for prediction when state of the art LES approaches are infeasible. As previously shown (e.g. Betts), if one neglects the role of q in bouyancy, the framework yields a unique relation between mixed layer Theta, mixed layer height (h), and cumulative sensible heat flux (SH) throughout the day. Similarly assuming an initially q profile yields a simple relation between q, h, and cumulative latent heat flux (LH). The diurnal dynamics of theta and q are strongly dependent on SH and the initial lapse rates of theta (gamma_thet) and q (gamma q). In the estimation method proposed here, we further constrain these relations with two more assumptions: 1) The specific humidity is the same at the start of the period of boundary layer growth and at the collapse; and 2) Once the mixed layer reaches the LCL, further drying occurs proportionally to the deardorff convective velocity scale (omega) multiplied by q. Assumption (1) is based on the idea that below the cloud layer, there are no sinks of moisture within the mixed layer (neglecting lateral humidity divergence). Thus the net mixing of dry air aloft with evaporation from the surface must balance. Inclusion of the simple model of moisture loss above the LCL into the bulk-CBL model allows definition of an equilibrium humidity (q) condition at which the diurnal cycle of q repeats (i.e. additions of q from surface balance entrainment of dry air from above). Surprisingly, this framework allows estimation of LH from q, theta, and estimated net radiation by solving for the value of Evaporative Fraction (EF) for which the diurnal cycle of q repeats. Three parameters need specification: cloud area fraction, entrainment factor, and morning lapse rate. Surprisingly, a single set of values for these parameters are adequate to estimate EF at over 70 tested Ameriflux sites to within about 20%, though improvements are gained using a single regression model for gamma_thet that has been fitted to radiosonde data.

Developing a stroke severity index based on administrative data was feasible using data mining techniques.

PubMed

Sung, Sheng-Feng; Hsieh, Cheng-Yang; Kao Yang, Yea-Huei; Lin, Huey-Juan; Chen, Chih-Hung; Chen, Yu-Wei; Hu, Ya-Han

2015-11-01

Case-mix adjustment is difficult for stroke outcome studies using administrative data. However, relevant prescription, laboratory, procedure, and service claims might be surrogates for stroke severity. This study proposes a method for developing a stroke severity index (SSI) by using administrative data. We identified 3,577 patients with acute ischemic stroke from a hospital-based registry and analyzed claims data with plenty of features. Stroke severity was measured using the National Institutes of Health Stroke Scale (NIHSS). We used two data mining methods and conventional multiple linear regression (MLR) to develop prediction models, comparing the model performance according to the Pearson correlation coefficient between the SSI and the NIHSS. We validated these models in four independent cohorts by using hospital-based registry data linked to a nationwide administrative database. We identified seven predictive features and developed three models. The k-nearest neighbor model (correlation coefficient, 0.743; 95% confidence interval: 0.737, 0.749) performed slightly better than the MLR model (0.742; 0.736, 0.747), followed by the regression tree model (0.737; 0.731, 0.742). In the validation cohorts, the correlation coefficients were between 0.677 and 0.725 for all three models. The claims-based SSI enables adjusting for disease severity in stroke studies using administrative data. Copyright © 2015 Elsevier Inc. All rights reserved.

Nondestructive prediction of the drug content of an aspirin suppository by near-infrared spectroscopy.

PubMed

Otsuka, Eri; Abe, Hiroyuki; Aburada, Masaki; Otsuka, Makoto

2010-07-01

A suppository dosage form has a rapid effect on therapeutics, because it dissolves in the rectum, is absorbed in the bloodstream, and passes the hepatic metabolism. However, the dosage form is unstable, because a suppository is made in a semisolid form, and so it is not easy to mix the bulk drug powder in the base. This article describes a nondestructive method of determining the drug content of suppositories using near-infrared spectrometry (NIR) combined with chemometrics. Suppositories (aspirin content: 1.8, 2.7, 4.5, 7.3, and 9.1%, w/w) were produced by mixing an aspirin bulk powder with hard fat at 50 degrees C and pouring the melt mixture into a plastic mold (2.25 mL). NIR spectra of 12 calibration and 12 validation sample sets were recorded 5 times. A total of 60 spectral data were used as a calibration set to establish a calibration model to predict drug content with a partial least-squares (PLS) regression analysis. NIR data of the suppository samples were divided into two wave number ranges, 4000-12500 cm(-1) (LR), and 5900-6300 cm(-1) (SR). Calibration models for the aspirin content of the suppositories were calculated based on LR and SR ranges of second-derivative NIR spectra using PLS. The models for LR and SR consisted of five and one principal components (PC), respectively. The plots of predicted values against actual values gave a straight line with regression coefficient constants of 0.9531 and 0.9749, respectively. The mean bias and mean accuracy of the calibration models were calculated based on the SR of variation data sets, and were lower than those of LR, respectively. Limiting the wave number of spectral data sets is useful to help understand the calibration model because of noise cancellation and to measure objective functions.

Linear mixed-effects models to describe individual tree crown width for China-fir in Fujian Province, southeast China.

PubMed

Hao, Xu; Yujun, Sun; Xinjie, Wang; Jin, Wang; Yao, Fu

2015-01-01

A multiple linear model was developed for individual tree crown width of Cunninghamia lanceolata (Lamb.) Hook in Fujian province, southeast China. Data were obtained from 55 sample plots of pure China-fir plantation stands. An Ordinary Linear Least Squares (OLS) regression was used to establish the crown width model. To adjust for correlations between observations from the same sample plots, we developed one level linear mixed-effects (LME) models based on the multiple linear model, which take into account the random effects of plots. The best random effects combinations for the LME models were determined by the Akaike's information criterion, the Bayesian information criterion and the -2logarithm likelihood. Heteroscedasticity was reduced by three residual variance functions: the power function, the exponential function and the constant plus power function. The spatial correlation was modeled by three correlation structures: the first-order autoregressive structure [AR(1)], a combination of first-order autoregressive and moving average structures [ARMA(1,1)], and the compound symmetry structure (CS). Then, the LME model was compared to the multiple linear model using the absolute mean residual (AMR), the root mean square error (RMSE), and the adjusted coefficient of determination (adj-R2). For individual tree crown width models, the one level LME model showed the best performance. An independent dataset was used to test the performance of the models and to demonstrate the advantage of calibrating LME models.

The Relationship Between Oxygen Reserve Index and Arterial Partial Pressure of Oxygen During Surgery

PubMed Central

Dorotta, Ihab L.; Wells, Briana; Juma, David; Applegate, Patricia M.

2016-01-01

BACKGROUND: The use of intraoperative pulse oximetry (Spo2) enhances hypoxia detection and is associated with fewer perioperative hypoxic events. However, Spo2 may be reported as 98% when arterial partial pressure of oxygen (Pao2) is as low as 70 mm Hg. Therefore, Spo2 may not provide advance warning of falling arterial oxygenation until Pao2 approaches this level. Multiwave pulse co-oximetry can provide a calculated oxygen reserve index (ORI) that may add to information from pulse oximetry when Spo2 is >98%. This study evaluates the ORI to Pao2 relationship during surgery. METHODS: We studied patients undergoing scheduled surgery in which arterial catheterization and intraoperative arterial blood gas analysis were planned. Data from multiple pulse co-oximetry sensors on each patient were continuously collected and stored on a research computer. Regression analysis was used to compare ORI with Pao2 obtained from each arterial blood gas measurement and changes in ORI with changes in Pao2 from sequential measurements. Linear mixed-effects regression models for repeated measures were then used to account for within-subject correlation across the repeatedly measured Pao2 and ORI and for the unequal time intervals of Pao2 determination over elapsed surgical time. Regression plots were inspected for ORI values corresponding to Pao2 of 100 and 150 mm Hg. ORI and Pao2 were compared using mixed-effects models with a subject-specific random intercept. RESULTS: ORI values and Pao2 measurements were obtained from intraoperative data collected from 106 patients. Regression analysis showed that the ORI to Pao2 relationship was stronger for Pao2 to 240 mm Hg (r2 = 0.536) than for Pao2 over 240 mm Hg (r2 = 0.0016). Measured Pao2 was ≥100 mm Hg for all ORI over 0.24. Measured Pao2 was ≥150 mm Hg in 96.6% of samples when ORI was over 0.55. A random intercept variance component linear mixed-effects model for repeated measures indicated that Pao2 was significantly related to ORI (β[95% confidence interval] = 0.002 [0.0019–0.0022]; P < 0.0001). A similar analysis indicated a significant relationship between change in Pao2 and change in ORI (β [95% confidence interval] = 0.0044 [0.0040–0.0048]; P < 0.0001). CONCLUSIONS: These findings suggest that ORI >0.24 can distinguish Pao2 ≥100 mm Hg when Spo2 is over 98%. Similarly, ORI > 0.55 appears to be a threshold to distinguish Pao2 ≥150 mm Hg. The usefulness of these values should be evaluated prospectively. Decreases in ORI to near 0.24 may provide advance indication of falling Pao2 approaching 100 mm Hg when Spo2 is >98%. The clinical utility of interventions based on continuous ORI monitoring should be studied prospectively. PMID:27007078

The Relationship Between Oxygen Reserve Index and Arterial Partial Pressure of Oxygen During Surgery.

PubMed

Applegate, Richard L; Dorotta, Ihab L; Wells, Briana; Juma, David; Applegate, Patricia M

2016-09-01

The use of intraoperative pulse oximetry (SpO2) enhances hypoxia detection and is associated with fewer perioperative hypoxic events. However, SpO2 may be reported as 98% when arterial partial pressure of oxygen (PaO2) is as low as 70 mm Hg. Therefore, SpO2 may not provide advance warning of falling arterial oxygenation until PaO2 approaches this level. Multiwave pulse co-oximetry can provide a calculated oxygen reserve index (ORI) that may add to information from pulse oximetry when SpO2 is >98%. This study evaluates the ORI to PaO2 relationship during surgery. We studied patients undergoing scheduled surgery in which arterial catheterization and intraoperative arterial blood gas analysis were planned. Data from multiple pulse co-oximetry sensors on each patient were continuously collected and stored on a research computer. Regression analysis was used to compare ORI with PaO2 obtained from each arterial blood gas measurement and changes in ORI with changes in PaO2 from sequential measurements. Linear mixed-effects regression models for repeated measures were then used to account for within-subject correlation across the repeatedly measured PaO2 and ORI and for the unequal time intervals of PaO2 determination over elapsed surgical time. Regression plots were inspected for ORI values corresponding to PaO2 of 100 and 150 mm Hg. ORI and PaO2 were compared using mixed-effects models with a subject-specific random intercept. ORI values and PaO2 measurements were obtained from intraoperative data collected from 106 patients. Regression analysis showed that the ORI to PaO2 relationship was stronger for PaO2 to 240 mm Hg (r = 0.536) than for PaO2 over 240 mm Hg (r = 0.0016). Measured PaO2 was ≥100 mm Hg for all ORI over 0.24. Measured PaO2 was ≥150 mm Hg in 96.6% of samples when ORI was over 0.55. A random intercept variance component linear mixed-effects model for repeated measures indicated that PaO2 was significantly related to ORI (β[95% confidence interval] = 0.002 [0.0019-0.0022]; P < 0.0001). A similar analysis indicated a significant relationship between change in PaO2 and change in ORI (β [95% confidence interval] = 0.0044 [0.0040-0.0048]; P < 0.0001). These findings suggest that ORI >0.24 can distinguish PaO2 ≥100 mm Hg when SpO2 is over 98%. Similarly, ORI > 0.55 appears to be a threshold to distinguish PaO2 ≥150 mm Hg. The usefulness of these values should be evaluated prospectively. Decreases in ORI to near 0.24 may provide advance indication of falling PaO2 approaching 100 mm Hg when SpO2 is >98%. The clinical utility of interventions based on continuous ORI monitoring should be studied prospectively.

Non-homogeneous hybrid rocket fuel for enhanced regression rates utilizing partial entrainment

NASA Astrophysics Data System (ADS)

Boronowsky, Kenny

A concept was developed and tested to enhance the performance and regression rate of hydroxyl terminated polybutadiene (HTPB), a commonly used hybrid rocket fuel. By adding small nodules of paraffin into the HTPB fuel, a non-homogeneous mixture was created resulting in increased regression rates. The goal was to develop a fuel with a simplified single core geometry and a tailorable regression rate. The new fuel would benefit from the structural stability of HTPB yet not suffer from the large void fraction representative of typical HTPB core geometries. Regression rates were compared between traditional HTPB single core grains, 85% HTPB mixed with 15% (by weight) paraffin cores, 70% HTPB mixed with 30% paraffin cores, and plain paraffin single core grains. Each fuel combination was tested at oxidizer flow rates, ranging from 0.9 - 3.3 g/s of gaseous oxygen, in a small scale hybrid test rocket and average regression rates were measured. While large uncertainties were present in the experimental setup, the overall data showed that the regression rate was enhanced as paraffin concentration increased. While further testing would be required at larger scales of interest, the trends are encouraging. Inclusion of paraffin nodules in the HTPB grain may produce a greater advantage than other more noxious additives in current use. In addition, it may lead to safer rocket motors with higher integrated thrust due to the decreased void fraction.

Understanding the Impact of School Factors on School Counselor Burnout: A Mixed-Methods Study

ERIC Educational Resources Information Center

Bardhoshi, Gerta; Schweinle, Amy; Duncan, Kelly

2014-01-01

This mixed-methods study investigated the relationship between burnout and performing noncounseling duties among a national sample of professional school counselors, while identifying school factors that could attenuate this relationship. Results of regression analyses indicate that performing noncounseling duties significantly predicted burnout…

D.b.h./crown diameter relationships in mixed Appalachian hardwood stands

Treesearch

Neil I. Lamson; Neil I. Lamson

1987-01-01

Linear regression formulae for predicting crown diameter as a function of stem diameter are presented for nine species found in 50- to 80-year-old mixed hardwood stands in north-central West Virginia. Generally, crown diameter was closely related to tolerance; more tolerant species had larger crowns.

Modeling CO{sub 2} and H{sub 2}S solubility in MDEA and DEA: Design implications

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

Rochelle, G.T.; Posey, M.

1996-12-31

The solubility of H{sub 2}S and CO{sub 2} in aqueous alkanolamines affects solution capacity and the required circulation rate for acid gas absorption. These thermodynamics also determine the relationship of steam rate and the lean loading of the solution which in turn sets the leak of acid gas from the top of the absorber. Finally, the mechanisms of mass transfer and the role of kinetics, especially in stripping, depend on the vapor/liquid equilibria. Published measurements of CO{sub 2} and H{sub 2}S solubility in methyldiethanolamine (MDEA) and diethanolamine (DEA) are not in general agreement, especially at low loading of acid gas.more » The available sets of solubility data have been regressed with the AspenPlus electrolyte/NRTL model. All of the parameters and constants that make up this model have been carefully evaluated. Independent thermodynamic data such as freezing point and heat of mixing have been included in the regression to strengthen the estimates of model parameters. The parameters for each set of solubility data have been evaluated in an attempt to determine which set is correct. Each evaluated model has been used to calculate the acid gas capacity and minimum stripping steam rate for several industrial cases of acid gas absorption/stripping.« less

Paying for Primary Care: The Factors Associated with Physician Self-selection into Payment Models.

PubMed

Rudoler, David; Deber, Raisa; Barnsley, Janet; Glazier, Richard H; Dass, Adrian Rohit; Laporte, Audrey

2015-09-01

To determine the factors associated with primary care physician self-selection into different payment models, we used a panel of eight waves of administrative data for all primary care physicians who practiced in Ontario between 2003/2004 and 2010/2011. We used a mixed effects logistic regression model to estimate physicians' choice of three alternative payment models: fee for service, enhanced fee for service, and blended capitation. We found that primary care physicians self-selected into payment models based on existing practice characteristics. Physicians with more complex patient populations were less likely to switch into capitation-based payment models where higher levels of effort were not financially rewarded. These findings suggested that investigations aimed at assessing the impact of different primary care reimbursement models on outcomes, including costs and access, should first account for potential selection effects. Copyright © 2015 John Wiley & Sons, Ltd.

Radio Propagation Prediction Software for Complex Mixed Path Physical Channels

DTIC Science & Technology

2006-08-14

63 4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz 69 4.4.7. Projected Scaling to...4.4.6. Applied Linear Regression Analysis in the Frequency Range 1-50 MHz In order to construct a comprehensive numerical algorithm capable of

Accelerating Improvement and Narrowing Gaps: Trends in Patients' Experiences with Hospital Care Reflected in HCAHPS Public Reporting.

PubMed

Elliott, Marc N; Cohea, Christopher W; Lehrman, William G; Goldstein, Elizabeth H; Cleary, Paul D; Giordano, Laura A; Beckett, Megan K; Zaslavsky, Alan M

2015-12-01

Measure HCAHPS improvement in hospitals participating in the second and fifth years of HCAHPS public reporting; determine whether change is greater for some hospital types. Surveys from 4,822,960 adult inpatients discharged July 2007-June 2008 or July 2010-June 2011 from 3,541 U.S. hospitals. Linear mixed-effect regression models with fixed effects for time, patient mix, and hospital characteristics (bedsize, ownership, Census division, teaching status, Critical Access status); random effects for hospitals and hospital-time interactions; fixed-effect interactions of hospital characteristics and patient characteristics (gender, health, education) with time predicted HCAHPS measures correcting for regression-to-the-mean biases. National probability sample of adult inpatients in any of four approved survey modes. HCAHPS scores increased by 2.8 percentage points from 2008 to 2011 in the most positive response category. Among the middle 95 percent of hospitals, changes ranged from a 5.1 percent decrease to a 10.2 percent gain overall. The greatest improvement was in for-profit and larger (200 or more beds) hospitals. Five years after HCAHPS public reporting began, meaningful improvement of patients' hospital care experiences continues, especially among initially low-scoring hospitals, reducing some gaps among hospitals. © Health Research and Educational Trust.

[Key physical parameters of hawthorn leaf granules by stepwise regression analysis method].

PubMed

Jiang, Qie-Ying; Zeng, Rong-Gui; Li, Zhe; Luo, Juan; Zhao, Guo-Wei; Lv, Dan; Liao, Zheng-Gen

2017-05-01

The purpose of this study was to investigate the effect of key physical properties of hawthorn leaf granule on its dissolution behavior. Hawthorn leaves extract was utilized as a model drug. The extract was mixed with microcrystalline cellulose or starch with the same ratio by using different methods. Appropriate amount of lubricant and disintegrating agent was added into part of the mixed powder, and then the granules were prepared by using extrusion granulation and high shear granulation. The granules dissolution behavior was evaluated by using equilibrium dissolution quantity and dissolution rate constant of the hypericin as the indicators. Then the effect of physical properties on dissolution behavior was analyzed through the stepwise regression analysis method. The equilibrium dissolution quantity of hypericin and adsorption heat constant in hawthorn leaves were positively correlated with the monolayer adsorption capacity and negatively correlated with the moisture absorption rate constant. The dissolution rate constants were decreased with the increase of Hausner rate, monolayer adsorption capacity and adsorption heat constant, and were increased with the increase of Carr index and specific surface area. Adsorption heat constant, monolayer adsorption capacity, moisture absorption rate constant, Carr index and specific surface area were the key physical properties of hawthorn leaf granule to affect its dissolution behavior. Copyright© by the Chinese Pharmaceutical Association.

Using FTIR spectroscopy to model alkaline pretreatment and enzymatic saccharification of six lignocellulosic biomasses.

PubMed

Sills, Deborah L; Gossett, James M

2012-04-01

Fourier transform infrared, attenuated total reflectance (FTIR-ATR) spectroscopy, combined with partial least squares (PLS) regression, accurately predicted solubilization of plant cell wall constituents and NaOH consumption through pretreatment, and overall sugar productions from combined pretreatment and enzymatic hydrolysis. PLS regression models were constructed by correlating FTIR spectra of six raw biomasses (two switchgrass cultivars, big bluestem grass, a low-impact, high-diversity mixture of prairie biomasses, mixed hardwood, and corn stover), plus alkali loading in pretreatment, to nine dependent variables: glucose, xylose, lignin, and total solids solubilized in pretreatment; NaOH consumed in pretreatment; and overall glucose and xylose conversions and yields from combined pretreatment and enzymatic hydrolysis. PLS models predicted the dependent variables with the following values of coefficient of determination for cross-validation (Q²): 0.86 for glucose, 0.90 for xylose, 0.79 for lignin, and 0.85 for total solids solubilized in pretreatment; 0.83 for alkali consumption; 0.93 for glucose conversion, 0.94 for xylose conversion, and 0.88 for glucose and xylose yields. The sugar yield models are noteworthy for their ability to predict overall saccharification through combined pretreatment and enzymatic hydrolysis per mass dry untreated solids without a priori knowledge of the composition of solids. All wavenumbers with significant variable-important-for-projection (VIP) scores have been attributed to chemical features of lignocellulose, demonstrating the models were based on real chemical information. These models suggest that PLS regression can be applied to FTIR-ATR spectra of raw biomasses to rapidly predict effects of pretreatment on solids and on subsequent enzymatic hydrolysis. Copyright © 2011 Wiley Periodicals, Inc.

Robust scoring functions for protein-ligand interactions with quantum chemical charge models.

PubMed

Wang, Jui-Chih; Lin, Jung-Hsin; Chen, Chung-Ming; Perryman, Alex L; Olson, Arthur J

2011-10-24

Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein-ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin-model 1-bond charge correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein-ligand free energy regression model with AM1-BCC charges for ligands and Amber99SB charges for proteins achieve lowest root-mean-squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted root-mean-squared deviation of less than 2 Å. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed).

Synergistic drug-cytokine induction of hepatocellular death as an in vitro approach for the study of inflammation-associated idiosyncratic drug hepatotoxicity

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

Cosgrove, Benjamin D.; Cell Decision Processes Center, Massachusetts Institute of Technology, Cambridge, MA; Biotechnology Process Engineering Center, Massachusetts Institute of Technology, Cambridge, MA

Idiosyncratic drug hepatotoxicity represents a major problem in drug development due to inadequacy of current preclinical screening assays, but recently established rodent models utilizing bacterial LPS co-administration to induce an inflammatory background have successfully reproduced idiosyncratic hepatotoxicity signatures for certain drugs. However, the low-throughput nature of these models renders them problematic for employment as preclinical screening assays. Here, we present an analogous, but high-throughput, in vitro approach in which drugs are administered to a variety of cell types (primary human and rat hepatocytes and the human HepG2 cell line) across a landscape of inflammatory contexts containing LPS and cytokines TNF,more » IFN{gamma}, IL-1{alpha}, and IL-6. Using this assay, we observed drug-cytokine hepatotoxicity synergies for multiple idiosyncratic hepatotoxicants (ranitidine, trovafloxacin, nefazodone, nimesulide, clarithromycin, and telithromycin) but not for their corresponding non-toxic control compounds (famotidine, levofloxacin, buspirone, and aspirin). A larger compendium of drug-cytokine mix hepatotoxicity data demonstrated that hepatotoxicity synergies were largely potentiated by TNF, IL-1{alpha}, and LPS within the context of multi-cytokine mixes. Then, we screened 90 drugs for cytokine synergy in human hepatocytes and found that a significantly larger fraction of the idiosyncratic hepatotoxicants (19%) synergized with a single cytokine mix than did the non-hepatotoxic drugs (3%). Finally, we used an information theoretic approach to ascertain especially informative subsets of cytokine treatments for most highly effective construction of regression models for drug- and cytokine mix-induced hepatotoxicities across these cell systems. Our results suggest that this drug-cytokine co-treatment approach could provide a useful preclinical tool for investigating inflammation-associated idiosyncratic drug hepatotoxicity.« less

Does the presence and mix of destinations influence walking and physical activity?

PubMed

King, Tania Louise; Bentley, Rebecca Jodie; Thornton, Lukar Ezra; Kavanagh, Anne Marie

2015-09-17

Local destinations have previously been shown to be associated with higher levels of both physical activity and walking, but little is known about how specific destinations are related to activity. This study examined associations between types and mix of destinations and both walking frequency and physical activity. The sample consisted of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Using geographic information systems, seven types of destinations were examined within three network buffers (400 meters (m), 800 m and 1200 m) of respondents' homes. Multilevel logistic regression was used to estimate effects of each destination type separately, as well as destination mix (variety) on: 1) likelihood of walking for at least 10 min ≥ 4/week; 2) likelihood of being sufficiently physically active. All models were adjusted for potential confounders. All destination types were positively associated with walking frequency, and physical activity sufficiency at 1200 m. For the 800 m buffer: all destinations except transport stops and sports facilities were significantly associated with physical activity, while all except sports facilities were associated with walking frequency; at 400 m, café/takeaway food stores and transport stops were associated with walking frequency and physical activity sufficiency, and sports facilities were also associated with walking frequency. Strongest associations for both outcomes were observed for community resources and small food stores at both 800 m and 1200 m. For all buffer distances: greater mix was associated with greater walking frequency. Inclusion of walking in physical activity models led to attenuation of associations. The results of this analysis indicate that there is an association between destinations and both walking frequency and physical activity sufficiency, and that this relationship varies by destination type. It is also clear that greater mix of destinations positively predicts walking frequency and physical activity sufficiency.

Using nitrate dual isotopic composition (δ15N and δ18O) as a tool for exploring sources and cycling of nitrate in an estuarine system: Elkhorn Slough, California

USGS Publications Warehouse

Wankel, Scott D.; Kendall, Carol; Paytan, Adina

2009-01-01

Nitrate (NO-3 concentrations and dual isotopic composition (??15N and ??18O) were measured during various seasons and tidal conditions in Elkhorn Slough to evaluate mixing of sources of NO-3 within this California estuary. We found the isotopic composition of NO-3 was influenced most heavily by mixing of two primary sources with unique isotopic signatures, a marine (Monterey Bay) and terrestrial agricultural runoff source (Old Salinas River). However, our attempt to use a simple two end-member mixing model to calculate the relative contribution of these two NO-3 sources to the Slough was complicated by periods of nonconservative behavior and/or the presence of additional sources, particularly during the dry season when NO-3 concentrations were low. Although multiple linear regression generally yielded good fits to the observed data, deviations from conservative mixing were still evident. After consideration of potential alternative sources, we concluded that deviations from two end-member mixing were most likely derived from interactions with marsh sediments in regions of the Slough where high rates of NO-3 uptake and nitrification result in NO-3 with low ?? 15N and high ??18O values. A simple steady state dual isotope model is used to illustrate the impact of cycling processes in an estuarine setting which may play a primary role in controlling NO -3 isotopic composition when and where cycling rates and water residence times are high. This work expands our understanding of nitrogen and oxygen isotopes as biogeochemical tools for investigating NO -3 sources and cycling in estuaries, emphasizing the role that cycling processes may play in altering isotopic composition. Copyright 2009 by the American Geophysical Union.

Regression of a vaginal leiomyoma after ovariohysterectomy in a dog: a case report.

PubMed

Sathya, Suresh; Linn, Kathleen

2014-01-01

An 11 yr old female mixed-breed Siberian husky was presented with a history of sanguineous vaginal discharge, swelling of the perineal area, decreased appetite, and lethargy. A single, large vaginal leiomyoma and multiple mammary tumors were diagnosed. Mastectomy and ovariohysterectomy were performed. The vaginal leiomyoma regressed completely after ovariohysterectomy. This is the first reported case of spontaneous regression of a vaginal leiomyoma after ovariohysterectomy in a dog.

Numerical simulation of Forchheimer flow to a partially penetrating well with a mixed-type boundary condition

NASA Astrophysics Data System (ADS)

Mathias, Simon A.; Wen, Zhang

2015-05-01

This article presents a numerical study to investigate the combined role of partial well penetration (PWP) and non-Darcy effects concerning the performance of groundwater production wells. A finite difference model is developed in MATLAB to solve the two-dimensional mixed-type boundary value problem associated with flow to a partially penetrating well within a cylindrical confined aquifer. Non-Darcy effects are incorporated using the Forchheimer equation. The model is verified by comparison to results from existing semi-analytical solutions concerning the same problem but assuming Darcy's law. A sensitivity analysis is presented to explore the problem of concern. For constant pressure production, Non-Darcy effects lead to a reduction in production rate, as compared to an equivalent problem solved using Darcy's law. For fully penetrating wells, this reduction in production rate becomes less significant with time. However, for partially penetrating wells, the reduction in production rate persists for much larger times. For constant production rate scenarios, the combined effect of PWP and non-Darcy flow takes the form of a constant additional drawdown term. An approximate solution for this loss term is obtained by performing linear regression on the modeling results.

In-line and Real-time Monitoring of Resonant Acoustic Mixing by Near-infrared Spectroscopy Combined with Chemometric Technology for Process Analytical Technology Applications in Pharmaceutical Powder Blending Systems.

PubMed

Tanaka, Ryoma; Takahashi, Naoyuki; Nakamura, Yasuaki; Hattori, Yusuke; Ashizawa, Kazuhide; Otsuka, Makoto

2017-01-01

Resonant acoustic ® mixing (RAM) technology is a system that performs high-speed mixing by vibration through the control of acceleration and frequency. In recent years, real-time process monitoring and prediction has become of increasing interest, and process analytical technology (PAT) systems will be increasingly introduced into actual manufacturing processes. This study examined the application of PAT with the combination of RAM, near-infrared spectroscopy, and chemometric technology as a set of PAT tools for introduction into actual pharmaceutical powder blending processes. Content uniformity was based on a robust partial least squares regression (PLSR) model constructed to manage the RAM configuration parameters and the changing concentration of the components. As a result, real-time monitoring may be possible and could be successfully demonstrated for in-line real-time prediction of active pharmaceutical ingredients and other additives using chemometric technology. This system is expected to be applicable to the RAM method for the risk management of quality.

Neighbourhood walkability, leisure-time and transport-related physical activity in a mixed urban-rural area.

PubMed

de Sa, Eric; Ardern, Chris I

2014-01-01

Objectives. To develop a walkability index specific to mixed rural/suburban areas, and to explore the relationship between walkability scores and leisure time physical activity. Methods. Respondents were geocoded with 500 m and 1,000 m buffer zones around each address. A walkability index was derived from intersections, residential density, and land-use mix according to built environment measures. Multivariable logistic regression models were used to quantify the association between the index and physical activity levels. Analyses used cross-sectional data from the 2007-2008 Canadian Community Health Survey (n = 1158; ≥18 y). Results. Respondents living in highly walkable 500 m buffer zones (upper quartiles of the walkability index) were more likely to walk or cycle for leisure than those living in low-walkable buffer zones (quartile 1). When a 1,000 m buffer zone was applied, respondents in more walkable neighbourhoods were more likely to walk or cycle for both leisure-time and transport-related purposes. Conclusion. Developing a walkability index can assist in exploring the associations between measures of the built environment and physical activity to prioritize neighborhood change.

Finite mixture models for the computation of isotope ratios in mixed isotopic samples

NASA Astrophysics Data System (ADS)

Koffler, Daniel; Laaha, Gregor; Leisch, Friedrich; Kappel, Stefanie; Prohaska, Thomas

2013-04-01

Finite mixture models have been used for more than 100 years, but have seen a real boost in popularity over the last two decades due to the tremendous increase in available computing power. The areas of application of mixture models range from biology and medicine to physics, economics and marketing. These models can be applied to data where observations originate from various groups and where group affiliations are not known, as is the case for multiple isotope ratios present in mixed isotopic samples. Recently, the potential of finite mixture models for the computation of 235U/238U isotope ratios from transient signals measured in individual (sub-)µm-sized particles by laser ablation - multi-collector - inductively coupled plasma mass spectrometry (LA-MC-ICPMS) was demonstrated by Kappel et al. [1]. The particles, which were deposited on the same substrate, were certified with respect to their isotopic compositions. Here, we focus on the statistical model and its application to isotope data in ecogeochemistry. Commonly applied evaluation approaches for mixed isotopic samples are time-consuming and are dependent on the judgement of the analyst. Thus, isotopic compositions may be overlooked due to the presence of more dominant constituents. Evaluation using finite mixture models can be accomplished unsupervised and automatically. The models try to fit several linear models (regression lines) to subgroups of data taking the respective slope as estimation for the isotope ratio. The finite mixture models are parameterised by: • The number of different ratios. • Number of points belonging to each ratio-group. • The ratios (i.e. slopes) of each group. Fitting of the parameters is done by maximising the log-likelihood function using an iterative expectation-maximisation (EM) algorithm. In each iteration step, groups of size smaller than a control parameter are dropped; thereby the number of different ratios is determined. The analyst only influences some control parameters of the algorithm, i.e. the maximum count of ratios, the minimum relative group-size of data points belonging to each ratio has to be defined. Computation of the models can be done with statistical software. In this study Leisch and Grün's flexmix package [2] for the statistical open-source software R was applied. A code example is available in the electronic supplementary material of Kappel et al. [1]. In order to demonstrate the usefulness of finite mixture models in fields dealing with the computation of multiple isotope ratios in mixed samples, a transparent example based on simulated data is presented and problems regarding small group-sizes are illustrated. In addition, the application of finite mixture models to isotope ratio data measured in uranium oxide particles is shown. The results indicate that finite mixture models perform well in computing isotope ratios relative to traditional estimation procedures and can be recommended for more objective and straightforward calculation of isotope ratios in geochemistry than it is current practice. [1] S. Kappel, S. Boulyga, L. Dorta, D. Günther, B. Hattendorf, D. Koffler, G. Laaha, F. Leisch and T. Prohaska: Evaluation Strategies for Isotope Ratio Measurements of Single Particles by LA-MC-ICPMS, Analytical and Bioanalytical Chemistry, 2013, accepted for publication on 2012-12-18 (doi: 10.1007/s00216-012-6674-3) [2] B. Grün and F. Leisch: Fitting finite mixtures of generalized linear regressions in R. Computational Statistics & Data Analysis, 51(11), 5247-5252, 2007. (doi:10.1016/j.csda.2006.08.014)

Estimation of Chinese surface NO2 concentrations combining satellite data and Land Use Regression

NASA Astrophysics Data System (ADS)

Anand, J.; Monks, P.

2016-12-01

Monitoring surface-level air quality is often limited by in-situ instrument placement and issues arising from harmonisation over long timescales. Satellite instruments can offer a synoptic view of regional pollution sources, but in many cases only a total or tropospheric column can be measured. In this work a new technique of estimating surface NO2 combining both satellite and in-situ data is presented, in which a Land Use Regression (LUR) model is used to create high resolution pollution maps based on known predictor variables such as population density, road networks, and land cover. By employing a mixed effects approach, it is possible to take advantage of the spatiotemporal variability in the satellite-derived column densities to account for daily and regional variations in surface NO2 caused by factors such as temperature, elevation, and wind advection. In this work, surface NO2 maps are modelled over the North China Plain and Pearl River Delta during high-pollution episodes by combining in-situ measurements and tropospheric columns from the Ozone Monitoring Instrument (OMI). The modelled concentrations show good agreement with in-situ data and surface NO2 concentrations derived from the MACC-II global reanalysis.

Toward a Better Understanding of Student Perceptions of Writing Feedback: A Mixed Methods Study

ERIC Educational Resources Information Center

Zumbrunn, Sharon; Marrs, Sarah; Mewborn, Caitlin

2016-01-01

This explanatory sequential mixed methods study investigated the writing feedback perceptions of middle and high school students (N = 598). The predictive and mediational roles of writing self-efficacy and perceptions of writing feedback on student writing self-regulation aptitude were examined using mediation regression analysis. To augment the…

Campaign Strategies and Voter Approval of School Referenda: A Mixed Methods Analysis

ERIC Educational Resources Information Center

Johnson, Paul A.; Ingle, William Kyle

2009-01-01

Drawing from state administrative data and surveys of superintendents in Ohio, this mixed methods study examined factors associated with voters' approval of local school levies. Utilizing binomial logistic regression, this study found that new levies and poverty rates were significantly associated with a decrease in the likelihood of passage.…

Collaborative Project. 3D Radiative Transfer Parameterization Over Mountains/Snow for High-Resolution Climate Models. Fast physics and Applications

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

Liou, Kuo-Nan

2016-02-09

Under the support of the aforementioned DOE Grant, we have made two fundamental contributions to atmospheric and climate sciences: (1) Develop an efficient 3-D radiative transfer parameterization for application to intense and intricate inhomogeneous mountain/snow regions. (2) Innovate a stochastic parameterization for light absorption by internally mixed black carbon and dust particles in snow grains for understanding and physical insight into snow albedo reduction in climate models. With reference to item (1), we divided solar fluxes reaching mountain surfaces into five components: direct and diffuse fluxes, direct- and diffuse-reflected fluxes, and coupled mountain-mountain flux. “Exact” 3D Monte Carlo photon tracingmore » computations can then be performed for these solar flux components to compare with those calculated from the conventional plane-parallel (PP) radiative transfer program readily available in climate models. Subsequently, Parameterizations of the deviations of 3D from PP results for five flux components are carried out by means of the multiple linear regression analysis associated with topographic information, including elevation, solar incident angle, sky view factor, and terrain configuration factor. We derived five regression equations with high statistical correlations for flux deviations and successfully incorporated this efficient parameterization into WRF model, which was used as the testbed in connection with the Fu-Liou-Gu PP radiation scheme that has been included in the WRF physics package. Incorporating this 3D parameterization program, we conducted simulations of WRF and CCSM4 to understand and evaluate the mountain/snow effect on snow albedo reduction during seasonal transition and the interannual variability for snowmelt, cloud cover, and precipitation over the Western United States presented in the final report. With reference to item (2), we developed in our previous research a geometric-optics surface-wave approach (GOS) for the computation of light absorption and scattering by complex and inhomogeneous particles for application to aggregates and snow grains with external and internal mixing structures. We demonstrated that a small black (BC) particle on the order of 1 μm internally mixed with snow grains could effectively reduce visible snow albedo by as much as 5–10%. Following this work and within the context of DOE support, we have made two key accomplishments presented in the attached final report.« less

Anodic microbial community diversity as a predictor of the power output of microbial fuel cells.

PubMed

Stratford, James P; Beecroft, Nelli J; Slade, Robert C T; Grüning, André; Avignone-Rossa, Claudio

2014-03-01

The relationship between the diversity of mixed-species microbial consortia and their electrogenic potential in the anodes of microbial fuel cells was examined using different diversity measures as predictors. Identical microbial fuel cells were sampled at multiple time-points. Biofilm and suspension communities were analysed by denaturing gradient gel electrophoresis to calculate the number and relative abundance of species. Shannon and Simpson indices and richness were examined for association with power using bivariate and multiple linear regression, with biofilm DNA as an additional variable. In simple bivariate regressions, the correlation of Shannon diversity of the biofilm and power is stronger (r=0.65, p=0.001) than between power and richness (r=0.39, p=0.076), or between power and the Simpson index (r=0.5, p=0.018). Using Shannon diversity and biofilm DNA as predictors of power, a regression model can be constructed (r=0.73, p<0.001). Ecological parameters such as the Shannon index are predictive of the electrogenic potential of microbial communities. Copyright © 2014 Elsevier Ltd. All rights reserved.

No increase in small-solute transport in peritoneal dialysis patients treated without hypertonic glucose for fifty-four months.

PubMed

Pagniez, Dominique; Duhamel, Alain; Boulanger, Eric; Lessore de Sainte Foy, Celia; Beuscart, Jean-Baptiste

2017-08-31

Glucose is widely used as an osmotic agent in peritoneal dialysis (PD), but exerts untoward effects on the peritoneum. The potential protective effect of a reduced exposure to hypertonic glucose has never been investigated. The cohort of PD patients attending our center which tackled the challenge of a restricted use of hypertonic glucose solutions has been prospectively followed since 1992. Small-solute transport was assessed using an equivalent of the glucose peritoneal equilibration test after 6 months, and then every year. Study was stopped on July 1st, 2008, before use of biocompatible solutions. Repeated measures in patients treated with PD for 54 months were analyzed by using (1) the slopes of the linear regression for D 4 /D 0 ratios over time computed for each individual, and (2) a linear mixed model. In the study period, 44 patients were treated for a total of 2376 months, 2058 without hypertonic glucose. There was one episode of peritoneal infection every 18 patient-months. The mean of slopes of the linear regression for D 4 /D 0 ratios was found to be significantly positive (Student's test, p < .001) and the results of the mixed model reflected a similar significant increase for D 4 /D 0 ratios over time. These results reflected a significant decrease of small-solute transport. In this large series, minimizing the use of hypertonic glucose solutions was associated in patients on long term PD with an overall decrease of small-solute transport within 54 months, despite a high rate of peritoneal infection.

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

Keys to Reducing Summer Regression: The Reader, Routine, and Relationship

ERIC Educational Resources Information Center

Blanton, Morgan V.

2015-01-01

This study utilized mixed-methods, quasi-experimental design to investigate the impact of parent development and home-based summer reading on summer reading regression (as measured by oral reading fluency) at three Title I elementary schools in North Carolina. Title I parents and students participated in a parent development and communicated…

A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

NASA Technical Reports Server (NTRS)

Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2014-01-01

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data.

PubMed

Kloog, Itai; Chudnovsky, Alexandra A; Just, Allan C; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2014-10-01

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM 2.5 ) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM 2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM 2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R 2 =0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R 2 =0.87, R 2 =0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

PubMed Central

Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2017-01-01

Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region. PMID:28966552

Dynamics modeling for sugar cane sucrose estimation using time series satellite imagery

NASA Astrophysics Data System (ADS)

Zhao, Yu; Justina, Diego Della; Kazama, Yoriko; Rocha, Jansle Vieira; Graziano, Paulo Sergio; Lamparelli, Rubens Augusto Camargo

2016-10-01

Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.

Constrained inference in mixed-effects models for longitudinal data with application to hearing loss.

PubMed

Davidov, Ori; Rosen, Sophia

2011-04-01

In medical studies, endpoints are often measured for each patient longitudinally. The mixed-effects model has been a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, in hearing loss studies, we expect hearing to deteriorate with time. This means that hearing thresholds which reflect hearing acuity will, on average, increase over time. Therefore, the regression coefficients associated with the mean effect of time on hearing ability will be constrained. Such constraints should be accounted for in the analysis. We propose maximum likelihood estimation procedures, based on the expectation-conditional maximization either algorithm, to estimate the parameters of the model while accounting for the constraints on them. The proposed methods improve, in terms of mean square error, on the unconstrained estimators. In some settings, the improvement may be substantial. Hypotheses testing procedures that incorporate the constraints are developed. Specifically, likelihood ratio, Wald, and score tests are proposed and investigated. Their empirical significance levels and power are studied using simulations. It is shown that incorporating the constraints improves the mean squared error of the estimates and the power of the tests. These improvements may be substantial. The methodology is used to analyze a hearing loss study.

Maximal bite force, facial morphology and sucking habits in young children with functional posterior crossbite.

PubMed

Castelo, Paula Midori; Gavião, Maria Beatriz Duarte; Pereira, Luciano José; Bonjardim, Leonardo Rigoldi

2010-01-01

The maintenance of normal conditions of the masticatory function is determinant for the correct growth and development of its structures. Thus, the aims of this study were to evaluate the influence of sucking habits on the presence of crossbite and its relationship with maximal bite force, facial morphology and body variables in 67 children of both genders (3.5-7 years) with primary or early mixed dentition. The children were divided in four groups: primary-normocclusion (PN, n=19), primary-crossbite (PC, n=19), mixed-normocclusion (MN, n=13), and mixed-crossbite (MC, n=16). Bite force was measured with a pressurized tube, and facial morphology was determined by standardized frontal photographs: AFH (anterior face height) and BFW (bizygomatic facial width). It was observed that MC group showed lower bite force than MN, and AFH/BFW was significantly smaller in PN than PC (t-test). Weight and height were only significantly correlated with bite force in PC group (Pearson's correlation test). In the primary dentition, AFH/BFW and breast-feeding (at least six months) were positive and negatively associated with crossbite, respectively (multiple logistic regression). In the mixed dentition, breast-feeding and bite force showed negative associations with crossbite (univariate regression), while nonnutritive sucking (up to 3 years) associated significantly with crossbite in all groups (multiple logistic regression). In the studied sample, sucking habits played an important role in the etiology of crossbite, which was associated with lower bite force and long-face tendency.

Subjective Social Status and Self-Reported Health Among US-born and Immigrant Latinos.

PubMed

Garza, Jeremiah R; Glenn, Beth A; Mistry, Rashmita S; Ponce, Ninez A; Zimmerman, Frederick J

2017-02-01

Subjective social status is associated with a range of health outcomes. Few studies have tested the relevance of subjective social status among Latinos in the U.S.; those that have yielded mixed results. Data come from the Latino subsample of the 2003 National Latino and Asian American Study (N = 2554). Regression models adjusted for socioeconomic and demographic factors. Stratified analyses tested whether nativity status modifies the effect of subjective social status on health. Subjective social status was associated with better health. Income and education mattered more for health than subjective social status among U.S.-born Latinos. However, the picture was mixed among immigrant Latinos, with subjective social status more strongly predictive than income but less so than education. Subjective social status may tap into stressful immigrant experiences that affect one's perceived self-worth and capture psychosocial consequences and social disadvantage left out by conventional socioeconomic measures.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

PubMed

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei

2016-02-01

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. © 2016 WILEY PERIODICALS, INC.

Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

PubMed Central

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E.; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y.; Chen, Wei

2015-01-01

Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979

[Application of artificial neural networks on the prediction of surface ozone concentrations].

PubMed

Shen, Lu-Lu; Wang, Yu-Xuan; Duan, Lei

2011-08-01

Ozone is an important secondary air pollutant in the lower atmosphere. In order to predict the hourly maximum ozone one day in advance based on the meteorological variables for the Wanqingsha site in Guangzhou, Guangdong province, a neural network model (Multi-Layer Perceptron) and a multiple linear regression model were used and compared. Model inputs are meteorological parameters (wind speed, wind direction, air temperature, relative humidity, barometric pressure and solar radiation) of the next day and hourly maximum ozone concentration of the previous day. The OBS (optimal brain surgeon) was adopted to prune the neutral work, to reduce its complexity and to improve its generalization ability. We find that the pruned neural network has the capacity to predict the peak ozone, with an agreement index of 92.3%, the root mean square error of 0.0428 mg/m3, the R-square of 0.737 and the success index of threshold exceedance 77.0% (the threshold O3 mixing ratio of 0.20 mg/m3). When the neural classifier was added to the neural network model, the success index of threshold exceedance increased to 83.6%. Through comparison of the performance indices between the multiple linear regression model and the neural network model, we conclud that that neural network is a better choice to predict peak ozone from meteorological forecast, which may be applied to practical prediction of ozone concentration.

Equilibrium Climate Sensitivity Obtained From Multimillennial Runs of Two GFDL Climate Models

NASA Astrophysics Data System (ADS)

Paynter, D.; Frölicher, T. L.; Horowitz, L. W.; Silvers, L. G.

2018-02-01

Equilibrium climate sensitivity (ECS), defined as the long-term change in global mean surface air temperature in response to doubling atmospheric CO2, is usually computed from short atmospheric simulations over a mixed layer ocean, or inferred using a linear regression over a short-time period of adjustment. We report the actual ECS from multimillenial simulations of two Geophysical Fluid Dynamics Laboratory (GFDL) general circulation models (GCMs), ESM2M, and CM3 of 3.3 K and 4.8 K, respectively. Both values are 1 K higher than estimates for the same models reported in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change obtained by regressing the Earth's energy imbalance against temperature. This underestimate is mainly due to changes in the climate feedback parameter (-α) within the first century after atmospheric CO2 has stabilized. For both GCMs it is possible to estimate ECS with linear regression to within 0.3 K by increasing CO2 at 1% per year to doubling and using years 51-350 after CO2 is constant. We show that changes in -α differ between the two GCMs and are strongly tied to the changes in both vertical velocity at 500 hPa (ω500) and estimated inversion strength that the GCMs experience during the progression toward the equilibrium. This suggests that while cloud physics parametrizations are important for determining the strength of -α, the substantially different atmospheric state resulting from a changed sea surface temperature pattern may be of equal importance.

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate which Fires Produce Ozone

NASA Technical Reports Server (NTRS)

Chatfield, Robert B.; Andreae, Meinrat O.

2016-01-01

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(burn) = delta C(tot) added to the fire plume, where C(tot) approximately equals (CO2 = CO). Mixed-effects regression can estimate pre-fire background values of C(tot) (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta, epsilon lambda tau alpha-x(sub I)/C(sub burn))I,j. MERET and "consensus" require more than emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (b scant, b abs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate Which Fires Produce Ozone

NASA Technical Reports Server (NTRS)

Chatfield, Robert B.; Andreae, Meinrat O.

2015-01-01

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(sub burn) = delta C(sub tot) added to the fire plume, where C(sub tot) approximately equals (CO2 + CO). Mixed-effects regression can estimate pre-fire background values of Ctot (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta epsilon lambda tau alpha-x(sub i)/(C(sub burn))i,j., MERET and "consensus" require more than two emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (bscat, babs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics.

A Skew-t space-varying regression model for the spectral analysis of resting state brain activity.

PubMed

Ismail, Salimah; Sun, Wenqi; Nathoo, Farouk S; Babul, Arif; Moiseev, Alexader; Beg, Mirza Faisal; Virji-Babul, Naznin

2013-08-01

It is known that in many neurological disorders such as Down syndrome, main brain rhythms shift their frequencies slightly, and characterizing the spatial distribution of these shifts is of interest. This article reports on the development of a Skew-t mixed model for the spatial analysis of resting state brain activity in healthy controls and individuals with Down syndrome. Time series of oscillatory brain activity are recorded using magnetoencephalography, and spectral summaries are examined at multiple sensor locations across the scalp. We focus on the mean frequency of the power spectral density, and use space-varying regression to examine associations with age, gender and Down syndrome across several scalp regions. Spatial smoothing priors are incorporated based on a multivariate Markov random field, and the markedly non-Gaussian nature of the spectral response variable is accommodated by the use of a Skew-t distribution. A range of models representing different assumptions on the association structure and response distribution are examined, and we conduct model selection using the deviance information criterion. (1) Our analysis suggests region-specific differences between healthy controls and individuals with Down syndrome, particularly in the left and right temporal regions, and produces smoothed maps indicating the scalp topography of the estimated differences.

Dough performance, quality and shelf life of flat bread supplemented with fractions of germinated date seed.

PubMed

Hejri-Zarifi, Sudiyeh; Ahmadian-Kouchaksaraei, Zahra; Pourfarzad, Amir; Khodaparast, Mohammad Hossein Haddad

2014-12-01

Germinated palm date seeds were milled into two fractions: germ and residue. Dough rheological characteristics, baking (specific volume and sensory evaluation), and textural properties (at first day and during storage for 5 days) were determined in Barbari flat bread. Germ and residue fractions were incorporated at various levels ranged in 0.5-3 g/100 g of wheat flour. Water absorption, arrival time and gelatination temperature were decreased by germ fraction but accompanied by an increasing effect on the mixing tolerance index and degree of softening in most levels. Although improvement in dough stability was monitored but specific volume of bread was not affected by both fractions. Texture analysis of bread samples during 5 days of storage indicated that both fractions of germinated date seeds were able to diminish bread staling. Avrami non-linear regression equation was chosen as useful mathematical model to properly study bread hardening kinetics. In addition, principal component analysis (PCA) allowed discriminating among dough and bread specialties. Partial least squares regression (PLSR) models were applied to determine the relationships between sensory and instrumental data.

A CONCISE PANEL OF BIOMARKERS IDENTIFIES NEUROCOGNITIVE FUNCTIONING CHANGES IN HIV-INFECTED INDIVIDUALS

PubMed Central

Marcotte, Thomas D.; Deutsch, Reena; Michael, Benedict Daniel; Franklin, Donald; Cookson, Debra Rosario; Bharti, Ajay R.; Grant, Igor; Letendre, Scott L.

2013-01-01

Background Neurocognitive (NC) impairment (NCI) occurs commonly in people living with HIV. Despite substantial effort, no biomarkers have been sufficiently validated for diagnosis and prognosis of NCI in the clinic. The goal of this project was to identify diagnostic or prognostic biomarkers for NCI in a comprehensively characterized HIV cohort. Methods Multidisciplinary case review selected 98 HIV-infected individuals and categorized them into four NC groups using normative data: stably normal (SN), stably impaired (SI), worsening (Wo), or improving (Im). All subjects underwent comprehensive NC testing, phlebotomy, and lumbar puncture at two timepoints separated by a median of 6.2 months. Eight biomarkers were measured in CSF and blood by immunoassay. Results were analyzed using mixed model linear regression and staged recursive partitioning. Results At the first visit, subjects were mostly middle-aged (median 45) white (58%) men (84%) who had AIDS (70%). Of the 73% who took antiretroviral therapy (ART), 54% had HIV RNA levels below 50 c/mL in plasma. Mixed model linear regression identified that only MCP-1 in CSF was associated with neurocognitive change group. Recursive partitioning models aimed at diagnosis (i.e., correctly classifying neurocognitive status at the first visit) were complex and required most biomarkers to achieve misclassification limits. In contrast, prognostic models were more efficient. A combination of three biomarkers (sCD14, MCP-1, SDF-1α) correctly classified 82% of Wo and SN subjects, including 88% of SN subjects. A combination of two biomarkers (MCP-1, TNF-α) correctly classified 81% of Im and SI subjects, including 100% of SI subjects. Conclusions This analysis of well-characterized individuals identified concise panels of biomarkers associated with NC change. Across all analyses, the two most frequently identified biomarkers were sCD14 and MCP-1, indicators of monocyte/macrophage activation. While the panels differed depending on the outcome and on the degree of misclassification, nearly all stable patients were correctly classified. PMID:24101401

On-road heavy-duty diesel particulate matter emissions modeled using chassis dynamometer data.

PubMed

Kear, Tom; Niemeier, D A

2006-12-15

This study presents a model, derived from chassis dynamometer test data, for factors (operational correction factors, or OCFs) that correct (g/mi) heavy-duty diesel particle emission rates measured on standard test cycles for real-world conditions. Using a random effects mixed regression model with data from 531 tests of 34 heavy-duty vehicles from the Coordinating Research Council's E55/E59 research project, we specify a model with covariates that characterize high power transient driving, time spent idling, and average speed. Gram per mile particle emissions rates were negatively correlated with high power transient driving, average speed, and time idling. The new model is capable of predicting relative changes in g/mi on-road heavy-duty diesel particle emission rates for real-world driving conditions that are not reflected in the driving cycles used to test heavy-duty vehicles.

Wood volume increment in thinned, 50- to 55-year-old, mixed-species Allegheny hardwoods

Treesearch

Christopher A. Nowak

1996-01-01

A thinning study in 50- to 55-year-old, even-aged, mixed species Allegheny hardwoods produced highly variable merchantable stemwood volume increment responses. Regression equations relating parameters of stand growth (ingrowth, mortality, survivor growth, net growth, and gross growth) to relative stand density had R2 values ranging from 0.07 to 0...

A Mixed-Methods Study Investigating the Relationship between Media Multitasking Orientation and Grade Point Average

ERIC Educational Resources Information Center

Lee, Jennifer

2012-01-01

The intent of this study was to examine the relationship between media multitasking orientation and grade point average. The study utilized a mixed-methods approach to investigate the research questions. In the quantitative section of the study, the primary method of statistical analyses was multiple regression. The independent variables for the…

A Mixed-Methods Inquiry into the Intimate Practices of Partnered Mature Students and Influences on Relationship, Sexual, and School Satisfaction

ERIC Educational Resources Information Center

van Rhijn, Tricia M.; Murray, Sarah H.; Mizzi, Robert C.

2015-01-01

Through the use of mixed qualitative and quantitative methods, the current study explored the impact of postsecondary study on the intimate relationships and school experiences of partnered mature students. Quantitative regression analyses indicated that parental status, family support, partner support, and sexual desire significantly predicted…

Maximum size-density relationships for mixed-hardwood forest stands in New England

Treesearch

Dale S. Solomon; Lianjun Zhang

2000-01-01

Maximum size-density relationships were investigated for two mixed-hardwood ecological types (sugar maple-ash and beech-red maple) in New England. Plots meeting type criteria and undergoing self-thinning were selected for each habitat. Using reduced major axis regression, no differences were found between the two ecological types. Pure species plots (the species basal...

A model for predicting thermal properties of asphalt mixtures from their constituents

NASA Astrophysics Data System (ADS)

Keller, Merlin; Roche, Alexis; Lavielle, Marc

Numerous theoretical and experimental approaches have been developed to predict the effective thermal conductivity of composite materials such as polymers, foams, epoxies, soils and concrete. None of such models have been applied to asphalt concrete. This study attempts to develop a model to predict the thermal conductivity of asphalt concrete from its constituents that will contribute to the asphalt industry by reducing costs and saving time on laboratory testing. The necessity to do the laboratory testing would be no longer required when a mix for the pavement is created with desired thermal properties at the design stage by selecting correct constituents. This thesis investigated six existing predictive models for applicability to asphalt mixtures, and four standard mathematical techniques were used to develop a regression model to predict the effective thermal conductivity. The effective thermal conductivities of 81 asphalt specimens were used as the response variables, and the thermal conductivities and volume fractions of their constituents were used as the predictors. The conducted statistical analyses showed that the measured values of thermal conductivities of the mixtures are affected by the bitumen and aggregate content, but not by the air content. Contrarily, the predicted data for some investigated models are highly sensitive to air voids, but not to bitumen and/or aggregate content. Additionally, the comparison of the experimental with analytical data showed that none of the existing models gave satisfactory results; on the other hand, two regression models (Exponential 1* and Linear 3*) are promising for asphalt concrete.

Anticonvulsants and suicide attempts in bipolar I disorders.

PubMed

Bellivier, F; Belzeaux, R; Scott, J; Courtet, P; Golmard, J-L; Azorin, J-M

2017-05-01

To identify risk factors for suicide attempts (SA) in individuals commencing treatment for a manic or mixed episode. A total of 3390 manic or mixed cases with bipolar disorder (BD) type I recruited from 14 European countries were included in a prospective, 2-year observational study. Poisson regression models were used to identify individual and treatment factors associated with new SA events. Two multivariate models were built, stratified for the presence or absence of prior SA. A total of 302 SA were recorded prospectively; the peak incidence was 0-12 weeks after commencing treatment. In cases with a prior history of SA, risk of SA repetition was associated with younger age of first manic episode (P = 0.03), rapid cycling (P < 0.001), history of alcohol and/or substance use disorder (P < 0.001), number of psychotropic drugs prescribed (P < 0.001) and initiation of an anticonvulsant at study entry (P < 0.001). In cases with no previous SA, the first SA event was associated with rapid cycling (P = 0.02), lifetime history of alcohol use disorder (P = 0.02) and initiation of an anticonvulsant at study entry (P = 0.002). The introduction of anticonvulsants for a recent-onset manic or mixed episode may be associated with an increased risk of SA. Further BD studies must determine whether this link is causal. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Distribution, occupancy, and habitat correlates of American martens (Martes americana) in Rocky Mountain National Park, Colorado

USGS Publications Warehouse

Baldwin, R.A.; Bender, L.C.

2008-01-01

A clear understanding of habitat associations of martens (Martes americana) is necessary to effectively manage and monitor populations. However, this information was lacking for martens in most of their southern range, particularly during the summer season. We studied the distribution and habitat correlates of martens from 2004 to 2006 in Rocky Mountain National Park (RMNP) across 3 spatial scales: site-specific, home-range, and landscape. We used remote-sensored cameras from early August through late October to inventory occurrence of martens and modeled occurrence as a function of habitat and landscape variables using binary response (BR) and binomial count (BC) logistic regression, and occupancy modeling (OM). We also assessed which was the most appropriate modeling technique for martens in RMNP. Of the 3 modeling techniques, OM appeared to be most appropriate given the explanatory power of derived models and its incorporation of detection probabilities, although the results from BR and BC provided corroborating evidence of important habitat correlates. Location of sites in the western portion of the park, riparian mixed-conifer stands, and mixed-conifer with aspen patches were most frequently positively correlated with occurrence of martens, whereas more xeric and open sites were avoided. Additionally, OM yielded unbiased occupancy values ranging from 91% to 100% and 20% to 30% for the western and eastern portions of RMNP, respectively. ?? 2008 American Society of Mammalogists.

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

Restructuring in response to case mix reimbursement in nursing homes: A contingency approach

PubMed Central

Zinn, Jacqueline; Feng, Zhanlian; Mor, Vincent; Intrator, Orna; Grabowski, David

2013-01-01

Background Resident-based case mix reimbursement has become the dominant mechanism for publicly funded nursing home care. In 1998 skilled nursing facility reimbursement changed from cost-based to case mix adjusted payments under the Medicare Prospective Payment System for the costs of all skilled nursing facility care provided to Medicare recipients. In addition, as of 2004, 35 state Medicaid programs had implemented some form of case mix reimbursement. Purpose The purpose of the study is to determine if the implementation of Medicare and Medicaid case mix reimbursement increased the administrative burden on nursing homes, as evidenced by increased levels of nurses in administrative functions. Methodology/Approach The primary data for this study come from the Centers for Medicare and Medicaid Services Online Survey Certification and Reporting database from 1997 through 2004, a national nursing home database containing aggregated facility-level information, including staffing, organizational characteristics and resident conditions, on all Medicare/Medicaid certified nursing facilities in the country. We conducted multivariate regression analyses using a facility fixed-effects model to examine the effects of the implementation of Medicaid case mix reimbursement and Medicare Prospective Payment System on changes in the level of total administrative nurse staffing in nursing homes. Findings Both Medicaid case mix reimbursement and Medicare Prospective Payment System increased the level of administrative nurse staffing, on average by 5.5% and 4.0% respectively. However, lack of evidence for a substitution effect suggests that any decline in direct care staffing after the introduction of case mix reimbursement is not attributable to a shift from clinical nursing resources to administrative functions. Practice Implications Our findings indicate that the administrative burden posed by case mix reimbursement has resource implications for all freestanding facilities. At the margin, the increased administrative burden imposed by case mix may become a factor influencing a range of decisions, including resident admission and staff hiring. PMID:18360162

Restructuring in response to case mix reimbursement in nursing homes: a contingency approach.

PubMed

Zinn, Jacqueline; Feng, Zhanlian; Mor, Vincent; Intrator, Orna; Grabowski, David

2008-01-01

Resident-based case mix reimbursement has become the dominant mechanism for publicly funded nursing home care. In 1998 skilled nursing facility reimbursement changed from cost-based to case mix adjusted payments under the Medicare Prospective Payment System for the costs of all skilled nursing facility care provided to Medicare recipients. In addition, as of 2004, 35 state Medicaid programs had implemented some form of case mix reimbursement. The purpose of the study is to determine if the implementation of Medicare and Medicaid case mix reimbursement increased the administrative burden on nursing homes, as evidenced by increased levels of nurses in administrative functions. The primary data for this study come from the Centers for Medicare and Medicaid Services Online Survey Certification and Reporting database from 1997 through 2004, a national nursing home database containing aggregated facility-level information, including staffing, organizational characteristics and resident conditions, on all Medicare/Medicaid certified nursing facilities in the country. We conducted multivariate regression analyses using a facility fixed-effects model to examine the effects of the implementation of Medicaid case mix reimbursement and Medicare Prospective Payment System on changes in the level of total administrative nurse staffing in nursing homes. Both Medicaid case mix reimbursement and Medicare Prospective Payment System increased the level of administrative nurse staffing, on average by 5.5% and 4.0% respectively. However, lack of evidence for a substitution effect suggests that any decline in direct care staffing after the introduction of case mix reimbursement is not attributable to a shift from clinical nursing resources to administrative functions. Our findings indicate that the administrative burden posed by case mix reimbursement has resource implications for all freestanding facilities. At the margin, the increased administrative burden imposed by case mix may become a factor influencing a range of decisions, including resident admission and staff hiring.

Direct vs. Expressed Breast Milk Feeding: Relation to Duration of Breastfeeding.

PubMed

Pang, Wei Wei; Bernard, Jonathan Y; Thavamani, Geetha; Chan, Yiong Huak; Fok, Doris; Soh, Shu-E; Chua, Mei Chien; Lim, Sok Bee; Shek, Lynette P; Yap, Fabian; Tan, Kok Hian; Gluckman, Peter D; Godfrey, Keith M; van Dam, Rob M; Kramer, Michael S; Chong, Yap-Seng

2017-05-27

Studies examining direct vs. expressed breast milk feeding are scarce. We explored the predictors of mode of breastfeeding and its association with breastfeeding duration in a multi-ethnic Asian population. We included 541 breastfeeding mother-infant pairs from the Growing Up in Singapore Toward healthy Outcomes cohort. Mode of breastfeeding (feeding directly at the breast, expressed breast milk (EBM) feeding only, or mixed feeding (a combination of the former 2 modes)) was ascertained at three months postpartum. Ordinal logistic regression analyses identified predictors of breast milk expression. Cox regression models examined the association between mode of breastfeeding and duration of any and of full breastfeeding. Maternal factors independently associated with a greater likelihood of breast milk expression instead of direct breastfeeding were Chinese (vs. Indian) ethnicity, (adjusted odds ratio, 95% CI; 3.41, 1.97-5.91), tertiary education (vs. secondary education or lower) (2.22, 1.22-4.04), primiparity (1.54, 1.04-2.26) and employment during pregnancy (2.53, 1.60-4.02). Relative to those who fed their infants directly at the breast, mothers who fed their infants EBM only had a higher likelihood of early weaning among all mothers who were breastfeeding (adjusted hazard ratio, 95% CI; 2.20, 1.61-3.02), and among those who were fully breastfeeding (2.39, 1.05-5.41). Mothers who practiced mixed feeding, however, were not at higher risk of earlier termination of any or of full breastfeeding. Mothers who fed their infants EBM exclusively, but not those who practiced mixed feeding, were at a higher risk of terminating breastfeeding earlier than those who fed their infants directly at the breast. More education and support are required for women who feed their infants EBM only.

Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, G.; Yule, I. J.

2017-06-01

New Zealand farming relies heavily on grazed pasture for feeding livestock; therefore it is important to provide high quality palatable grass in order to maintain profitable and sustainable grassland management. The presence of non-photosynthetic vegetation (NPV) such as dead vegetation in pastures severely limits the quality and productivity of pastures. Quantifying the fraction of dead vegetation in mixed pastures is a great challenge even with remote sensing approaches. In this study, a high spatial resolution with pixel resolution of 1 m and spectral resolution of 3.5-5.6 nm imaging spectroscopy data from AisaFENIX (380-2500 nm) was used to assess the fraction of dead vegetation component in mixed pastures on a hill country farm in New Zealand. We used different methods to retrieve dead vegetation fraction from the spectra; narrow band vegetation indices, full spectrum based partial least squares (PLS) regression and feature selection based PLS regression. Among all approaches, feature selection based PLS model exhibited better performance in terms of prediction accuracy (R2CV = 0.73, RMSECV = 6.05, RPDCV = 2.25). The results were consistent with validation data, and also performed well on the external test data (R2 = 0.62, RMSE = 8.06, RPD = 2.06). In addition, statistical tests were conducted to ascertain the effect of topographical variables such as slope and aspect on the accumulation of the dead vegetation fraction. Steep slopes (>25°) had a significantly (p < 0.05) higher amount of dead vegetation. In contrast, aspect showed non-significant impact on dead vegetation accumulation. The results from the study indicate that AisaFENIX imaging spectroscopy data could be a useful tool for mapping the dead vegetation fraction accurately.

Comparing methods of measuring geographic patterns in temporal trends: an application to county-level heart disease mortality in the United States, 1973 to 2010.

PubMed

Vaughan, Adam S; Kramer, Michael R; Waller, Lance A; Schieb, Linda J; Greer, Sophia; Casper, Michele

2015-05-01

To demonstrate the implications of choosing analytical methods for quantifying spatiotemporal trends, we compare the assumptions, implementation, and outcomes of popular methods using county-level heart disease mortality in the United States between 1973 and 2010. We applied four regression-based approaches (joinpoint regression, both aspatial and spatial generalized linear mixed models, and Bayesian space-time model) and compared resulting inferences for geographic patterns of local estimates of annual percent change and associated uncertainty. The average local percent change in heart disease mortality from each method was -4.5%, with the Bayesian model having the smallest range of values. The associated uncertainty in percent change differed markedly across the methods, with the Bayesian space-time model producing the narrowest range of variance (0.0-0.8). The geographic pattern of percent change was consistent across methods with smaller declines in the South Central United States and larger declines in the Northeast and Midwest. However, the geographic patterns of uncertainty differed markedly between methods. The similarity of results, including geographic patterns, for magnitude of percent change across these methods validates the underlying spatial pattern of declines in heart disease mortality. However, marked differences in degree of uncertainty indicate that Bayesian modeling offers substantially more precise estimates. Copyright © 2015 Elsevier Inc. All rights reserved.

Random regression analysis for body weights and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

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

2018-02-01

To genetically analyse growth traits in genetically improved farmed tilapia (GIFT), the body weight (BWE) and main morphological traits, including body length (BL), body depth (BD), body width (BWI), head length (HL) and length of the caudal peduncle (CPL), were measured six times in growth duration on 1451 fish from 45 mixed families of full and half sibs. A random regression model (RRM) was used to model genetic changes of the growth traits with days of age and estimate the heritability for any growth point and genetic correlations between pairwise growth points. Using the covariance function based on optimal RRMs, the heritabilities were estimated to be from 0.102 to 0.662 for BWE, 0.157 to 0.591 for BL, 0.047 to 0.621 for BD, 0.018 to 0.577 for BWI, 0.075 to 0.597 for HL and 0.032 to 0.610 for CPL between 60 and 140 days of age. All genetic correlations exceeded 0.5 between pairwise growth points. Moreover, the traits at initial days of age showed less correlation with those at later days of age. With phenotypes observed repeatedly, the model choice showed that the optimal RRMs could more precisely predict breeding values at a specific growth time than repeatability models or multiple trait animal models, which enhanced the efficiency of selection for the BWE and main morphological traits.

Determinants of isocyanate exposures in auto body repair and refinishing shops.

PubMed

Woskie, S R; Sparer, J; Gore, R J; Stowe, M; Bello, D; Liu, Y; Youngs, F; Redlich, C; Eisen, E; Cullen, M

2004-07-01

As part of the Survey of Painters and Repairers of Auto bodies by Yale (SPRAY), the determinants of isocyanate exposure in auto body repair shops were evaluated. Measurements (n = 380) of hexamethylene diisocyanate-based monomer and polyisocyanate and isophorone diisocyanate-based polyisocyanate were collected from 33 auto body shops. The median total reactive isocyanate concentrations expressed as mass concentration of the NCO functional group were: 206 microg NCO/m3 for spray operations; 0.93 microg NCO/m3 for samples collected in the vicinity of spray operations done on the shop floor (near spray); 0.05 microg NCO/m3 for office or other shop areas adjacent to spray areas (workplace background); 0.17 microg NCO/m3 for paint mixing and gun cleaning operations (mixing); 0.27 microg NCO/m3 for sanding operations. Exposure determinants for the sample NCO mass load were identified using linear regression, tobit regression and logistic regression models. For spray samples in a spray booth the significant determinants were the number of milliliters of NCO applied, the gallons of clear coat used by the shop each month and the type of spray booth used (custom built crossdraft, prefabricated crossdraft or downdraft/semi-downdraft). For near spray (bystander) samples, outdoor temperature >65 degrees F (18 degrees C) and shop size >5000 feet2 (465 m2) were significant determinants of exposure levels. For workplace background samples the shop annual income was the most important determinant. For sanding samples, the shop annual income and outdoor temperature >65 degrees F (18 degrees C) were the most significant determinants. Identification of these key exposure determinants will be useful in targeting exposure evaluation and control efforts to reduce isocyanate exposures.

surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials.

PubMed

Rotolo, Federico; Paoletti, Xavier; Michiels, Stefan

2018-03-01

Surrogate endpoints are attractive for use in clinical trials instead of well-established endpoints because of practical convenience. To validate a surrogate endpoint, two important measures can be estimated in a meta-analytic context when individual patient data are available: the R indiv 2 or the Kendall's τ at the individual level, and the R trial 2 at the trial level. We aimed at providing an R implementation of classical and well-established as well as more recent statistical methods for surrogacy assessment with failure time endpoints. We also intended incorporating utilities for model checking and visualization and data generating methods described in the literature to date. In the case of failure time endpoints, the classical approach is based on two steps. First, a Kendall's τ is estimated as measure of individual level surrogacy using a copula model. Then, the R trial 2 is computed via a linear regression of the estimated treatment effects; at this second step, the estimation uncertainty can be accounted for via measurement-error model or via weights. In addition to the classical approach, we recently developed an approach based on bivariate auxiliary Poisson models with individual random effects to measure the Kendall's τ and treatment-by-trial interactions to measure the R trial 2 . The most common data simulation models described in the literature are based on: copula models, mixed proportional hazard models, and mixture of half-normal and exponential random variables. The R package surrosurv implements the classical two-step method with Clayton, Plackett, and Hougaard copulas. It also allows to optionally adjusting the second-step linear regression for measurement-error. The mixed Poisson approach is implemented with different reduced models in addition to the full model. We present the package functions for estimating the surrogacy models, for checking their convergence, for performing leave-one-trial-out cross-validation, and for plotting the results. We illustrate their use in practice on individual patient data from a meta-analysis of 4069 patients with advanced gastric cancer from 20 trials of chemotherapy. The surrosurv package provides an R implementation of classical and recent statistical methods for surrogacy assessment of failure time endpoints. Flexible simulation functions are available to generate data according to the methods described in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

Transient modeling in simulation of hospital operations for emergency response.

PubMed

Paul, Jomon Aliyas; George, Santhosh K; Yi, Pengfei; Lin, Li

2006-01-01

Rapid estimates of hospital capacity after an event that may cause a disaster can assist disaster-relief efforts. Due to the dynamics of hospitals, following such an event, it is necessary to accurately model the behavior of the system. A transient modeling approach using simulation and exponential functions is presented, along with its applications in an earthquake situation. The parameters of the exponential model are regressed using outputs from designed simulation experiments. The developed model is capable of representing transient, patient waiting times during a disaster. Most importantly, the modeling approach allows real-time capacity estimation of hospitals of various sizes and capabilities. Further, this research is an analysis of the effects of priority-based routing of patients within the hospital and the effects on patient waiting times determined using various patient mixes. The model guides the patients based on the severity of injuries and queues the patients requiring critical care depending on their remaining survivability time. The model also accounts the impact of prehospital transport time on patient waiting time.

Modelling episodic acidification of surface waters: the state of science.

PubMed

Eshleman, K N; Wigington, P J; Davies, T D; Tranter, M

1992-01-01

Field studies of chemical changes in surface waters associated with rainfall and snowmelt events have provided evidence of episodic acidification of lakes and streams in Europe and North America. Modelling these chemical changes is particularly challenging because of the variability associated with hydrological transport and chemical transformation processes in catchments. This paper provides a review of mathematical models that have been applied to the problem of episodic acidification. Several empirical approaches, including regression models, mixing models and time series models, support a strong hydrological interpretation of episodic acidification. Regional application of several models has suggested that acidic episodes (in which the acid neutralizing capacity becomes negative) are relatively common in surface waters in several regions of the US that receive acid deposition. Results from physically based models have suggested a lack of understanding of hydrological flowpaths, hydraulic residence times and biogeochemical reactions, particularly those involving aluminum. The ability to better predict episodic chemical responses of surface waters is thus dependent upon elucidation of these and other physical and chemical processes.

Is recurrence in major depressive disorder related to bipolarity and mixed features? Results from the BRIDGE-II-Mix study.

PubMed

Mazzarini, Lorenzo; Kotzalidis, Georgios D; Piacentino, Daria; Rizzato, Salvatore; Angst, Jules; Azorin, Jean-Michel; Bowden, Charles L; Mosolov, Sergey; Young, Allan H; Vieta, Eduard; Girardi, Paolo; Perugi, Giulio

2018-03-15

Current classifications separate Bipolar (BD) from Major Depressive Disorder (MDD) based on polarity rather than recurrence. We aimed to determine bipolar/mixed feature frequency in a large MDD multinational sample with (High-Rec) and without (Low-Rec) >3 recurrences, comparing the two subsamples. We measured frequency of bipolarity/hypomanic features during current depressive episodes (MDEs) in 2347 MDD patients from the BRIDGE-II-mix database, comparing High-Rec with Low-Rec. We used Bonferroni-corrected Student's t-test for continuous, and chi-squared test, for categorical variables. Logistic regression estimated the size of the association between clinical characteristics and High-Rec MDD. Compared to Low-Rec (n = 1084, 46.2%), High-Rec patients (n = 1263, 53.8%) were older, with earlier depressive onset, had more family history of BD, more atypical features, suicide attempts, hospitalisations, and treatment resistance and (hypo)manic switches when treated with antidepressants, higher comorbidity with borderline personality disorder, and more hypomanic symptoms during current MDE, resulting in higher rates of mixed depression according to both DSM-5 and research-based diagnostic (RBDC) criteria. Logistic regression showed age at first symptoms < 30 years, current MDE duration ≤ 1 month, hypomania/mania among first-degree relatives, past suicide attempts, treatment-resistance, antidepressant-induced swings, and atypical, mixed, or psychotic features during MDE to associate with High-Rec. Number of MDEs for defining recurrence was arbitrary; cross-sectionality did not allow assessment of conversion from MDD to BD. High-Rec MDD differed from Low-Rec group for several clinical/epidemiological variables, including bipolar/mixed features. Bipolarity specifier and RBDC were more sensitive than DSM-5 criteria in detecting bipolar and mixed features in MDD. Copyright © 2017. Published by Elsevier B.V.

Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire

USGS Publications Warehouse

Andy, Caroline; Fahnestock, Maria Florencia; Lombard, Melissa; Hayes, Laura; Bryce, Julie; Ayotte, Joseph

2017-01-01

Three existing multivariate logistic regression models were assessed using new data to evaluate the capacity of the models to correctly predict the probability of groundwater arsenic concentrations exceeding the threshold values of 1, 5, and 10 micrograms per liter (µg/L) in New Hampshire, USA. A recently released testing dataset includes arsenic concentrations from groundwater samples collected in 2004–2005 from a mix of 367 public-supply and private domestic wells. The use of this dataset to test three existing logistic regression models demonstrated enhanced overall predictive accuracy for the 5 and 10 μg/L models. Overall accuracies of 54.8, 76.3, and 86.4 percent were reported for the 1, 5, and 10 μg/L models, respectively. The state was divided by counties into northwest and southeast regions. Regional differences in accuracy were identified; models had an average accuracy of 83.1 percent for the counties in the northwest and 63.7 percent in the southeast. This is most likely due to high model specificity in the northwest and regional differences in arsenic occurrence. Though these models have limitations, they allow for arsenic hazard assessment across the region. The introduction of well-type (public or private), well depth, and casing length as explanatory variables may be appropriate measures to improve model performance. Our findings indicate that the original models generalize to the testing dataset, and should continue to serve as an important vehicle of preventative public health that may be applied to other groundwater contaminants in New Hampshire.

Evaluation of land use regression models for NO2 in El Paso, Texas, USA

PubMed Central

Gonzales, Melissa; Myers, Orrin; Smith, Luther; Olvera, Hector A.; Mukerjee, Shaibal; Li, Wen-Whai; Pingitore, Nicholas; Amaya, Maria; Burchiel, Scott; Berwick, Marianne

2012-01-01

Developing suitable exposure estimates for air pollution health studies is problematic due to spatial and temporal variation in concentrations and often limited monitoring data. Though land use regression models (LURs) are often used for this purpose, their applicability to later periods of time, larger geographic areas, and seasonal variation is largely untested. We evaluate a series of mixed model LURs to describe the spatial-temporal gradients of NO2 across El Paso County, Texas based on measurements collected during cool and warm seasons in 2006–2007 (2006–7). We also evaluated performance of a general additive model (GAM) developed for central El Paso in 1999 to assess spatial gradients across the County in 2006–7. Five LURs were developed iteratively from the study data and their predictions were averaged to provide robust nitrogen dioxide (NO2) concentration gradients across the county. Despite differences in sampling time frame, model covariates and model estimation methods, predicted NO2 concentration gradients were similar in the current study as compared to the 1999 study. Through a comprehensive LUR modeling campaign, it was shown that the nature of the most influential predictive variables remained the same for El Paso between the 1999 and 2006–7. The similar LUR results obtained here demonstrate that, at least for El Paso, LURs developed from prior years may still be applicable to assess exposure conditions in subsequent years and in different seasons when seasonal variation is taken into consideration. PMID:22728301

Interhospital differences and case-mix in a nationwide prevalence survey.

PubMed

Kanerva, M; Ollgren, J; Lyytikäinen, O

2010-10-01

A prevalence survey is a time-saving and useful tool for obtaining an overview of healthcare-associated infection (HCAI) either in a single hospital or nationally. Direct comparison of prevalence rates is difficult. We evaluated the impact of case-mix adjustment on hospital-specific prevalences. All five tertiary care, all 15 secondary care and 10 (25% of 40) other acute care hospitals took part in the first national prevalence survey in Finland in 2005. US Centers for Disease Control and Prevention criteria served to define HCAI. The information collected included demographic characteristics, severity of the underlying disease, use of catheters and a respirator, and previous surgery. Patients with HCAI related to another hospital were excluded. Case-mix-adjusted HCAI prevalences were calculated by using a multivariate logistic regression model for HCAI risk and an indirect standardisation method. Altogether, 587 (7.2%) of 8118 adult patients had at least one infection; hospital-specific prevalences ranged between 1.9% and 12.6%. Risk factors for HCAI that were previously known or identified by univariate analysis (age, male gender, intensive care, high Charlson comorbidity and McCabe indices, respirator, central venous or urinary catheters, and surgery during stay) were included in the multivariate analysis for standardisation. Case-mix-adjusted prevalences varied between 2.6% and 17.0%, and ranked the hospitals differently from the observed rates. In 11 (38%) hospitals, the observed prevalence rank was lower than predicted by the case-mix-adjusted figure. Case-mix should be taken into consideration in the interhospital comparison of prevalence rates. Copyright 2010 The Hospital Infection Society. Published by Elsevier Ltd. All rights reserved.

Resource utilization in home health care: results of a prospective study.

PubMed

Trisolini, M G; Thomas, C P; Cashman, S B; Payne, S M

1994-01-01

Resource utilization in home health care has become an issue of concern due to rising costs and recent initiatives to develop prospective payment systems for home health care. A number of issues remain unresolved for the development of prospective reimbursement in this sector, including the types of variables to be included as payment variables and appropriate measures of resource use. This study supplements previous work on home health case-mix by analyzing the factors affecting one aspect of resource use for skilled nursing visits--visit length--and explores the usefulness of several specially collected variables which are not routinely available in administrative records. A data collection instrument was developed with a focus group of skilled nurses, identifying a range of variables hypothesized to affect visit length. Five categories of variables were studied using multiple regression analysis: provider-related; patient's socio-economic status; patient's clinical status; patient's support services; and visit-specific. The final regression model identifies 9 variables which significantly affect visit time. Five of the 9 are visit-specific variables, a significant finding since these are not routinely collected. Case-mix systems which include visit time as a measure of resource use will need to investigate visit-specific variables, as this study indicates they could have the largest influence on visit time. Two other types of resources used in home health care, supplies and security drivers, were also investigated in less detail.

Using the simplified case mix tool (sCMT) to identify cost in special care dental services to support commissioning.

PubMed

Duane, B G; Freeman, R; Richards, D; Crosbie, S; Patel, P; White, S; Humphris, G

2017-03-01

To commission dental services for vulnerable (special care) patient groups effectively, consistently and fairly an evidence base is needed of the costs involved. The simplified Case Mixed Tool (sCMT) can assess treatment mode complexity for these patient groups. To determine if the sCMT can be used to identify costs of service provision. Patients (n=495) attending the Sussex Community NHS Trust Special Care Dental Service for care were assessed using the sCMT. sCMT score and costs (staffing, laboratory fees, etc.) besides patient age, whether a new patient and use of general anaesthetic/intravenous sedation. Statistical analysis (adjusted linear regression modelling) compared sCMT score and costs then sensitivity analyses of the costings to age, being a new patient and sedation use were undertaken. Regression tables were produced to present estimates of service costs. Costs increased with sCMT total scale and single item values in a predictable manner in all analyses except for 'cooperation'. Costs increased with the use of IV sedation; with each rising level of the sCMT, and with complexity in every sCMT category, except cooperation. Costs increased with increase in complexity of treatment mode as measured by sCMT scores. Measures such as the sCMT can provide predictions of the resource allocations required when commissioning special care dental services. Copyright© 2017 Dennis Barber Ltd.

The dynamic volume changes of polymerising polymethyl methacrylate bone cement.

PubMed

Muller, Scott D; Green, Sarah M; McCaskie, Andrew W

2002-12-01

The Swedish hip register found an increased risk of early revision of vacuum-mixed cemented total hip replacements. The influence of cement mixing technique on the dynamic volume change in polymerising PMMA is not well understood and may be relevant to this observation. Applying Archimedes' principle, we have investigated the dynamic volume changes in polymerising cement and determined the influence of mixing technique. All specimens showed an overall volume reduction: hand-mixed 3.4% and vacuum-mixed 6.0%. Regression analysis of sectional porosity and volume reduction showed a highly significant relationship. Hand-mixed porous cement showed a transient volume increase before solidification. However, vacuum-mixed cement showed a progressive volume reduction throughout polymerisation. Transient expansion of porous cement occurs at the critical time of micro-interlock formation, possibly improving fixation. Conversely, progressive volume reduction of vacuum-mixed cement throughout the formation of interlock may damage fixation. Stable fixation of vacuum-mixed cement may depend on additional techniques to offset the altered volumetric behaviour of vacuum-mixed cement.

Mixed features in patients with a major depressive episode: the BRIDGE-II-MIX study.

PubMed

Perugi, Giulio; Angst, Jules; Azorin, Jean-Michel; Bowden, Charles L; Mosolov, Sergey; Reis, Joao; Vieta, Eduard; Young, Allan H

2015-03-01

To estimate the frequency of mixed states in patients diagnosed with major depressive episode (MDE) according to conceptually different definitions and to compare their clinical validity. This multicenter, multinational cross-sectional Bipolar Disorders: Improving Diagnosis, Guidance and Education (BRIDGE)-II-MIX study enrolled 2,811 adult patients experiencing an MDE. Data were collected per protocol on sociodemographic variables, current and past psychiatric symptoms, and clinical variables that are risk factors for bipolar disorder. The frequency of mixed features was determined by applying both DSM-5 criteria and a priori described Research-Based Diagnostic Criteria (RBDC). Clinical variables associated with mixed features were assessed using logistic regression. Overall, 212 patients (7.5%) fulfilled DSM-5 criteria for MDE with mixed features (DSM-5-MXS), and 818 patients (29.1%) fulfilled diagnostic criteria for a predefined RBDC depressive mixed state (RBDC-MXS). The most frequent manic/hypomanic symptoms were irritable mood (32.6%), emotional/mood lability (29.8%), distractibility (24.4%), psychomotor agitation (16.1%), impulsivity (14.5%), aggression (14.2%), racing thoughts (11.8%), and pressure to keep talking (11.4%). Euphoria (4.6%), grandiosity (3.7%), and hypersexuality (2.6%) were less represented. In multivariate logistic regression analysis, RBDC-MXS was associated with the largest number of variables including diagnosis of bipolar disorder, family history of mania, lifetime suicide attempts, duration of the current episode > 1 month, atypical features, early onset, history of antidepressant-induced mania/hypomania, and lifetime comorbidity with anxiety, alcohol and substance use disorders, attention-deficit/hyperactivity disorder, and borderline personality disorder. Depressive mixed state, defined as the presence of 3 or more manic/hypomanic features, was present in around one-third of patients experiencing an MDE. The valid symptom, illness course and family history RBDC criteria we assessed identified 4 times more MDE patients as having mixed features and yielded statistically more robust associations with several illness characteristics of bipolar disorder than did DSM-5 criteria. © Copyright 2015 Physicians Postgraduate Press, Inc.

Maximal bite force, facial morphology and sucking habits in young children with functional posterior crossbite

PubMed Central

CASTELO, Paula Midori; GAVIÃO, Maria Beatriz Duarte; PEREIRA, Luciano José; BONJARDIM, Leonardo Rigoldi

2010-01-01

Objective The maintenance of normal conditions of the masticatory function is determinant for the correct growth and development of its structures. Thus, the aims of this study were to evaluate the influence of sucking habits on the presence of crossbite and its relationship with maximal bite force, facial morphology and body variables in 67 children of both genders (3.5-7 years) with primary or early mixed dentition. Material and methods The children were divided in four groups: primary-normocclusion (PN, n=19), primary-crossbite (PC, n=19), mixed-normocclusion (MN, n=13), and mixed-crossbite (MC, n=16). Bite force was measured with a pressurized tube, and facial morphology was determined by standardized frontal photographs: AFH (anterior face height) and BFW (bizygomatic facial width). Results It was observed that MC group showed lower bite force than MN, and AFH/ BFW was significantly smaller in PN than PC (t-test). Weight and height were only significantly correlated with bite force in PC group (Pearson’s correlation test). In the primary dentition, AFH/BFW and breast-feeding (at least six months) were positive and negatively associated with crossbite, respectively (multiple logistic regression). In the mixed dentition, breastfeeding and bite force showed negative associations with crossbite (univariate regression), while nonnutritive sucking (up to 3 years) associated significantly with crossbite in all groups (multiple logistic regression). Conclusions In the studied sample, sucking habits played an important role in the etiology of crossbite, which was associated with lower bite force and long-face tendency. PMID:20485925

A tutorial on count regression and zero-altered count models for longitudinal substance use data

PubMed Central

Atkins, David C.; Baldwin, Scott A.; Zheng, Cheng; Gallop, Robert J.; Neighbors, Clayton

2012-01-01

Critical research questions in the study of addictive behaviors concern how these behaviors change over time - either as the result of intervention or in naturalistic settings. The combination of count outcomes that are often strongly skewed with many zeroes (e.g., days using, number of total drinks, number of drinking consequences) with repeated assessments (e.g., longitudinal follow-up after intervention or daily diary data) present challenges for data analyses. The current article provides a tutorial on methods for analyzing longitudinal substance use data, focusing on Poisson, zero-inflated, and hurdle mixed models, which are types of hierarchical or multilevel models. Two example datasets are used throughout, focusing on drinking-related consequences following an intervention and daily drinking over the past 30 days, respectively. Both datasets as well as R, SAS, Mplus, Stata, and SPSS code showing how to fit the models are available on a supplemental website. PMID:22905895

Estimating Mixed Broadleaves Forest Stand Volume Using Dsm Extracted from Digital Aerial Images

NASA Astrophysics Data System (ADS)

Sohrabi, H.

2012-07-01

In mixed old growth broadleaves of Hyrcanian forests, it is difficult to estimate stand volume at plot level by remotely sensed data while LiDar data is absent. In this paper, a new approach has been proposed and tested for estimating stand forest volume. The approach is based on this idea that forest volume can be estimated by variation of trees height at plots. In the other word, the more the height variation in plot, the more the stand volume would be expected. For testing this idea, 120 circular 0.1 ha sample plots with systematic random design has been collected in Tonekaon forest located in Hyrcanian zone. Digital surface model (DSM) measure the height values of the first surface on the ground including terrain features, trees, building etc, which provides a topographic model of the earth's surface. The DSMs have been extracted automatically from aerial UltraCamD images so that ground pixel size for extracted DSM varied from 1 to 10 m size by 1m span. DSMs were checked manually for probable errors. Corresponded to ground samples, standard deviation and range of DSM pixels have been calculated. For modeling, non-linear regression method was used. The results showed that standard deviation of plot pixels with 5 m resolution was the most appropriate data for modeling. Relative bias and RMSE of estimation was 5.8 and 49.8 percent, respectively. Comparing to other approaches for estimating stand volume based on passive remote sensing data in mixed broadleaves forests, these results are more encouraging. One big problem in this method occurs when trees canopy cover is totally closed. In this situation, the standard deviation of height is low while stand volume is high. In future studies, applying forest stratification could be studied.

Using Case-Mix Adjustment Methods To Measure the Effectiveness of Substance Abuse Treatment: Three Examples Using Client Employment Outcomes.

ERIC Educational Resources Information Center

Koenig, Lane; Fields, Errol L.; Dall, Timothy M.; Ameen, Ansari Z.; Harwood, Henrick J.

This report demonstrates three applications of case-mix methods using regression analysis. The results are used to assess the relative effectiveness of substance abuse treatment providers. The report also examines the ability of providers to improve client employment outcomes, an outcome domain relatively unexamined in the assessment of provider…

Comparative Effects of Methylphenidate and Mixed Salts Amphetamine on Height and Weight in Children with Attention-Deficit/Hyperactivity Disorder

ERIC Educational Resources Information Center

Pliszka, Steven R.; Matthews, Thomas L.; Braslow, Kenneth J.; Watson, Melissa A.

2006-01-01

Objective: To determine whether methylphenidate (MPH) and mixed salts amphetamine (MSA) have different effects on growth in children with attention-deficit/hyperactivity disorder. Method: Patients treated for at least 1 year with MPH or MSA were identified. A linear regression was performed to determine the effect of stimulant type, patient…

Parental report of the early development of children with regressive autism: the delays-plus-regression phenotype.

PubMed

Ozonoff, Sally; Williams, Brenda J; Landa, Rebecca

2005-12-01

Most children with autism demonstrate developmental abnormalities in their first year, whereas others display regression after mostly normal development. Few studies have examined the early development of the latter group. This study developed a retrospective measure, the Early Development Questionnaire (EDQ), to collect specific, parent-reported information about development in the first 18 months. Based on their EDQ scores, 60 children with autism between the ages of 3 and 9 were divided into three groups: an early onset group (n = 29), a definite regression group (n = 23), and a heterogeneous mixed group (n = 8). Significant differences in early social development were found between the early onset and regression groups. However, over 50 percent of the children who experienced a regression demonstrated some early social deficits during the first year of life, long before regression and the apparent onset of autism. This group, tentatively labeled 'delays-plus-regression', deserves further study.

Modeling the spatial distribution of African buffalo (Syncerus caffer) in the Kruger National Park, South Africa

PubMed Central

Hughes, Kristen; Budke, Christine M.; Ward, Michael P.; Kerry, Ruth; Ingram, Ben

2017-01-01

The population density of wildlife reservoirs contributes to disease transmission risk for domestic animals. The objective of this study was to model the African buffalo distribution of the Kruger National Park. A secondary objective was to collect field data to evaluate models and determine environmental predictors of buffalo detection. Spatial distribution models were created using buffalo census information and archived data from previous research. Field data were collected during the dry (August 2012) and wet (January 2013) seasons using a random walk design. The fit of the prediction models were assessed descriptively and formally by calculating the root mean square error (rMSE) of deviations from field observations. Logistic regression was used to estimate the effects of environmental variables on the detection of buffalo herds and linear regression was used to identify predictors of larger herd sizes. A zero-inflated Poisson model produced distributions that were most consistent with expected buffalo behavior. Field data confirmed that environmental factors including season (P = 0.008), vegetation type (P = 0.002), and vegetation density (P = 0.010) were significant predictors of buffalo detection. Bachelor herds were more likely to be detected in dense vegetation (P = 0.005) and during the wet season (P = 0.022) compared to the larger mixed-sex herds. Static distribution models for African buffalo can produce biologically reasonable results but environmental factors have significant effects and therefore could be used to improve model performance. Accurate distribution models are critical for the evaluation of disease risk and to model disease transmission. PMID:28902858

Modeling ozone episodes in the Baltimore-Washington region

NASA Technical Reports Server (NTRS)

Ryan, William F.

1994-01-01

Surface ozone (O3) concentrations in excess of the National Ambient Air Quality Standard (NAAQS) continue to occur in metropolitan areas in the United States despite efforts to control emissions of O3 precursors. Future O3 control strategies will be based on results from modeling efforts that have just begun in many areas. Two initial questions that arise are model sensitivity to domain-specific conditions and the selection of episodes for model evaluation and control strategy development. For the Baltimore-Washington region (B-W), the presence of the Chesapeake Bay introduces a number of issues relevant to model sensitivity. In this paper, the specific questions of the determination of model volume (mixing height) for the Urban Airshed Model (UAM) is discussed and various alternative methods compared. For the latter question, several analytic approaches, Cluster Analysis and classification and Regression Tree (CART) analysis are undertaken to determine meteorological conditions associated with severe O3 events in the B-W domain.

Growth rate characteristics of acidophilic heterotrophic organisms from mine waste rock piles

NASA Astrophysics Data System (ADS)

Yacob, T. W.; Silverstein, J.; Jenkins, J.; Andre, B. J.; Rajaram, H.

2010-12-01

Autotrophic iron oxidizing bacteria play a key role in pyrite oxidation and generation of acid mine drainage AMD. Scarcity of organic substrates in many disturbed sites insures that IOB have sufficient oxygen and other nutrients for growth. It is proposed that addition of organic carbon substrate to waste rock piles will result in enrichment of heterotrophic microorganisms limiting the role of IOB in AMD generation. Previous researchers have used the acidophilic heterotroph Acidiphilium cryptum as a model to study the effects of organic substrate addition on the pyrite oxidation/AMD cycle. In order to develop a quantitative model of effects such as competition for oxygen, it is necessary to use growth and substrate consumption rate expressions, and one approach is to choose a model strain such as A. cryptum for kinetic studies. However we have found that the growth rate characteristics of A. cryptum may not provide an accurate model of the remediation effects of organic addition to subsurface mined sites. Fluorescent in-situ hybridization (FISH) assays of extracts of mine waste rock enriched with glucose and yeast extract did not produce countable numbers of cells in the Acidiphilium genus, with a detection limit of3 x 104 cells/gram rock, despite evidence of the presence of well established heterotrophic organisms. However, an MPN enrichment produced heterotrophic population estimates of 1x107 and 1x109 cells/gram rock. Growth rate studies of A. cryptum showed that cultures took 120 hours to degrade 50% of an initial glucose concentration of 2,000 mg/L. However a mixed culture enriched from mine waste rock consumed 100% of the same amount of glucose in 24 hours. Substrate consumption data for the mixed culture were fit to a Monod growth model: {dS}/{dt} = μ_{max}S {( {X_0}/{Y} + S_0 -S )}/{(K_s +S)} Kinetic parameters were estimated utilizing a non linear regression method coupled with an ODE solver. The maximum specific growth rate of the mixed population with μ max was calculated to be 0.13 hr-1 and a yield of 0.52 g cells/g glucose and Ks of 0.2 g/L glucose. The effect of pH on growth was compared for A. cryptum and the mixed population. It was found that the mixed culture had a higher tolerance for extremely low pH conditions, with no growth at pH = 1; whereas no growth of A cryptum was observed at pH = 1.5. Both A. cryptum and the mixed cultures grew within a pH range of 2.5 - 6. A phospholipid fatty acid analysis (PLFA) of the mixed culture indicated that both eukaryotic and prokaryotic organisms are present at a ratio of approximately 1:1, indicating that organisms such as fungi may be important in carbon cycling in these acidic subsurface formations. The results from this research show that utilization of mixed wild cultures for environmental modeling may yield better results than selection of a single strain to represent populations in a quantitative model.

Risk modeling for ventricular assist device support in post-cardiotomy shock.

PubMed

Alsoufi, Bahaaldin; Rao, Vivek; Tang, Augustine; Maganti, Manjula; Cusimano, Robert

2012-04-01

Post-cardiotomy shock (PCS) has a complex etiology. Although treatment with inotrops and intra-aortic balloon pump (IABP) support improves cardiac performance, end-organ injuries are common and lead to prolonged ICU stay, extended hospitalization and increased mortality. Early consideration of mechanical circulatory support may prevent such complications and improve outcome. Between January 1997 and January 2002, 321 patients required IABP and inotropic support for PCS following coronary artery bypass grafting (CABG) at our institution. Perioperative variables including age, mixed venous saturation (MVO2), inotropic requirements and LV function were analyzed using multivariate statistical methods. All explanatory variables with a univariate p value <0.10 were entered into a stepwise logistic regression model to predict hospital mortality. Odds ratios from significant variables (p < 0.05) in the regression model were used to compose a risk score. Overall hospital mortality was 16%. The independent risk factors for mortality in this population were: MVO2 < 60% (OR = 3.2), milrinone > 0.5 μg/kg/min (OR = 3.2), age > 75 (OR = 2.7), adrenaline > 0.1 μg/kg/min (OR = 1.5). A 15-point risk score was developed based on the regression model. Hospital mortality in patients with a score >6 was 46% (n = 13/28), 3-6 was 31% (n = 9/29) and <3 was 11% (n = 29/264). A significant proportion of patients with PCS continue to face high mortality despite IABP and inotropic support. Advanced age, heavy inotropic dependency and poor oxygen delivery all predicted increased risk for death. Further investigation is needed to assess whether early institution of VAD support could improve outcome in this high-risk group of patients.

[A site index model for Larix principis-rupprechtii plantation in Saihanba, north China].

PubMed

Wang, Dong-zhi; Zhang, Dong-yan; Jiang, Feng-ling; Bai, Ye; Zhang, Zhi-dong; Huang, Xuan-rui

2015-11-01

It is often difficult to estimate site indices for different types of plantation by using an ordinary site index model. The objective of this paper was to establish a site index model for plantations in varied site conditions, and assess the site qualities. In this study, a nonlinear mixed site index model was constructed based on data from the second class forest resources inventory and 173 temporary sample plots. The results showed that the main limiting factors for height growth of Larix principis-rupprechtii were elevation, slope, soil thickness and soil type. A linear regression model was constructed for the main constraining site factors and dominant tree height, with the coefficient of determination being 0.912, and the baseline age of Larix principis-rupprechtii determined as 20 years. The nonlinear mixed site index model parameters for the main site types were estimated (R2 > 0.85, the error between the predicted value and the actual value was in the range of -0.43 to 0.45, with an average root mean squared error (RMSE) in the range of 0.907 to 1.148). The estimation error between the predicted value and the actual value of dominant tree height for the main site types was in the confidence interval of [-0.95, 0.95]. The site quality of the high altitude-shady-sandy loam-medium soil layer was the highest and that of low altitude-sunny-sandy loam-medium soil layer was the lowest, while the other two sites were moderate.

Conditional random slope: A new approach for estimating individual child growth velocity in epidemiological research.

PubMed

Leung, Michael; Bassani, Diego G; Racine-Poon, Amy; Goldenberg, Anna; Ali, Syed Asad; Kang, Gagandeep; Premkumar, Prasanna S; Roth, Daniel E

2017-09-10

Conditioning child growth measures on baseline accounts for regression to the mean (RTM). Here, we present the "conditional random slope" (CRS) model, based on a linear-mixed effects model that incorporates a baseline-time interaction term that can accommodate multiple data points for a child while also directly accounting for RTM. In two birth cohorts, we applied five approaches to estimate child growth velocities from 0 to 12 months to assess the effect of increasing data density (number of measures per child) on the magnitude of RTM of unconditional estimates, and the correlation and concordance between the CRS and four alternative metrics. Further, we demonstrated the differential effect of the choice of velocity metric on the magnitude of the association between infant growth and stunting at 2 years. RTM was minimally attenuated by increasing data density for unconditional growth modeling approaches. CRS and classical conditional models gave nearly identical estimates with two measures per child. Compared to the CRS estimates, unconditional metrics had moderate correlation (r = 0.65-0.91), but poor agreement in the classification of infants with relatively slow growth (kappa = 0.38-0.78). Estimates of the velocity-stunting association were the same for CRS and classical conditional models but differed substantially between conditional versus unconditional metrics. The CRS can leverage the flexibility of linear mixed models while addressing RTM in longitudinal analyses. © 2017 The Authors American Journal of Human Biology Published by Wiley Periodicals, Inc.

BDNF Val66Met predicts cognitive decline in the Wisconsin Registry for Alzheimer's Prevention

PubMed Central

Boots, Elizabeth A.; Schultz, Stephanie A.; Clark, Lindsay R.; Racine, Annie M.; Darst, Burcu F.; Koscik, Rebecca L.; Carlsson, Cynthia M.; Gallagher, Catherine L.; Hogan, Kirk J.; Bendlin, Barbara B.; Asthana, Sanjay; Sager, Mark A.; Hermann, Bruce P.; Christian, Bradley T.; Dubal, Dena B.; Engelman, Corinne D.; Johnson, Sterling C.

2017-01-01

Objective: To examine the influence of the brain-derived neurotrophic factor (BDNF) Val66Met polymorphism on longitudinal cognitive trajectories in a large, cognitively healthy cohort enriched for Alzheimer disease (AD) risk and to understand whether β-amyloid (Aβ) burden plays a moderating role in this relationship. Methods: One thousand twenty-three adults (baseline age 54.94 ± 6.41 years) enrolled in the Wisconsin Registry for Alzheimer's Prevention underwent BDNF genotyping and cognitive assessment at up to 5 time points (average follow-up 6.92 ± 3.22 years). A subset (n = 140) underwent 11C-Pittsburgh compound B (PiB) scanning. Covariate-adjusted mixed-effects regression models were used to elucidate the effect of BDNF on cognitive trajectories in 4 cognitive domains, including verbal learning and memory, speed and flexibility, working memory, and immediate memory. Secondary mixed-effects regression models were conducted to examine whether Aβ burden, indexed by composite PiB load, modified any observed BDNF-related cognitive trajectories. Results: Compared to BDNF Val/Val homozygotes, Met carriers showed steeper decline in verbal learning and memory (p = 0.002) and speed and flexibility (p = 0.017). In addition, Aβ burden moderated the relationship between BDNF and verbal learning and memory such that Met carriers with greater Aβ burden showed even steeper cognitive decline (p = 0.033). Conclusions: In a middle-aged cohort with AD risk, carriage of the BDNF Met allele was associated with steeper decline in episodic memory and executive function. This decline was exacerbated by greater Aβ burden. These results suggest that the BDNF Val66Met polymorphism may play an important role in cognitive decline and could be considered as a target for novel AD therapeutics. PMID:28468845

Dissecting HIV Virulence: Heritability of Setpoint Viral Load, CD4+ T-Cell Decline, and Per-Parasite Pathogenicity.

PubMed

Bertels, Frederic; Marzel, Alex; Leventhal, Gabriel; Mitov, Venelin; Fellay, Jacques; Günthard, Huldrych F; Böni, Jürg; Yerly, Sabine; Klimkait, Thomas; Aubert, Vincent; Battegay, Manuel; Rauch, Andri; Cavassini, Matthias; Calmy, Alexandra; Bernasconi, Enos; Schmid, Patrick; Scherrer, Alexandra U; Müller, Viktor; Bonhoeffer, Sebastian; Kouyos, Roger; Regoes, Roland R

2018-01-01

Pathogen strains may differ in virulence because they attain different loads in their hosts, or because they induce different disease-causing mechanisms independent of their load. In evolutionary ecology, the latter is referred to as "per-parasite pathogenicity". Using viral load and CD4+ T-cell measures from 2014 HIV-1 subtype B-infected individuals enrolled in the Swiss HIV Cohort Study, we investigated if virulence-measured as the rate of decline of CD4+ T cells-and per-parasite pathogenicity are heritable from donor to recipient. We estimated heritability by donor-recipient regressions applied to 196 previously identified transmission pairs, and by phylogenetic mixed models applied to a phylogenetic tree inferred from HIV pol sequences. Regressing the CD4+ T-cell declines and per-parasite pathogenicities of the transmission pairs did not yield heritability estimates significantly different from zero. With the phylogenetic mixed model, however, our best estimate for the heritability of the CD4+ T-cell decline is 17% (5-30%), and that of the per-parasite pathogenicity is 17% (4-29%). Further, we confirm that the set-point viral load is heritable, and estimate a heritability of 29% (12-46%). Interestingly, the pattern of evolution of all these traits differs significantly from neutrality, and is most consistent with stabilizing selection for the set-point viral load, and with directional selection for the CD4+ T-cell decline and the per-parasite pathogenicity. Our analysis shows that the viral genotype affects virulence mainly by modulating the per-parasite pathogenicity, while the indirect effect via the set-point viral load is minor. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Dissecting HIV Virulence: Heritability of Setpoint Viral Load, CD4+ T-Cell Decline, and Per-Parasite Pathogenicity

PubMed Central

Bertels, Frederic; Marzel, Alex; Leventhal, Gabriel; Mitov, Venelin; Fellay, Jacques; Günthard, Huldrych F; Böni, Jürg; Yerly, Sabine; Klimkait, Thomas; Aubert, Vincent; Battegay, Manuel; Rauch, Andri; Cavassini, Matthias; Calmy, Alexandra; Bernasconi, Enos; Schmid, Patrick; Scherrer, Alexandra U; Müller, Viktor; Bonhoeffer, Sebastian; Kouyos, Roger; Regoes, Roland R

2018-01-01

Abstract Pathogen strains may differ in virulence because they attain different loads in their hosts, or because they induce different disease-causing mechanisms independent of their load. In evolutionary ecology, the latter is referred to as “per-parasite pathogenicity”. Using viral load and CD4+ T-cell measures from 2014 HIV-1 subtype B-infected individuals enrolled in the Swiss HIV Cohort Study, we investigated if virulence—measured as the rate of decline of CD4+ T cells—and per-parasite pathogenicity are heritable from donor to recipient. We estimated heritability by donor–recipient regressions applied to 196 previously identified transmission pairs, and by phylogenetic mixed models applied to a phylogenetic tree inferred from HIV pol sequences. Regressing the CD4+ T-cell declines and per-parasite pathogenicities of the transmission pairs did not yield heritability estimates significantly different from zero. With the phylogenetic mixed model, however, our best estimate for the heritability of the CD4+ T-cell decline is 17% (5–30%), and that of the per-parasite pathogenicity is 17% (4–29%). Further, we confirm that the set-point viral load is heritable, and estimate a heritability of 29% (12–46%). Interestingly, the pattern of evolution of all these traits differs significantly from neutrality, and is most consistent with stabilizing selection for the set-point viral load, and with directional selection for the CD4+ T-cell decline and the per-parasite pathogenicity. Our analysis shows that the viral genotype affects virulence mainly by modulating the per-parasite pathogenicity, while the indirect effect via the set-point viral load is minor. PMID:29029206

Establishing a composite endpoint for measuring the effectiveness of geriatric interventions based on older persons' and informal caregivers' preference weights: a vignette study.

PubMed

Hofman, Cynthia S; Makai, Peter; Boter, Han; Buurman, Bianca M; de Craen, Anton J M; Olde Rikkert, Marcel G M; Donders, Rogier A R T; Melis, René J F

2014-04-18

The Older Persons and Informal Caregivers Survey Minimal Dataset's (TOPICS-MDS) questionnaire which measures relevant outcomes for elderly people was successfully incorporated into over 60 research projects of the Dutch National Care for the Elderly Programme. A composite endpoint (CEP) for this instrument would be helpful to compare effectiveness of the various intervention projects. Therefore, our aim is to establish a CEP for the TOPICS-MDS questionnaire, based on the preferences of elderly persons and informal caregivers. A vignette study was conducted with 200 persons (124 elderly and 76 informal caregivers) as raters. The vignettes described eight TOPICS-MDS outcomes of older persons (morbidity, functional limitations, emotional well-being, pain experience, cognitive functioning, social functioning, self-perceived health and self-perceived quality of life) and the raters assessed the general well-being (GWB) of these vignette cases on a numeric rating scale (0-10). Mixed linear regression analyses were used to derive the preference weights of the TOPICS-MDS outcomes (dependent variable: GWB scores; fixed factors: the eight outcomes; unstandardized coefficients: preference weights). The mixed regression model that combined the eight outcomes showed that the weights varied from 0.01 for social functioning to 0.16 for self-perceived health. A model that included "informal caregiver" showed that the interactions between this variable and each of the eight outcomes were not significant (p > 0.05). A preference-weighted CEP for TOPICS-MDS questionnaire was established based on the preferences of older persons and informal caregivers. With this CEP optimal comparing the effectiveness of interventions in older persons can be realized.

Longitudinal data analysis in support of functional stability concepts for leachate management at closed municipal landfills

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

Gibbons, Robert D., E-mail: rdg@uchicago.edu; Morris, Jeremy W.F., E-mail: jmorris@geosyntec.com; Prucha, Christopher P., E-mail: cprucha@wm.com

2014-09-15

Highlights: • Longitudinal data analysis using a mixed-effects regression model. • Dataset consisted of a total of 1402 samples from 101 closed municipal landfills. • Target analytes and classes generally showed predictable degradation trends. • Validates historical studies focused on macro organic indicators such as BOD. • BOD can serve as “gateway” indicator for planning leachate management. - Abstract: Landfill functional stability provides a target that supports no environmental threat at the relevant point of exposure in the absence of active control systems. With respect to leachate management, this study investigates “gateway” indicators for functional stability in terms of themore » predictability of leachate characteristics, and thus potential threat to water quality posed by leachate emissions. Historical studies conducted on changes in municipal solid waste (MSW) leachate concentrations over time (longitudinal analysis) have concentrated on indicator compounds, primarily chemical oxygen demand (COD) and biochemical oxygen demand (BOD). However, validation of these studies using an expanded database and larger constituent sets has not been performed. This study evaluated leachate data using a mixed-effects regression model to determine the extent to which leachate constituent degradation can be predicted based on waste age or operational practices. The final dataset analyzed consisted of a total of 1402 samples from 101 MSW landfills. Results from the study indicated that all leachate constituents exhibit a decreasing trend with time in the post-closure period, with 16 of the 25 target analytes and aggregate classes exhibiting a statistically significant trend consistent with well-studied indicators such as BOD. Decreasing trends in BOD concentration after landfill closure can thus be considered representative of trends for many leachate constituents of concern.« less

Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks.

PubMed

van Mierlo, Trevor; Hyatt, Douglas; Ching, Andrew T

2016-01-01

Digital Health Social Networks (DHSNs) are common; however, there are few metrics that can be used to identify participation inequality. The objective of this study was to investigate whether the Gini coefficient, an economic measure of statistical dispersion traditionally used to measure income inequality, could be employed to measure DHSN inequality. Quarterly Gini coefficients were derived from four long-standing DHSNs. The combined data set included 625,736 posts that were generated from 15,181 actors over 18,671 days. The range of actors (8-2323), posts (29-28,684), and Gini coefficients (0.15-0.37) varied. Pearson correlations indicated statistically significant associations between number of actors and number of posts (0.527-0.835, p  < .001), and Gini coefficients and number of posts (0.342-0.725, p  < .001). However, the association between Gini coefficient and number of actors was only statistically significant for the addiction networks (0.619 and 0.276, p  < .036). Linear regression models had positive but mixed R 2 results (0.333-0.527). In all four regression models, the association between Gini coefficient and posts was statistically significant ( t  = 3.346-7.381, p  < .002). However, unlike the Pearson correlations, the association between Gini coefficient and number of actors was only statistically significant in the two mental health networks ( t  = -4.305 and -5.934, p  < .000). The Gini coefficient is helpful in measuring shifts in DHSN inequality. However, as a standalone metric, the Gini coefficient does not indicate optimal numbers or ratios of actors to posts, or effective network engagement. Further, mixed-methods research investigating quantitative performance metrics is required.

BDNF Val66Met predicts cognitive decline in the Wisconsin Registry for Alzheimer's Prevention.

PubMed

Boots, Elizabeth A; Schultz, Stephanie A; Clark, Lindsay R; Racine, Annie M; Darst, Burcu F; Koscik, Rebecca L; Carlsson, Cynthia M; Gallagher, Catherine L; Hogan, Kirk J; Bendlin, Barbara B; Asthana, Sanjay; Sager, Mark A; Hermann, Bruce P; Christian, Bradley T; Dubal, Dena B; Engelman, Corinne D; Johnson, Sterling C; Okonkwo, Ozioma C

2017-05-30

To examine the influence of the brain-derived neurotrophic factor ( BDNF ) Val66Met polymorphism on longitudinal cognitive trajectories in a large, cognitively healthy cohort enriched for Alzheimer disease (AD) risk and to understand whether β-amyloid (Aβ) burden plays a moderating role in this relationship. One thousand twenty-three adults (baseline age 54.94 ± 6.41 years) enrolled in the Wisconsin Registry for Alzheimer's Prevention underwent BDNF genotyping and cognitive assessment at up to 5 time points (average follow-up 6.92 ± 3.22 years). A subset (n = 140) underwent 11 C-Pittsburgh compound B (PiB) scanning. Covariate-adjusted mixed-effects regression models were used to elucidate the effect of BDNF on cognitive trajectories in 4 cognitive domains, including verbal learning and memory, speed and flexibility, working memory, and immediate memory. Secondary mixed-effects regression models were conducted to examine whether Aβ burden, indexed by composite PiB load, modified any observed BDNF -related cognitive trajectories. Compared to BDNF Val/Val homozygotes, Met carriers showed steeper decline in verbal learning and memory ( p = 0.002) and speed and flexibility ( p = 0.017). In addition, Aβ burden moderated the relationship between BDNF and verbal learning and memory such that Met carriers with greater Aβ burden showed even steeper cognitive decline ( p = 0.033). In a middle-aged cohort with AD risk, carriage of the BDNF Met allele was associated with steeper decline in episodic memory and executive function. This decline was exacerbated by greater Aβ burden. These results suggest that the BDNF Val66Met polymorphism may play an important role in cognitive decline and could be considered as a target for novel AD therapeutics. © 2017 American Academy of Neurology.

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

Posttraumatic Stress Disorder Symptom Clusters and the Interpersonal Theory of Suicide in a Large Military Sample.

PubMed

Pennings, Stephanie M; Finn, Joseph; Houtsma, Claire; Green, Bradley A; Anestis, Michael D

2017-10-01

Prior studies examining posttraumatic stress disorder (PTSD) symptom clusters and the components of the interpersonal theory of suicide (ITS) have yielded mixed results, likely stemming in part from the use of divergent samples and measurement techniques. This study aimed to expand on these findings by utilizing a large military sample, gold standard ITS measures, and multiple PTSD factor structures. Utilizing a sample of 935 military personnel, hierarchical multiple regression analyses were used to test the association between PTSD symptom clusters and the ITS variables. Additionally, we tested for indirect effects of PTSD symptom clusters on suicidal ideation through thwarted belongingness, conditional on levels of perceived burdensomeness. Results indicated that numbing symptoms are positively associated with both perceived burdensomeness and thwarted belongingness and hyperarousal symptoms (dysphoric arousal in the 5-factor model) are positively associated with thwarted belongingness. Results also indicated that hyperarousal symptoms (anxious arousal in the 5-factor model) were positively associated with fearlessness about death. The positive association between PTSD symptom clusters and suicidal ideation was inconsistent and modest, with mixed support for the ITS model. Overall, these results provide further clarity regarding the association between specific PTSD symptom clusters and suicide risk factors. © 2016 The American Association of Suicidology.

The effects of green areas on air surface temperature of the Kuala Lumpur city using WRF-ARW modelling and Remote Sensing technique

NASA Astrophysics Data System (ADS)

Isa, N. A.; Mohd, W. M. N. Wan; Salleh, S. A.; Ooi, M. C. G.

2018-02-01

Matured trees contain high concentration of chlorophyll that encourages the process of photosynthesis. This process produces oxygen as a by-product and releases it into the atmosphere and helps in lowering the ambient temperature. This study attempts to analyse the effect of green area on air surface temperature of the Kuala Lumpur city. The air surface temperatures of two different dates which are, in March 2006 and March 2016 were simulated using the Weather Research and Forecasting (WRF) model. The green area in the city was extracted using the Normalized Difference Vegetation Index (NDVI) from two Landsat satellite images. The relationship between the air surface temperature and the green area were analysed using linear regression models. From the study, it was found that, the green area was significantly affecting the distribution of air temperature within the city. A strong negative correlation was identified through this study which indicated that higher NDVI values tend to have lower air surface temperature distribution within the focus study area. It was also found that, different urban setting in mixed built-up and vegetated areas resulted in different distributions of air surface temperature. Future studies should focus on analysing the air surface temperature within the area of mixed built-up and vegetated area.

An Efficient Test for Gene-Environment Interaction in Generalized Linear Mixed Models with Family Data.

PubMed

Mazo Lopera, Mauricio A; Coombes, Brandon J; de Andrade, Mariza

2017-09-27

Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma ( PPARG ) gene associated with diabetes.

An Update of the Bodeker Scientific Vertically Resolved, Global, Gap-Free Ozone Database

NASA Astrophysics Data System (ADS)

Kremser, S.; Bodeker, G. E.; Lewis, J.; Hassler, B.

2016-12-01

High vertical resolution ozone measurements from multiple satellite-based instruments have been merged with measurements from the global ozonesonde network to calculate monthly mean ozone values in 5º latitude zones. Ozone number densities and ozone mixing ratios are provided on 70 altitude levels (1 to 70 km) and on 70 pressure levels spaced approximately 1 km apart (878.4 hPa to 0.046 hPa). These data are sparse and do not cover the entire globe or altitude range. To provide a gap-free database, a least squares regression model is fitted to these data and then evaluated globally. By applying a single fit at each level, and using the approach of allowing the regression fits to change only slightly from one level to the next, the regression is less sensitive to measurement anomalies at individual stations or to individual satellite-based instruments. Particular attention is paid to ensuring that the low ozone abundances in the polar regions are captured. This presentation reports on updates to an earlier version of the vertically resolved ozone database, including the incorporation of new ozone measurements and new techniques for combining the data. Compared to previous versions of the database, particular attention is paid to avoiding spatial and temporal sampling biases and tracing uncertainties through to the final product. This updated database, developed within the New Zealand Deep South National Science Challenge, is suitable for assessing ozone fields from chemistry-climate model simulations or for providing the ozone boundary conditions for global climate model simulations that do not treat stratospheric chemistry interactively.

Rural Hospital Ownership: Medical Service Provision, Market Mix, and Spillover Effects

PubMed Central

Horwitz, Jill R; Nichols, Austin

2011-01-01

Objective To test whether nonprofit, for-profit, or government hospital ownership affects medical service provision in rural hospital markets, either directly or through the spillover effects of ownership mix. Data Sources/Study Setting Data are from the American Hospital Association, U.S. Census, CMS Healthcare Cost Report Information System and Prospective Payment System Minimum Data File, and primary data collection for geographic coordinates. The sample includes all nonfederal, general medical, and surgical hospitals located outside of metropolitan statistical areas and within the continental United States from 1988 to 2005. Study Design We estimate multivariate regression models to examine the effects of (1) hospital ownership and (2) hospital ownership mix within rural hospital markets on profitable versus unprofitable medical service offerings. Principal Findings Rural nonprofit hospitals are more likely than for-profit hospitals to offer unprofitable services, many of which are underprovided services. Nonprofits respond less than for-profits to changes in service profitability. Nonprofits with more for-profit competitors offer more profitable services and fewer unprofitable services than those with fewer for-profit competitors. Conclusions Rural hospital ownership affects medical service provision at the hospital and market levels. Nonprofit hospital regulation should reflect both the direct and spillover effects of ownership. PMID:21639860

Rural hospital ownership: medical service provision, market mix, and spillover effects.

PubMed

Horwitz, Jill R; Nichols, Austin

2011-10-01

To test whether nonprofit, for-profit, or government hospital ownership affects medical service provision in rural hospital markets, either directly or through the spillover effects of ownership mix. Data are from the American Hospital Association, U.S. Census, CMS Healthcare Cost Report Information System and Prospective Payment System Minimum Data File, and primary data collection for geographic coordinates. The sample includes all nonfederal, general medical, and surgical hospitals located outside of metropolitan statistical areas and within the continental United States from 1988 to 2005. We estimate multivariate regression models to examine the effects of (1) hospital ownership and (2) hospital ownership mix within rural hospital markets on profitable versus unprofitable medical service offerings. Rural nonprofit hospitals are more likely than for-profit hospitals to offer unprofitable services, many of which are underprovided services. Nonprofits respond less than for-profits to changes in service profitability. Nonprofits with more for-profit competitors offer more profitable services and fewer unprofitable services than those with fewer for-profit competitors. Rural hospital ownership affects medical service provision at the hospital and market levels. Nonprofit hospital regulation should reflect both the direct and spillover effects of ownership. © Health Research and Educational Trust.

The Relative Effectiveness of Women-Only and Mixed-Gender Treatment for Substance-Abusing Women

PubMed Central

Prendergast, Michael L.; Messina, Nena P.; Hall, Elizabeth A.; Warda, Umme S.

2011-01-01

Following research indicating that the treatment needs of women are different from those of men, researchers and clinicians have argued that drug treatment programs for women should be designed to take their needs into account. Such programs tend to admit only women and incorporate philosophies and activities that are based on a social, peer-based model that is responsive to their needs. To assess the relative effectiveness of women-only (WO) outpatient programs compared to mixed-gender (MG) outpatient programs, 291 study volunteers were recruited (152 WO, 139 MG), and a 1-year follow-up was completed with 259 women (135 WO, 124 MG). Using bivariate, logistic regression, and generalized estimating equation analysis, the following four outcomes were examined: drug and alcohol use, criminal activity, arrests, and employment. In both groups, women showed improvement in the four outcome measures. Comparison of the groups on outcomes yielded mixed results; women who participated in WO treatment reported significantly less substance use and criminal activity than women in MG treatment, but there were no differences in arrest or employment status at follow up compared with those in MG treatment. PMID:21315540

A Revised Method of Presenting Wavenumber-Frequency Power Spectrum Diagrams That Reveals the Asymmetric Nature of Tropical Large-scale Waves

NASA Technical Reports Server (NTRS)

Chao, Winston C.; Yang, Bo; Fu, Xiouhua

2007-01-01

The popular method of presenting wavenumber-frequency power spectrum diagrams for studying tropical large-scale waves in the literature is shown to give an incomplete presentation of these waves. The so-called "convectively-coupled Kelvin (mixed Rossby-gravity) waves" are presented as existing only in the symmetric (antisymmetric) component of the diagrams. This is obviously not consistent with the published composite/regression studies of "convectively-coupled Kelvin waves," which illustrate the asymmetric nature of these waves. The cause of this inconsistency is revealed in this note and a revised method of presenting the power spectrum diagrams is proposed. When this revised method is used, "convectively-coupled Kelvin waves" do show anti-symmetric components, and "convectively-coupled mixed Rossby-gravity waves (also known as Yanai waves)" do show a hint of symmetric components. These results bolster a published proposal that these waves be called "chimeric Kelvin waves," "chimeric mixed Rossby-gravity waves," etc. This revised method of presenting power spectrum diagrams offers a more rigorous means of comparing the General Circulation Models (GCM) output with observations by calling attention to the capability of GCMs in correctly simulating the asymmetric characteristics of the equatorial waves.

Mobile phone use during driving: Effects on speed and effectiveness of driver compensatory behaviour.

PubMed

Choudhary, Pushpa; Velaga, Nagendra R

2017-09-01

This study analysed and modelled the effects of conversation and texting (each with two difficulty levels) on driving performance of Indian drivers in terms of their mean speed and accident avoiding abilities; and further explored the relationship between speed reduction strategy of the drivers and their corresponding accident frequency. 100 drivers of three different age groups (young, mid-age and old-age) participated in the simulator study. Two sudden events of Indian context: unexpected crossing of pedestrians and joining of parked vehicles from road side, were simulated for estimating the accident probabilities. Generalized linear mixed models approach was used for developing linear regression models for mean speed and binary logistic regression models for accident probability. The results of the models showed that the drivers significantly compensated the increased workload by reducing their mean speed by 2.62m/s and 5.29m/s in the presence of conversation and texting tasks respectively. The logistic models for accident probabilities showed that the accident probabilities increased by 3 and 4 times respectively when the drivers were conversing or texting on a phone during driving. Further, the relationship between the speed reduction patterns and their corresponding accident frequencies showed that all the drivers compensated differently; but, among all the drivers, only few drivers, who compensated by reducing the speed by 30% or more, were able to fully offset the increased accident risk associated with the phone use. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating Building Age with 3d GIS

NASA Astrophysics Data System (ADS)

Biljecki, F.; Sindram, M.

2017-10-01

Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from other attributes using random forest regression. The developed method has a two-fold benefit: enriching datasets and quality control (verification of existing attributes). Experiments are carried out on a semantically rich LOD1 dataset of Rotterdam in the Netherlands using 9 attributes. The results are mixed: the accuracy in the estimation of building age depends on the available information used in the regression model. In the best scenario we have achieved predictions with an RMSE of 11 years, but in more realistic situations with limited knowledge about buildings the error is much larger (RMSE = 26 years). Hence the main conclusion of the paper is that inferring building age with 3D city models is possible to a certain extent because it reveals the approximate period of construction, but precise estimations remain a difficult task.

Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus.

PubMed

Cohen, Mark E; Ko, Clifford Y; Bilimoria, Karl Y; Zhou, Lynn; Huffman, Kristopher; Wang, Xue; Liu, Yaoming; Kraemer, Kari; Meng, Xiangju; Merkow, Ryan; Chow, Warren; Matel, Brian; Richards, Karen; Hart, Amy J; Dimick, Justin B; Hall, Bruce L

2013-08-01

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects detailed clinical data from participating hospitals using standardized data definitions, analyzes these data, and provides participating hospitals with reports that permit risk-adjusted comparisons with a surgical quality standard. Since its inception, the ACS NSQIP has worked to refine surgical outcomes measurements and enhance statistical methods to improve the reliability and validity of this hospital profiling. From an original focus on controlling for between-hospital differences in patient risk factors with logistic regression, ACS NSQIP has added a variable to better adjust for the complexity and risk profile of surgical procedures (procedure mix adjustment) and stabilized estimates derived from small samples by using a hierarchical model with shrinkage adjustment. New models have been developed focusing on specific surgical procedures (eg, "Procedure Targeted" models), which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment. In addition, comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance. Finally, procedures have been developed to estimate surgical risk for individual patients. This article describes the development of, and justification for, these new statistical methods and reporting strategies in ACS NSQIP. Copyright © 2013 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Quantitation of active pharmaceutical ingredients and excipients in powder blends using designed multivariate calibration models by near-infrared spectroscopy.

PubMed

Li, Weiyong; Worosila, Gregory D

2005-05-13

This research note demonstrates the simultaneous quantitation of a pharmaceutical active ingredient and three excipients in a simulated powder blend containing acetaminophen, Prosolv and Crospovidone. An experimental design approach was used in generating a 5-level (%, w/w) calibration sample set that included 125 samples. The samples were prepared by weighing suitable amount of powders into separate 20-mL scintillation vials and were mixed manually. Partial least squares (PLS) regression was used in calibration model development. The models generated accurate results for quantitation of Crospovidone (at 5%, w/w) and magnesium stearate (at 0.5%, w/w). Further testing of the models demonstrated that the 2-level models were as effective as the 5-level ones, which reduced the calibration sample number to 50. The models had a small bias for quantitation of acetaminophen (at 30%, w/w) and Prosolv (at 64.5%, w/w) in the blend. The implication of the bias is discussed.

Downscaling reanalysis data to high-resolution variables above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Mölg, Thomas; Marzeion, Ben; Kaser, Georg

2010-05-01

Recently initiated observation networks in the Cordillera Blanca provide temporally high-resolution, yet short-term atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly NCEP/NCAR reanalysis data to the local target variables, measured at the tropical glacier Artesonraju (Northern Cordillera Blanca). The approach is particular in the context of ESD for two reasons. First, the observational time series for model calibration are short (only about two years). Second, unlike most ESD studies in climate research, we focus on variables at a high temporal resolution (i.e., six-hourly values). Our target variables are two important drivers in the surface energy balance of tropical glaciers; air temperature and specific humidity. The selection of predictor fields from the reanalysis data is based on regression analyses and climatologic considerations. The ESD modelling procedure includes combined empirical orthogonal function and multiple regression analyses. Principal component screening is based on cross-validation using the Akaike Information Criterion as model selection criterion. Double cross-validation is applied for model evaluation. Potential autocorrelation in the time series is considered by defining the block length in the resampling procedure. Apart from the selection of predictor fields, the modelling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice by using both single- and mixed-field predictors of the variables air temperature (1000 hPa), specific humidity (1000 hPa), and zonal wind speed (500 hPa). The chosen downscaling domain ranges from 80 to 50 degrees west and from 0 to 20 degrees south. Statistical transfer functions are derived individually for different months and times of day (month/hour-models). The forecast skill of the month/hour-models largely depends on month and time of day, ranging from 0 to 0.8, but the mixed-field predictors generally perform better than the single-field predictors. At all time scales, the ESD model shows added value against two simple reference models; (i) the direct use of reanalysis grid point values, and (ii) mean diurnal and seasonal cycles over the calibration period. The ESD model forecast 1960 to 2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation, but is sensitive to the chosen predictor type. So far, we have not assessed the performance of NCEP/NCAR reanalysis data against other reanalysis products. The developed ESD model is computationally cheap and applicable wherever measurements are available for model calibration.

Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures.

PubMed

Bobb, Jennifer F; Valeri, Linda; Claus Henn, Birgit; Christiani, David C; Wright, Robert O; Mazumdar, Maitreyi; Godleski, John J; Coull, Brent A

2015-07-01

Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency. However, most health effects studies focus on single agents or consider simple two-way interaction models, in part because we lack the statistical methodology to more realistically capture the complexity of mixed exposures. We introduce Bayesian kernel machine regression (BKMR) as a new approach to study mixtures, in which the health outcome is regressed on a flexible function of the mixture (e.g. air pollution or toxic waste) components that is specified using a kernel function. In high-dimensional settings, a novel hierarchical variable selection approach is incorporated to identify important mixture components and account for the correlated structure of the mixture. Simulation studies demonstrate the success of BKMR in estimating the exposure-response function and in identifying the individual components of the mixture responsible for health effects. We demonstrate the features of the method through epidemiology and toxicology applications. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Daily Kilometer-Scale MODIS Satellite Maps of PM2.5 Describe Wintertime Episodes

NASA Technical Reports Server (NTRS)

Chatfield, Robert B.; Sorek Hamer, Meytar; Lyapustin, Alexei; Wang, Yujie

2017-01-01

The San Joaquin Valley (SJV) suffers from severe health-endangering episodes of PM2.5 aerosol loadings in wintertime; episodes last approximately 5 days and differ in geographical distribution and composition. PM2.5 stations are scattered; consequently the use of remote sensing to map variable regional patterns of these varying respirable aerosol concentrations is desirable. High-precision AOT retrievals can capture column particulate loading. However,PM2.5 mapping is challenging due to several reasons: particularly thin mixed layers (ML) and thus relatively low aerosol optical thickness (AOT) close to current measurement limits, variable and a typical composition of the aerosols, and complex surface bidirectional reflectance. However, the West does present some advantages in analysis. Air basins are isolated from long-distance transport, and experience predominant strong meteorological subsidence. Thus these Western basin regions have fewer problematic cases of overriding aerosol layers detached from the surface. To counter such local overriding, Chu et al. have described an approach for the Eastern US, and He et al have described a synoptic classification approach useful in Shanghai. The Bay Area Air Quality Management District (BAAQMD) expands our experience with the use of AOT, with lower PM2.5 and several isolated sub-basins. We have prepared daily maps of episodes in each region. We present also a sequence of increasingly detailed statistical models, AOT initially appears to contribute little information; however, inclusion of weather information reveals its utility. Lyapustin and Wang's MultiAngle Implementation of Atmospheric Correction (MAIAC) retrieval for AOT provided the most useful operational remote sensing information for these regions. It provides high (1-km) spatial resolution maps and a high percentage of availability. Empirical regression methods have found that random effects regression models (aka mixed effects models, ME) employing AOT provide good estimates of ground PM2.5 concentrations.Here, we attempt to extend these methods and evaluate the usefulness of AOT with greater physical analysis, based on DISCOVER-AQ4 experience.

Evaluating large-scale propensity score performance through real-world and synthetic data experiments.

PubMed

Tian, Yuxi; Schuemie, Martijn J; Suchard, Marc A

2018-06-22

Propensity score adjustment is a popular approach for confounding control in observational studies. Reliable frameworks are needed to determine relative propensity score performance in large-scale studies, and to establish optimal propensity score model selection methods. We detail a propensity score evaluation framework that includes synthetic and real-world data experiments. Our synthetic experimental design extends the 'plasmode' framework and simulates survival data under known effect sizes, and our real-world experiments use a set of negative control outcomes with presumed null effect sizes. In reproductions of two published cohort studies, we compare two propensity score estimation methods that contrast in their model selection approach: L1-regularized regression that conducts a penalized likelihood regression, and the 'high-dimensional propensity score' (hdPS) that employs a univariate covariate screen. We evaluate methods on a range of outcome-dependent and outcome-independent metrics. L1-regularization propensity score methods achieve superior model fit, covariate balance and negative control bias reduction compared with the hdPS. Simulation results are mixed and fluctuate with simulation parameters, revealing a limitation of simulation under the proportional hazards framework. Including regularization with the hdPS reduces commonly reported non-convergence issues but has little effect on propensity score performance. L1-regularization incorporates all covariates simultaneously into the propensity score model and offers propensity score performance superior to the hdPS marginal screen.

An anatomy of the projected North Atlantic warming hole in CMIP5 models

NASA Astrophysics Data System (ADS)

Menary, Matthew B.; Wood, Richard A.

2018-04-01

Global mean surface air temperature has increased over the past century and climate models project this trend to continue. However, the pattern of change is not homogeneous. Of particular interest is the subpolar North Atlantic, which has cooled in recent years and is projected to continue to warm less rapidly than the global mean. This is often termed the North Atlantic warming hole (WH). In climate model projections, the development of the WH is concomitant with a weakening of the Atlantic meridional overturning circulation (AMOC). Here, we further investigate the possible link between the AMOC and WH and the competing drivers of vertical mixing and surface heat fluxes. Across a large ensemble of 41 climate models we find that the spatial structure of the WH varies considerably from model to model but is generally upstream of the simulated deep water formation regions. A heat budget analysis suggests the formation of the WH is related to changes in ocean heat transport. Although the models display a plethora of AMOC mean states, they generally predict a weakening and shallowing of the AMOC also consistent with the evolving depth structure of the WH. A lagged regression analysis during the WH onset phase suggests that reductions in wintertime mixing lead a weakening of the AMOC by 5 years in turn leading initiation of the WH by 5 years. Inter-model differences in the evolution and structure of the WH are likely to lead to somewhat different projected climate impacts in nearby Europe and North America.

A Two-Step Approach for Analysis of Nonignorable Missing Outcomes in Longitudinal Regression: an Application to Upstate KIDS Study.

PubMed

Liu, Danping; Yeung, Edwina H; McLain, Alexander C; Xie, Yunlong; Buck Louis, Germaine M; Sundaram, Rajeshwari

2017-09-01

Imperfect follow-up in longitudinal studies commonly leads to missing outcome data that can potentially bias the inference when the missingness is nonignorable; that is, the propensity of missingness depends on missing values in the data. In the Upstate KIDS Study, we seek to determine if the missingness of child development outcomes is nonignorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism. To correct for nonignorable missingness, the shared random effects model (SREM) jointly models the outcome and the missing mechanism. However, the computational complexity and lack of software packages has limited its practical applications. This paper proposes a novel two-step approach to handle nonignorable missing outcomes in generalized linear mixed models. We first analyse the missing mechanism with a generalized linear mixed model and predict values of the random effects; then, the outcome model is fitted adjusting for the predicted random effects to account for heterogeneity in the missingness propensity. Extensive simulation studies suggest that the proposed method is a reliable approximation to SREM, with a much faster computation. The nonignorability of missing data in the Upstate KIDS Study is estimated to be mild to moderate, and the analyses using the two-step approach or SREM are similar to the model assuming ignorable missingness. The two-step approach is a computationally straightforward method that can be conducted as sensitivity analyses in longitudinal studies to examine violations to the ignorable missingness assumption and the implications relative to health outcomes. © 2017 John Wiley & Sons Ltd.

Risk adjustment in the American College of Surgeons National Surgical Quality Improvement Program: a comparison of logistic versus hierarchical modeling.

PubMed

Cohen, Mark E; Dimick, Justin B; Bilimoria, Karl Y; Ko, Clifford Y; Richards, Karen; Hall, Bruce Lee

2009-12-01

Although logistic regression has commonly been used to adjust for risk differences in patient and case mix to permit quality comparisons across hospitals, hierarchical modeling has been advocated as the preferred methodology, because it accounts for clustering of patients within hospitals. It is unclear whether hierarchical models would yield important differences in quality assessments compared with logistic models when applied to American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) data. Our objective was to evaluate differences in logistic versus hierarchical modeling for identifying hospitals with outlying outcomes in the ACS-NSQIP. Data from ACS-NSQIP patients who underwent colorectal operations in 2008 at hospitals that reported at least 100 operations were used to generate logistic and hierarchical prediction models for 30-day morbidity and mortality. Differences in risk-adjusted performance (ratio of observed-to-expected events) and outlier detections from the two models were compared. Logistic and hierarchical models identified the same 25 hospitals as morbidity outliers (14 low and 11 high outliers), but the hierarchical model identified 2 additional high outliers. Both models identified the same eight hospitals as mortality outliers (five low and three high outliers). The values of observed-to-expected events ratios and p values from the two models were highly correlated. Results were similar when data were permitted from hospitals providing < 100 patients. When applied to ACS-NSQIP data, logistic and hierarchical models provided nearly identical results with respect to identification of hospitals' observed-to-expected events ratio outliers. As hierarchical models are prone to implementation problems, logistic regression will remain an accurate and efficient method for performing risk adjustment of hospital quality comparisons.

Nursing home performance under case-mix reimbursement: responding to heavy-care incentives and market changes.

PubMed Central

Davis, M A; Freeman, J W; Kirby, E C

1998-01-01

OBJECTIVE: To examine the effect of case mix-adjusted reimbursement policy and market factors on nursing home performance. DATA SOURCES AND STUDY SETTING: Data from Medicaid certification inspection surveys, Medicaid cost reports, and the Kentucky State Center for Health Statistics for the years 1989 and 1991, to examine changes in nursing home performance stemming from the adoption of case mix-adjusted reimbursement in 1990. STUDY DESIGN: In addition to cross-sectional regressions, a first-difference approach to fixed-effects regression analyses was employed to control for facility differences that were essentially fixed during the survey years and to estimate the effects of time-varying predictors on changes in facility expenditures, efficiency, and profitability. PRINCIPAL FINDINGS: Facilities that increased the proportion of Medicaid residents and eliminated excess capacity experienced higher profitability gains during the beginning phase of case-mix reimbursement. Having a heavy-care resident population was positively related to expenditures prior to reimbursement reform, and it was negatively related to expenditures after the case-mix reimbursement policy was introduced. While facility-level changes in case mix had no reliable influence on costs or profits, nursing homes showing an increased prevalence of poor-quality nursing practices exhibited increases in efficiency and profitability. At the market level, reductions in excess or empty nursing home beds were accompanied by a significant growth in home health services. Moreover, nursing homes located in markets with expanding home health services exhibited higher increases in costs per case-mix unit. CONCLUSIONS: Characteristics of the reimbursement system appear to reward a cost minimization orientation with potentially detrimental effects on quality of care. These effects, exacerbated by a supply-constrained market, may be mitigated by policies that encourage the expansion of home health service availability. PMID:9776938

Nursing home performance under case-mix reimbursement: responding to heavy-care incentives and market changes.

PubMed

Davis, M A; Freeman, J W; Kirby, E C

1998-10-01

To examine the effect of case mix-adjusted reimbursement policy and market factors on nursing home performance. Data from Medicaid certification inspection surveys, Medicaid cost reports, and the Kentucky State Center for Health Statistics for the years 1989 and 1991, to examine changes in nursing home performance stemming from the adoption of case mix-adjusted reimbursement in 1990. In addition to cross-sectional regressions, a first-difference approach to fixed-effects regression analyses was employed to control for facility differences that were essentially fixed during the survey years and to estimate the effects of time-varying predictors on changes in facility expenditures, efficiency, and profitability. Facilities that increased the proportion of Medicaid residents and eliminated excess capacity experienced higher profitability gains during the beginning phase of case-mix reimbursement. Having a heavy-care resident population was positively related to expenditures prior to reimbursement reform, and it was negatively related to expenditures after the case-mix reimbursement policy was introduced. While facility-level changes in case mix had no reliable influence on costs or profits, nursing homes showing an increased prevalence of poor-quality nursing practices exhibited increases in efficiency and profitability. At the market level, reductions in excess or empty nursing home beds were accompanied by a significant growth in home health services. Moreover, nursing homes located in markets with expanding home health services exhibited higher increases in costs per case-mix unit. Characteristics of the reimbursement system appear to reward a cost minimization orientation with potentially detrimental effects on quality of care. These effects, exacerbated by a supply-constrained market, may be mitigated by policies that encourage the expansion of home health service availability.

Turtle groups or turtle soup: dispersal patterns of hawksbill turtles in the Caribbean.

PubMed

Blumenthal, J M; Abreu-Grobois, F A; Austin, T J; Broderick, A C; Bruford, M W; Coyne, M S; Ebanks-Petrie, G; Formia, A; Meylan, P A; Meylan, A B; Godley, B J

2009-12-01

Despite intense interest in conservation of marine turtles, spatial ecology during the oceanic juvenile phase remains relatively unknown. Here, we used mixed stock analysis and examination of oceanic drift to elucidate movements of hawksbill turtles (Eretmochelys imbricata) and address management implications within the Caribbean. Among samples collected from 92 neritic juvenile hawksbills in the Cayman Islands we detected 11 mtDNA control region haplotypes. To estimate contributions to the aggregation, we performed 'many-to-many' mixed stock analysis, incorporating published hawksbill genetic and population data. The Cayman Islands aggregation represents a diverse mixed stock: potentially contributing source rookeries spanned the Caribbean basin, delineating a scale of recruitment of 200-2500 km. As hawksbills undergo an extended phase of oceanic dispersal, ocean currents may drive patterns of genetic diversity observed on foraging aggregations. Therefore, using high-resolution Aviso ocean current data, we modelled movement of particles representing passively drifting oceanic juvenile hawksbills. Putative distribution patterns varied markedly by origin: particles from many rookeries were broadly distributed across the region, while others would appear to become entrained in local gyres. Overall, we detected a significant correlation between genetic profiles of foraging aggregations and patterns of particle distribution produced by a hatchling drift model (Mantel test, r = 0.77, P < 0.001; linear regression, r = 0.83, P < 0.001). Our results indicate that although there is a high degree of mixing across the Caribbean (a 'turtle soup'), current patterns play a substantial role in determining genetic structure of foraging aggregations (forming turtle groups). Thus, for marine turtles and other widely distributed marine species, integration of genetic and oceanographic data may enhance understanding of population connectivity and management requirements.

An investigation of the predictors of photoprotection and UVR dose to the face in patients with XP: a protocol using observational mixed methods

PubMed Central

Walburn, Jessica; Sarkany, Robert; Norton, Sam; Foster, Lesley; Morgan, Myfanwy; Sainsbury, Kirby; Araújo-Soares, Vera; Anderson, Rebecca; Garrood, Isabel; Heydenreich, Jakob; Sniehotta, Falko F; Vieira, Rute; Wulf, Hans Christian; Weinman, John

2017-01-01

Introduction Xeroderma pigmentosum (XP) is a rare genetic condition caused by defective nucleotide excision repair and characterised by skin cancer, ocular and neurological involvement. Stringent ultraviolet protection is the only way to prevent skin cancer. Despite the risks, some patients’ photoprotection is poor, with a potentially devastating impact on their prognosis. The aim of this research is to identify disease-specific and psychosocial predictors of photoprotection behaviour and ultraviolet radiation (UVR) dose to the face. Methods and analysis Mixed methods research based on 45 UK patients will involve qualitative interviews to identify individuals’ experience of XP and the influences on their photoprotection behaviours and a cross-sectional quantitative survey to assess biopsychosocial correlates of these behaviours at baseline. This will be followed by objective measurement of UVR exposure for 21 days by wrist-worn dosimeter and daily recording of photoprotection behaviours and psychological variables for up to 50 days in the summer months. This novel methodology will enable UVR dose reaching the face to be calculated and analysed as a clinically relevant endpoint. A range of qualitative and quantitative analytical approaches will be used, reflecting the mixed methods (eg, cross-sectional qualitative interviews, n-of-1 studies). Framework analysis will be used to analyse the qualitative interviews; mixed-effects longitudinal models will be used to examine the association of clinical and psychosocial factors with the average daily UVR dose; dynamic logistic regression models will be used to investigate participant-specific psychosocial factors associated with photoprotection behaviours. Ethics and dissemination This research has been approved by Camden and King’s Cross Research Ethics Committee 15/LO/1395. The findings will be published in peer-reviewed journals and presented at national and international scientific conferences. PMID:28827277

Effects of photochemical oxidation on the mixing state and light absorption of black carbon in the urban atmosphere of China

NASA Astrophysics Data System (ADS)

Wang, Qiyuan; Huang, Rujin; Zhao, Zhuzi; Cao, Junji; Ni, Haiyan; Tie, Xuexi; Zhu, Chongshu; Shen, Zhenxing; Wang, Meng; Dai, Wenting; Han, Yongming; Zhang, Ningning; Prévôt, André S. H.

2017-04-01

The relationship between the refractory black carbon (rBC) aerosol mixing state and the atmospheric oxidation capacity was investigated to assess the possible influence of oxidants on the particles’ light absorption effects at two large cities in China. The number fraction of thickly-coated rBC particles (F rBC) was positively correlated with a measure of the oxidant concentrations (OX = O3 + NO2), indicating an enhancement of coated rBC particles under more oxidizing conditions. The slope of a linear regression of F rBC versus OX was 0.58% ppb-1 for Beijing and 0.84% ppb-1 for Xi’an, and these relationships provide some insights into the evolution of rBC mixing state in relation to atmospheric oxidation processes. The mass absorption cross-section of rBC (MACrBC) increased with OX during the daytime at Xi’an, at a rate of 0.26 m2 g-1 ppb-1, suggesting that more oxidizing conditions lead to internal mixing that enhances the light-absorbing capacity of rBC particles. Understanding the dependence of the increasing rates of F rBC and MACrBC as a function of OX may lead to improvements of climate models that deal with the warming effects, but more studies in different cities and seasons are needed to gauge the broader implications of these findings.

Neighborhood income and major depressive disorder in a large Dutch population: results from the LifeLines Cohort study.

PubMed

Klijs, Bart; Kibele, Eva U B; Ellwardt, Lea; Zuidersma, Marij; Stolk, Ronald P; Wittek, Rafael P M; Mendes de Leon, Carlos M; Smidt, Nynke

2016-08-11

Previous studies are inconclusive on whether poor socioeconomic conditions in the neighborhood are associated with major depressive disorder. Furthermore, conceptual models that relate neighborhood conditions to depressive disorder have not been evaluated using empirical data. In this study, we investigated whether neighborhood income is associated with major depressive episodes. We evaluated three conceptual models. Conceptual model 1: The association between neighborhood income and major depressive episodes is explained by diseases, lifestyle factors, stress and social participation. Conceptual model 2: A low individual income relative to the mean income in the neighborhood is associated with major depressive episodes. Conceptual model 3: A high income of the neighborhood buffers the effect of a low individual income on major depressive disorder. We used adult baseline data from the LifeLines Cohort Study (N = 71,058) linked with data on the participants' neighborhoods from Statistics Netherlands. The current presence of a major depressive episode was assessed using the MINI neuropsychiatric interview. The association between neighborhood income and major depressive episodes was assessed using a mixed effect logistic regression model adjusted for age, sex, marital status, education and individual (equalized) income. This regression model was sequentially adjusted for lifestyle factors, chronic diseases, stress, and social participation to evaluate conceptual model 1. To evaluate conceptual models 2 and 3, an interaction term for neighborhood income*individual income was included. Multivariate regression analysis showed that a low neighborhood income is associated with major depressive episodes (OR (95 % CI): 0.82 (0.73;0.93)). Adjustment for diseases, lifestyle factors, stress, and social participation attenuated this association (ORs (95 % CI): 0.90 (0.79;1.01)). Low individual income was also associated with major depressive episodes (OR (95 % CI): 0.72 (0.68;0.76)). The interaction of individual income*neighborhood income on major depressive episodes was not significant (p = 0.173). Living in a low-income neighborhood is associated with major depressive episodes. Our results suggest that this association is partly explained by chronic diseases, lifestyle factors, stress and poor social participation, and thereby partly confirm conceptual model 1. Our results do not support conceptual model 2 and 3.

Is Private Production of Public Services Cheaper Than Public Production? A Meta-Regression Analysis of Solid Waste and Water Services

ERIC Educational Resources Information Center

Bel, Germa; Fageda, Xavier; Warner, Mildred E.

2010-01-01

Privatization of local government services is assumed to deliver cost savings, but empirical evidence for this from around the world is mixed. We conduct a meta-regression analysis of all econometric studies examining privatization of water distribution and solid waste collection services and find no systematic support for lower costs with private…

The impact of green stormwater infrastructure installation on surrounding health and safety.

PubMed

Kondo, Michelle C; Low, Sarah C; Henning, Jason; Branas, Charles C

2015-03-01

We investigated the health and safety effects of urban green stormwater infrastructure (GSI) installments. We conducted a difference-in-differences analysis of the effects of GSI installments on health (e.g., blood pressure, cholesterol and stress levels) and safety (e.g., felonies, nuisance and property crimes, narcotics crimes) outcomes from 2000 to 2012 in Philadelphia, Pennsylvania. We used mixed-effects regression models to compare differences in pre- and posttreatment measures of outcomes for treatment sites (n=52) and randomly chosen, matched control sites (n=186) within multiple geographic extents surrounding GSI sites. Regression-adjusted models showed consistent and statistically significant reductions in narcotics possession (18%-27% less) within 16th-mile, quarter-mile, half-mile (P<.001), and eighth-mile (P<.01) distances from treatment sites and at the census tract level (P<.01). Narcotics manufacture and burglaries were also significantly reduced at multiple scales. Nonsignificant reductions in homicides, assaults, thefts, public drunkenness, and narcotics sales were associated with GSI installation in at least 1 geographic extent. Health and safety considerations should be included in future assessments of GSI programs. Subsequent studies should assess mechanisms of this association.

The Impact of Green Stormwater Infrastructure Installation on Surrounding Health and Safety

PubMed Central

Low, Sarah C.; Henning, Jason; Branas, Charles C.

2015-01-01

Objectives. We investigated the health and safety effects of urban green stormwater infrastructure (GSI) installments. Methods. We conducted a difference-in-differences analysis of the effects of GSI installments on health (e.g., blood pressure, cholesterol and stress levels) and safety (e.g., felonies, nuisance and property crimes, narcotics crimes) outcomes from 2000 to 2012 in Philadelphia, Pennsylvania. We used mixed-effects regression models to compare differences in pre- and posttreatment measures of outcomes for treatment sites (n = 52) and randomly chosen, matched control sites (n = 186) within multiple geographic extents surrounding GSI sites. Results. Regression-adjusted models showed consistent and statistically significant reductions in narcotics possession (18%–27% less) within 16th-mile, quarter-mile, half-mile (P < .001), and eighth-mile (P < .01) distances from treatment sites and at the census tract level (P < .01). Narcotics manufacture and burglaries were also significantly reduced at multiple scales. Nonsignificant reductions in homicides, assaults, thefts, public drunkenness, and narcotics sales were associated with GSI installation in at least 1 geographic extent. Conclusions. Health and safety considerations should be included in future assessments of GSI programs. Subsequent studies should assess mechanisms of this association. PMID:25602887

Aerodynamic parameters of High-Angle-of attack Research Vehicle (HARV) estimated from flight data

NASA Technical Reports Server (NTRS)

Klein, Vladislav; Ratvasky, Thomas R.; Cobleigh, Brent R.

1990-01-01

Aerodynamic parameters of the High-Angle-of-Attack Research Aircraft (HARV) were estimated from flight data at different values of the angle of attack between 10 degrees and 50 degrees. The main part of the data was obtained from small amplitude longitudinal and lateral maneuvers. A small number of large amplitude maneuvers was also used in the estimation. The measured data were first checked for their compatibility. It was found that the accuracy of air data was degraded by unexplained bias errors. Then, the data were analyzed by a stepwise regression method for obtaining a structure of aerodynamic model equations and least squares parameter estimates. Because of high data collinearity in several maneuvers, some of the longitudinal and all lateral maneuvers were reanalyzed by using two biased estimation techniques, the principal components regression and mixed estimation. The estimated parameters in the form of stability and control derivatives, and aerodynamic coefficients were plotted against the angle of attack and compared with the wind tunnel measurements. The influential parameters are, in general, estimated with acceptable accuracy and most of them are in agreement with wind tunnel results. The simulated responses of the aircraft showed good prediction capabilities of the resulting model.

The Importance of Specific Workplace Environment Characteristics for Maximum Health and Performance: Healthcare Workers' Perspective.

PubMed

Sagha Zadeh, Rana; Shepley, Mardelle M; Owora, Arthur Hamie; Dannenbaum, Martha C; Waggener, Laurie T; Chung, Susan Sung Eun

2018-05-01

To examine the importance of specific workplace environment characteristics for maximum health and performance, assigned by healthcare employees, and how they relate to the nature of their work. A cross-sectional mixed-method study was conducted with content analysis and robust regression models to examine the relationship between workplace environment characteristics and perceived importance in promoting health and performance. Our findings suggest that perceptions of key environment characteristics that safeguard health and performance in healthcare workplaces may vary by employee sex, setting, and nature of healthcare work involved. Theme and model descriptions of the influence of these factors on participant perceptions are provided. Employee feedback on workplace characteristics that impact health and performance could be instrumental in determining the priorities of workplace design.

Inferential Processing among Adequate and Struggling Adolescent Comprehenders and Relations to Reading Comprehension

PubMed Central

Barth, Amy E.; Barnes, Marcia; Francis, David J.; Vaughn, Sharon; York, Mary

2015-01-01

Separate mixed model analyses of variance (ANOVA) were conducted to examine the effect of textual distance on the accuracy and speed of text consistency judgments among adequate and struggling comprehenders across grades 6–12 (n = 1203). Multiple regressions examined whether accuracy in text consistency judgments uniquely accounted for variance in comprehension. Results suggest that there is considerable growth across the middle and high school years, particularly for adequate comprehenders in those text integration processes that maintain local coherence. Accuracy in text consistency judgments accounted for significant unique variance for passage-level, but not sentence-level comprehension, particularly for adequate comprehenders. PMID:26166946

Oil Extraction and Indigenous Livelihoods in the Northern Ecuadorian Amazon

PubMed Central

Bozigar, Matthew; Gray, Clark L.; Bilsborrow, Richard E.

2015-01-01

Globally, the extraction of minerals and fossil fuels is increasingly penetrating into isolated regions inhabited by indigenous peoples, potentially undermining their livelihoods and well-being. To provide new insight to this issue, we draw on a unique longitudinal dataset collected in the Ecuadorian Amazon over an 11-year period from 484 indigenous households with varying degrees of exposure to oil extraction. Fixed and random effects regression models of the consequences of oil activities for livelihood outcomes reveal mixed and multidimensional effects. These results challenge common assumptions about these processes and are only partly consistent with hypotheses drawn from the Dutch disease literature. PMID:26543302

Association Between Cardiovascular and Intraocular Pressure Changes in a 14-day 6 deg Head Down Tilt (HDT) Bed Rest Study: Possible Implications in Retinal Anatomy

NASA Technical Reports Server (NTRS)

Cromwell, R. L.; Zanello, S. B.; Yarbough, P. O.; Ploutz-Snyder, R.; Taibbi, G.; Brewer, J. L.; Vizzeri, G.

2013-01-01

Mean IOP significantly increased while at 6deg HDT and returned towards pre-bed rest values upon leaving bed rest. While mean IOP increased during bed rest, it remained within the normal limits for subject safety. A diuretic shift and cardiovascular deconditioning occurs during in-bed rest, as expected. There was no demonstrable correlation between the largest change in IOP (pre/post) and cardiovascular measure changes (pre/post). Additional mixed effects linear regression modeling may reveal some subclinical physiological changes that might assist in describing the VIIP syndrome pathophysiology.

A mixing timescale model for TPDF simulations of turbulent premixed flames

DOE PAGES

Kuron, Michael; Ren, Zhuyin; Hawkes, Evatt R.; ...

2017-02-06

Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micro-mixing needs to be modeled and this modeling is considered a primary challenge for TPDF methods. In the present study, a new algebraic mixing rate model for TPDF simulations of turbulent premixed flames is proposed, which is a key ingredient in commonly used molecular mixing models. The new model aims to properly account for the transition in reactive scalar mixing rate behavior from the limit of turbulence-dominated mixing to molecular mixing behavior in flamelets. An a priorimore » assessment of the new model is performed using direct numerical simulation (DNS) data of a lean premixed hydrogen–air jet flame. The new model accurately captures the mixing timescale behavior in the DNS and is found to be a significant improvement over the commonly used constant mechanical-to-scalar mixing timescale ratio model. An a posteriori TPDF study is then performed using the same DNS data as a numerical test bed. The DNS provides the initial conditions and time-varying input quantities, including the mean velocity, turbulent diffusion coefficient, and modeled scalar mixing rate for the TPDF simulations, thus allowing an exclusive focus on the mixing model. Here, the new mixing timescale model is compared with the constant mechanical-to-scalar mixing timescale ratio coupled with the Euclidean Minimum Spanning Tree (EMST) mixing model, as well as a laminar flamelet closure. It is found that the laminar flamelet closure is unable to properly capture the mixing behavior in the thin reaction zones regime while the constant mechanical-to-scalar mixing timescale model under-predicts the flame speed. Furthermore, the EMST model coupled with the new mixing timescale model provides the best prediction of the flame structure and flame propagation among the models tested, as the dynamics of reactive scalar mixing across different flame regimes are appropriately accounted for.« less

A mixing timescale model for TPDF simulations of turbulent premixed flames

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

Kuron, Michael; Ren, Zhuyin; Hawkes, Evatt R.

Transported probability density function (TPDF) methods are an attractive modeling approach for turbulent flames as chemical reactions appear in closed form. However, molecular micro-mixing needs to be modeled and this modeling is considered a primary challenge for TPDF methods. In the present study, a new algebraic mixing rate model for TPDF simulations of turbulent premixed flames is proposed, which is a key ingredient in commonly used molecular mixing models. The new model aims to properly account for the transition in reactive scalar mixing rate behavior from the limit of turbulence-dominated mixing to molecular mixing behavior in flamelets. An a priorimore » assessment of the new model is performed using direct numerical simulation (DNS) data of a lean premixed hydrogen–air jet flame. The new model accurately captures the mixing timescale behavior in the DNS and is found to be a significant improvement over the commonly used constant mechanical-to-scalar mixing timescale ratio model. An a posteriori TPDF study is then performed using the same DNS data as a numerical test bed. The DNS provides the initial conditions and time-varying input quantities, including the mean velocity, turbulent diffusion coefficient, and modeled scalar mixing rate for the TPDF simulations, thus allowing an exclusive focus on the mixing model. Here, the new mixing timescale model is compared with the constant mechanical-to-scalar mixing timescale ratio coupled with the Euclidean Minimum Spanning Tree (EMST) mixing model, as well as a laminar flamelet closure. It is found that the laminar flamelet closure is unable to properly capture the mixing behavior in the thin reaction zones regime while the constant mechanical-to-scalar mixing timescale model under-predicts the flame speed. Furthermore, the EMST model coupled with the new mixing timescale model provides the best prediction of the flame structure and flame propagation among the models tested, as the dynamics of reactive scalar mixing across different flame regimes are appropriately accounted for.« less

The effects of climate change on harp seals (Pagophilus groenlandicus).

PubMed

Johnston, David W; Bowers, Matthew T; Friedlaender, Ari S; Lavigne, David M

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data.

The Effects of Climate Change on Harp Seals (Pagophilus groenlandicus)

PubMed Central

Johnston, David W.; Bowers, Matthew T.; Friedlaender, Ari S.; Lavigne, David M.

2012-01-01

Harp seals (Pagophilus groenlandicus) have evolved life history strategies to exploit seasonal sea ice as a breeding platform. As such, individuals are prepared to deal with fluctuations in the quantity and quality of ice in their breeding areas. It remains unclear, however, how shifts in climate may affect seal populations. The present study assesses the effects of climate change on harp seals through three linked analyses. First, we tested the effects of short-term climate variability on young-of-the year harp seal mortality using a linear regression of sea ice cover in the Gulf of St. Lawrence against stranding rates of dead harp seals in the region during 1992 to 2010. A similar regression of stranding rates and North Atlantic Oscillation (NAO) index values was also conducted. These analyses revealed negative correlations between both ice cover and NAO conditions and seal mortality, indicating that lighter ice cover and lower NAO values result in higher mortality. A retrospective cross-correlation analysis of NAO conditions and sea ice cover from 1978 to 2011 revealed that NAO-related changes in sea ice may have contributed to the depletion of seals on the east coast of Canada during 1950 to 1972, and to their recovery during 1973 to 2000. This historical retrospective also reveals opposite links between neonatal mortality in harp seals in the Northeast Atlantic and NAO phase. Finally, an assessment of the long-term trends in sea ice cover in the breeding regions of harp seals across the entire North Atlantic during 1979 through 2011 using multiple linear regression models and mixed effects linear regression models revealed that sea ice cover in all harp seal breeding regions has been declining by as much as 6 percent per decade over the time series of available satellite data. PMID:22238591

Freedom Solo Versus Trifecta Bioprotheses: Clinical and Hemodynamic Evaluation after Propensity Score Matching.

PubMed

J Cerqueira, Rui; Melo, Renata; Moreira, Soraia; A Saraiva, Francisca; Andrade, Marta; Salgueiro, Elson; Almeida, Jorge; J Amorim, Mário; Pinho, Paulo; Lourenço, André; F Leite-Moreira, Adelino

2017-01-01

To compare stentless Freedom Solo and stented Trifecta aortic bioprostheses regarding hemodynamic profile, left ventricular mass regression, early and late postoperative outcomes and survival. Longitudinal cohort study of consecutive patients undergoing aortic valve replacement (from 2009 to 2016) with either Freedom Solo or Trifecta at one centre. Local databases and national records were queried. Postoperative echocardiography (3-6 months) was obtained for hemodynamic profile (mean transprosthetic gradient and effective orifice area) and left ventricle mass determination. After propensity score matching (21 covariates), Kaplan-Meier analysis and cumulative incidence analysis were performed for survival and combined outcome of structural valve deterioration and endocarditis, respectively. Hemodynamics and left ventricle mass regression were assessed by a mixed- -effects model including propensity score as a covariate. From a total sample of 397 Freedom Solo and 525 Trifecta patients with a median follow-up time of 4.0 (2.2- 6.0) and 2.4 (1.4-3.7) years, respectively, a matched sample of 329 pairs was obtained. Well-balanced matched groups showed no difference in survival (hazard ratio=1.04, 95% confidence interval=0.69-1.56) or cumulative hazards of combined outcome (subhazard ratio=0.54, 95% confidence interval=0.21-1.39). Although Trifecta showed improved hemodynamic profile compared to Freedom Solo, no differences were found in left ventricle mass regression. Trifecta has a slightly improved hemodynamic profile compared to Freedom Solo but this does not translate into differences in the extent of mass regression, postoperative outcomes or survival, which were good and comparable for both bioprostheses. Long-term follow-up is needed for comparisons with older models of bioprostheses.

Modelling daily PM2.5 concentrations at high spatio-temporal resolution across Switzerland.

PubMed

de Hoogh, Kees; Héritier, Harris; Stafoggia, Massimo; Künzli, Nino; Kloog, Itai

2018-02-01

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM 2.5 ) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM 2.5 monitoring data was supplemented by imputing PM 2.5 concentrations at PM 10 sites, using PM 2.5 /PM 10 ratios at co-located sites. Daily PM 2.5 concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM 2.5 in cells with AOD but without PM 2.5 measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM 2.5 predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM 2.5 concentrations. Copyright © 2017 Elsevier Ltd. All rights reserved.

The relationship of California's Medicaid reimbursement system to nurse staffing levels.

PubMed

Mukamel, Dana B; Kang, Taewoon; Collier, Eric; Harrington, Charlene

2012-10-01

Policy initiatives at the Federal and state level are aimed at increasing staffing in nursing homes. These include direct staffing standards, public reporting, and financial incentives. To examine the impact of California's Medicaid reimbursement for nursing homes which includes incentives directed at staffing. Two-stage limited-information maximum-likelihood regressions were used to model the relationship between staffing [registered nurses (RNs), licensed practical nurses, and certified nursing assistants hours per resident day] and the Medicaid payment rate, accounting for the specific structure of the payment system, endogeneity of payment and case-mix, and controlling for facility and market characteristics. A total of 927 California free-standing nursing homes in 2006. The model included facility characteristics (case-mix, size, ownership, and chain affiliation), market competition and excess demand, labor supply and wages, unemployment, and female employment. The instrumental variable for Medicaid reimbursement was the peer group payment rate for 7 geographical market areas, and the instrumental variables for resident case-mix were the average county revenues for professional therapy establishments and the percent of county population aged 65 and over. Consistent with the rate incentives and rational expectation behavior, expected nursing home reimbursement rates in 2008 were associated with increased RN staffing levels in 2006 but had no relationship with licensed practical nurse and certified nursing assistant staffing. The effect was estimated at 2 minutes per $10 increase in rate. The incentives in the Medicaid system impacted only RN staffing suggesting the need to improve the state's rate setting methodology.

Child Maltreatment and Delinquency Onset Among African American Adolescent Males

PubMed Central

Williams, James Herbert; Van Dorn, Richard A.; Bright, Charlotte Lyn; Jonson-Reid, Melissa; Nebbitt, Von E.

2013-01-01

Child welfare and criminology research have increasingly sought to better understand factors that increase the likelihood that abused and neglected children will become involved in the juvenile justice system. However, few studies have addressed this relationship among African American male adolescents. The current study examines the relationship between child maltreatment (i.e., neglect, physical abuse, sexual abuse, and other/mixed abuse) and the likelihood of a delinquency petition using a sample of African American males (N = 2,335) born before 1990. Multivariable logistic regression models compared those with a delinquency-based juvenile justice petition to those without. Results indicate that African American males with a history of neglect, physical abuse, or other/mixed abuse were more likely to be involved in the juvenile justice system than those without any child maltreatment. Additionally, multiple maltreatment reports, a prior history of mental health treatment, victimization, and having a parent who did not complete high school also increased the likelihood of a delinquency petition. Implications for intervention and prevention are discussed. PMID:23730121

Determinants of performance failure in the nursing home industry☆

PubMed Central

Zinn, Jacqueline; Mor, Vincent; Feng, Zhanlian; Intrator, Orna

2013-01-01

This study investigates the determinants of performance failure in U.S. nursing homes. The sample consisted of 91,168 surveys from 10,901 facilities included in the Online Survey Certification and Reporting system from 1996 to 2005. Failed performance was defined as termination from the Medicare and Medicaid programs. Determinants of performance failure were identified as core structural change (ownership change), peripheral change (related diversification), prior financial and quality of care performance, size and environmental shock (Medicaid case mix reimbursement and prospective payment system introduction). Additional control variables that could contribute to the likelihood of performance failure were included in a cross-sectional time series generalized estimating equation logistic regression model. Our results support the contention, derived from structural inertia theory, that where in an organization’s structure change occurs determines whether it is adaptive or disruptive. In addition, while poor prior financial and quality performance and the introduction of case mix reimbursement increases the risk of failure, larger size is protective, decreasing the likelihood of performance failure. PMID:19128865

The Balanced Budget Act (1997) and the supplyof nursing home subacute care.

PubMed

Qaseem, Amir; Weech-Maldonado, Robert; Mkanta, William

2007-01-01

This article examines the impact of the Medicare prospective payment system (PPS) on the supply of subacute care services by nursing homes. A quasi-experimental interrupted time-series design using Heckman's two-stage regression model is employed to test for changes before and after the implementation of Medicare PPS. Our findings suggest that the change in Medicare reimbursement from cost-based to PPS under the Balanced Budget Act of 1997 resulted in a decrease of 1.7 percent in the supply of subacute care beds by nursing homes. However, this was a one-time, short-term negative effect. The supply of nursing home subacute care remained stable in the long-term. Other environmental factors, such as Medicare hospital discharges, hospital-based subacute care, Medicare managed care penetration, availability of home health, and per capita income were associated with nursing home subacute care supply. Organizational-level factors, such as occupancy rate, RN staff mix, and Medicare payer mix were also predictors of nursing home subacute care supply.

Determinants of performance failure in the nursing home industry.

PubMed

Zinn, Jacqueline; Mor, Vincent; Feng, Zhanlian; Intrator, Orna

2009-03-01

This study investigates the determinants of performance failure in U.S. nursing homes. The sample consisted of 91,168 surveys from 10,901 facilities included in the Online Survey Certification and Reporting system from 1996 to 2005. Failed performance was defined as termination from the Medicare and Medicaid programs. Determinants of performance failure were identified as core structural change (ownership change), peripheral change (related diversification), prior financial and quality of care performance, size and environmental shock (Medicaid case mix reimbursement and prospective payment system introduction). Additional control variables that could contribute to the likelihood of performance failure were included in a cross-sectional time series generalized estimating equation logistic regression model. Our results support the contention, derived from structural inertia theory, that where in an organization's structure change occurs determines whether it is adaptive or disruptive. In addition, while poor prior financial and quality performance and the introduction of case mix reimbursement increases the risk of failure, larger size is protective, decreasing the likelihood of performance failure.

Child Maltreatment and Delinquency Onset Among African American Adolescent Males.

PubMed

Williams, James Herbert; Van Dorn, Richard A; Bright, Charlotte Lyn; Jonson-Reid, Melissa; Nebbitt, Von E

2010-05-01

Child welfare and criminology research have increasingly sought to better understand factors that increase the likelihood that abused and neglected children will become involved in the juvenile justice system. However, few studies have addressed this relationship among African American male adolescents. The current study examines the relationship between child maltreatment (i.e., neglect, physical abuse, sexual abuse, and other/mixed abuse) and the likelihood of a delinquency petition using a sample of African American males ( N = 2,335) born before 1990. Multivariable logistic regression models compared those with a delinquency-based juvenile justice petition to those without. Results indicate that African American males with a history of neglect, physical abuse, or other/mixed abuse were more likely to be involved in the juvenile justice system than those without any child maltreatment. Additionally, multiple maltreatment reports, a prior history of mental health treatment, victimization, and having a parent who did not complete high school also increased the likelihood of a delinquency petition. Implications for intervention and prevention are discussed.

Choice and Constraint in the Negotiation of the Grandparent Role: A Mixed-Methods Study.

PubMed

McGarrigle, Christine A; Timonen, Virpi; Layte, Richard

2018-01-01

Few studies have examined how the allocation and consequences of grandchild care vary across different socioeconomic groups. We analyze qualitative data alongside data from The Irish Longitudinal Study on Ageing (TILDA), in a convergent mixed-methods approach. Regression models examined characteristics associated with grandchild care, and the relationship between grandchild care and depressive symptoms and well-being. Qualitative data shed light on processes and choices that explain patterns of grandchild care provision. Tertiary-educated grandparents provided less intensive grandchild care compared with primary educated. Qualitative data indicated that this pattern stems from early boundary-drawing among higher educated grandparents while lower socioeconomic groups were constrained and less able to say no. Intensive grandchild care was associated with more depressive symptoms and lower well-being and was moderated by participation in social activities and level of education attainment. The effect of grandchild care on well-being of grandparents depends on whether it is provided by choice or obligation.

Choice and Constraint in the Negotiation of the Grandparent Role: A Mixed-Methods Study

PubMed Central

McGarrigle, Christine A.; Timonen, Virpi; Layte, Richard

2018-01-01

Few studies have examined how the allocation and consequences of grandchild care vary across different socioeconomic groups. We analyze qualitative data alongside data from The Irish Longitudinal Study on Ageing (TILDA), in a convergent mixed-methods approach. Regression models examined characteristics associated with grandchild care, and the relationship between grandchild care and depressive symptoms and well-being. Qualitative data shed light on processes and choices that explain patterns of grandchild care provision. Tertiary-educated grandparents provided less intensive grandchild care compared with primary educated. Qualitative data indicated that this pattern stems from early boundary-drawing among higher educated grandparents while lower socioeconomic groups were constrained and less able to say no. Intensive grandchild care was associated with more depressive symptoms and lower well-being and was moderated by participation in social activities and level of education attainment. The effect of grandchild care on well-being of grandparents depends on whether it is provided by choice or obligation. PMID:29372176

Physiology-Based Modeling May Predict Surgical Treatment Outcome for Obstructive Sleep Apnea

PubMed Central

Li, Yanru; Ye, Jingying; Han, Demin; Cao, Xin; Ding, Xiu; Zhang, Yuhuan; Xu, Wen; Orr, Jeremy; Jen, Rachel; Sands, Scott; Malhotra, Atul; Owens, Robert

2017-01-01

Study Objectives: To test whether the integration of both anatomical and nonanatomical parameters (ventilatory control, arousal threshold, muscle responsiveness) in a physiology-based model will improve the ability to predict outcomes after upper airway surgery for obstructive sleep apnea (OSA). Methods: In 31 patients who underwent upper airway surgery for OSA, loop gain and arousal threshold were calculated from preoperative polysomnography (PSG). Three models were compared: (1) a multiple regression based on an extensive list of PSG parameters alone; (2) a multivariate regression using PSG parameters plus PSG-derived estimates of loop gain, arousal threshold, and other trait surrogates; (3) a physiological model incorporating selected variables as surrogates of anatomical and nonanatomical traits important for OSA pathogenesis. Results: Although preoperative loop gain was positively correlated with postoperative apnea-hypopnea index (AHI) (P = .008) and arousal threshold was negatively correlated (P = .011), in both model 1 and 2, the only significant variable was preoperative AHI, which explained 42% of the variance in postoperative AHI. In contrast, the physiological model (model 3), which included AHIREM (anatomy term), fraction of events that were hypopnea (arousal term), the ratio of AHIREM and AHINREM (muscle responsiveness term), loop gain, and central/mixed apnea index (control of breathing terms), was able to explain 61% of the variance in postoperative AHI. Conclusions: Although loop gain and arousal threshold are associated with residual AHI after surgery, only preoperative AHI was predictive using multivariate regression modeling. Instead, incorporating selected surrogates of physiological traits on the basis of OSA pathophysiology created a model that has more association with actual residual AHI. Commentary: A commentary on this article appears in this issue on page 1023. Clinical Trial Registration: ClinicalTrials.Gov; Title: The Impact of Sleep Apnea Treatment on Physiology Traits in Chinese Patients With Obstructive Sleep Apnea; Identifier: NCT02696629; URL: https://clinicaltrials.gov/show/NCT02696629 Citation: Li Y, Ye J, Han D, Cao X, Ding X, Zhang Y, Xu W, Orr J, Jen R, Sands S, Malhotra A, Owens R. Physiology-based modeling may predict surgical treatment outcome for obstructive sleep apnea. J Clin Sleep Med. 2017;13(9):1029–1037. PMID:28818154

Relative Contributions of Agricultural Drift, Para-Occupational, and Residential Use Exposure Pathways to House Dust Pesticide Concentrations: Meta-Regression of Published Data.

PubMed

Deziel, Nicole C; Freeman, Laura E Beane; Graubard, Barry I; Jones, Rena R; Hoppin, Jane A; Thomas, Kent; Hines, Cynthia J; Blair, Aaron; Sandler, Dale P; Chen, Honglei; Lubin, Jay H; Andreotti, Gabriella; Alavanja, Michael C R; Friesen, Melissa C

2017-03-01

Increased pesticide concentrations in house dust in agricultural areas have been attributed to several exposure pathways, including agricultural drift, para-occupational, and residential use. To guide future exposure assessment efforts, we quantified relative contributions of these pathways using meta-regression models of published data on dust pesticide concentrations. From studies in North American agricultural areas published from 1995 to 2015, we abstracted dust pesticide concentrations reported as summary statistics [e.g., geometric means (GM)]. We analyzed these data using mixed-effects meta-regression models that weighted each summary statistic by its inverse variance. Dependent variables were either the log-transformed GM (drift) or the log-transformed ratio of GMs from two groups (para-occupational, residential use). For the drift pathway, predicted GMs decreased sharply and nonlinearly, with GMs 64% lower in homes 250 m versus 23 m from fields (interquartile range of published data) based on 52 statistics from seven studies. For the para-occupational pathway, GMs were 2.3 times higher [95% confidence interval (CI): 1.5, 3.3; 15 statistics, five studies] in homes of farmers who applied pesticides more recently or frequently versus less recently or frequently. For the residential use pathway, GMs were 1.3 (95% CI: 1.1, 1.4) and 1.5 (95% CI: 1.2, 1.9) times higher in treated versus untreated homes, when the probability that a pesticide was used for the pest treatment was 1-19% and ≥ 20%, respectively (88 statistics, five studies). Our quantification of the relative contributions of pesticide exposure pathways in agricultural populations could improve exposure assessments in epidemiologic studies. The meta-regression models can be updated when additional data become available. Citation: Deziel NC, Beane Freeman LE, Graubard BI, Jones RR, Hoppin JA, Thomas K, Hines CJ, Blair A, Sandler DP, Chen H, Lubin JH, Andreotti G, Alavanja MC, Friesen MC. 2017. Relative contributions of agricultural drift, para-occupational, and residential use exposure pathways to house dust pesticide concentrations: meta-regression of published data. Environ Health Perspect 125:296-305; http://dx.doi.org/10.1289/EHP426.

Relative Contributions of Agricultural Drift, Para-Occupational, and Residential Use Exposure Pathways to House Dust Pesticide Concentrations: Meta-Regression of Published Data

PubMed Central

Deziel, Nicole C.; Freeman, Laura E. Beane; Graubard, Barry I.; Jones, Rena R.; Hoppin, Jane A.; Thomas, Kent; Hines, Cynthia J.; Blair, Aaron; Sandler, Dale P.; Chen, Honglei; Lubin, Jay H.; Andreotti, Gabriella; Alavanja, Michael C. R.; Friesen, Melissa C.

2016-01-01

Background: Increased pesticide concentrations in house dust in agricultural areas have been attributed to several exposure pathways, including agricultural drift, para-occupational, and residential use. Objective: To guide future exposure assessment efforts, we quantified relative contributions of these pathways using meta-regression models of published data on dust pesticide concentrations. Methods: From studies in North American agricultural areas published from 1995 to 2015, we abstracted dust pesticide concentrations reported as summary statistics [e.g., geometric means (GM)]. We analyzed these data using mixed-effects meta-regression models that weighted each summary statistic by its inverse variance. Dependent variables were either the log-transformed GM (drift) or the log-transformed ratio of GMs from two groups (para-occupational, residential use). Results: For the drift pathway, predicted GMs decreased sharply and nonlinearly, with GMs 64% lower in homes 250 m versus 23 m from fields (interquartile range of published data) based on 52 statistics from seven studies. For the para-occupational pathway, GMs were 2.3 times higher [95% confidence interval (CI): 1.5, 3.3; 15 statistics, five studies] in homes of farmers who applied pesticides more recently or frequently versus less recently or frequently. For the residential use pathway, GMs were 1.3 (95% CI: 1.1, 1.4) and 1.5 (95% CI: 1.2, 1.9) times higher in treated versus untreated homes, when the probability that a pesticide was used for the pest treatment was 1–19% and ≥ 20%, respectively (88 statistics, five studies). Conclusion: Our quantification of the relative contributions of pesticide exposure pathways in agricultural populations could improve exposure assessments in epidemiologic studies. The meta-regression models can be updated when additional data become available. Citation: Deziel NC, Beane Freeman LE, Graubard BI, Jones RR, Hoppin JA, Thomas K, Hines CJ, Blair A, Sandler DP, Chen H, Lubin JH, Andreotti G, Alavanja MC, Friesen MC. 2017. Relative contributions of agricultural drift, para-occupational, and residential use exposure pathways to house dust pesticide concentrations: meta-regression of published data. Environ Health Perspect 125:296–305; http://dx.doi.org/10.1289/EHP426 PMID:27458779

A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models.

PubMed

Zhu, Hongxiao; Caspers, Philip; Morris, Jeffrey S; Wu, Xiaowei; Müller, Rolf

2018-01-01

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design.

A Unified Analysis of Structured Sonar-terrain Data using Bayesian Functional Mixed Models

PubMed Central

Zhu, Hongxiao; Caspers, Philip; Morris, Jeffrey S.; Wu, Xiaowei; Müller, Rolf

2017-01-01

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. While existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this paper, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. PMID:29749977

Evaluating risk factors for endemic human Salmonella Enteritidis infections with different phage types in Ontario, Canada using multinomial logistic regression and a case-case study approach

PubMed Central

2012-01-01

Background Identifying risk factors for Salmonella Enteritidis (SE) infections in Ontario will assist public health authorities to design effective control and prevention programs to reduce the burden of SE infections. Our research objective was to identify risk factors for acquiring SE infections with various phage types (PT) in Ontario, Canada. We hypothesized that certain PTs (e.g., PT8 and PT13a) have specific risk factors for infection. Methods Our study included endemic SE cases with various PTs whose isolates were submitted to the Public Health Laboratory-Toronto from January 20th to August 12th, 2011. Cases were interviewed using a standardized questionnaire that included questions pertaining to demographics, travel history, clinical symptoms, contact with animals, and food exposures. A multinomial logistic regression method using the Generalized Linear Latent and Mixed Model procedure and a case-case study design were used to identify risk factors for acquiring SE infections with various PTs in Ontario, Canada. In the multinomial logistic regression model, the outcome variable had three categories representing human infections caused by SE PT8, PT13a, and all other SE PTs (i.e., non-PT8/non-PT13a) as a referent category to which the other two categories were compared. Results In the multivariable model, SE PT8 was positively associated with contact with dogs (OR=2.17, 95% CI 1.01-4.68) and negatively associated with pepper consumption (OR=0.35, 95% CI 0.13-0.94), after adjusting for age categories and gender, and using exposure periods and health regions as random effects to account for clustering. Conclusions Our study findings offer interesting hypotheses about the role of phage type-specific risk factors. Multinomial logistic regression analysis and the case-case study approach are novel methodologies to evaluate associations among SE infections with different PTs and various risk factors. PMID:23057531

Application of Multiregressive Linear Models, Dynamic Kriging Models and Neural Network Models to Predictive Maintenance of Hydroelectric Power Systems

NASA Astrophysics Data System (ADS)

Lucifredi, A.; Mazzieri, C.; Rossi, M.

2000-05-01

Since the operational conditions of a hydroelectric unit can vary within a wide range, the monitoring system must be able to distinguish between the variations of the monitored variable caused by variations of the operation conditions and those due to arising and progressing of failures and misoperations. The paper aims to identify the best technique to be adopted for the monitoring system. Three different methods have been implemented and compared. Two of them use statistical techniques: the first, the linear multiple regression, expresses the monitored variable as a linear function of the process parameters (independent variables), while the second, the dynamic kriging technique, is a modified technique of multiple linear regression representing the monitored variable as a linear combination of the process variables in such a way as to minimize the variance of the estimate error. The third is based on neural networks. Tests have shown that the monitoring system based on the kriging technique is not affected by some problems common to the other two models e.g. the requirement of a large amount of data for their tuning, both for training the neural network and defining the optimum plane for the multiple regression, not only in the system starting phase but also after a trivial operation of maintenance involving the substitution of machinery components having a direct impact on the observed variable. Or, in addition, the necessity of different models to describe in a satisfactory way the different ranges of operation of the plant. The monitoring system based on the kriging statistical technique overrides the previous difficulties: it does not require a large amount of data to be tuned and is immediately operational: given two points, the third can be immediately estimated; in addition the model follows the system without adapting itself to it. The results of the experimentation performed seem to indicate that a model based on a neural network or on a linear multiple regression is not optimal, and that a different approach is necessary to reduce the amount of work during the learning phase using, when available, all the information stored during the initial phase of the plant to build the reference baseline, elaborating, if it is the case, the raw information available. A mixed approach using the kriging statistical technique and neural network techniques could optimise the result.

Evaluation of third-degree and fourth-degree laceration rates as quality indicators.

PubMed

Friedman, Alexander M; Ananth, Cande V; Prendergast, Eri; D'Alton, Mary E; Wright, Jason D

2015-04-01

To examine the patterns and predictors of third-degree and fourth-degree laceration in women undergoing vaginal delivery. We identified a population-based cohort of women in the United States who underwent a vaginal delivery between 1998 and 2010 using the Nationwide Inpatient Sample. Multivariable log-linear regression models were developed to account for patient, obstetric, and hospital factors related to lacerations. Between-hospital variability of laceration rates was calculated using generalized log-linear mixed models. Among 7,096,056 women who underwent vaginal delivery in 3,070 hospitals, 3.3% (n=232,762) had a third-degree laceration and 1.1% (n=76,347) had a fourth-degree laceration. In an adjusted model for fourth-degree lacerations, important risk factors included shoulder dystocia and forceps and vacuum deliveries with and without episiotomy. Other demographic, obstetric, medical, and hospital variables, although statistically significant, were not major determinants of lacerations. Risk factors in a multivariable model for third-degree lacerations were similar to those in the fourth-degree model. Regression analysis of hospital rates (n=3,070) of lacerations demonstrated limited between-hospital variation. Risk of third-degree and fourth-degree laceration was most strongly related to operative delivery and shoulder dystocia. Between-hospital variation was limited. Given these findings and that the most modifiable practice related to lacerations would be reduction in operative vaginal deliveries (and a possible increase in cesarean delivery), third-degree and fourth-degree laceration rates may be a quality metric of limited utility.

Prevalence and characteristics of abuse experiences and depression symptoms among injection drug-using female sex workers in Mexico.

PubMed

Ulibarri, Monica D; Hiller, Sarah P; Lozada, Remedios; Rangel, M Gudelia; Stockman, Jamila K; Silverman, Jay G; Ojeda, Victoria D

2013-01-01

This mixed methods study examined the prevalence and characteristics of physical and sexual abuse and depression symptoms among 624 injection drug-using female sex workers (FSW-IDUs) in Tijuana and Ciudad Juarez, Mexico; a subset of 47 from Tijuana also underwent qualitative interviews. Linear regressions identified correlates of current depression symptoms. In the interviews, FSW-IDUs identified drug use as a method of coping with the trauma they experienced from abuse that occurred before and after age 18 and during the course of sex work. In a multivariate linear regression model, two factors-ever experiencing forced sex and forced sex in the context of sex work-were significantly associated with higher levels of depression symptoms. Our findings suggest the need for integrated mental health and drug abuse services for FSW-IDUs addressing history of trauma as well as for further research on violence revictimization in the context of sex work in Mexico.

Prevalence and Characteristics of Abuse Experiences and Depression Symptoms among Injection Drug-Using Female Sex Workers in Mexico

PubMed Central

Ulibarri, Monica D.; Hiller, Sarah P.; Lozada, Remedios; Rangel, M. Gudelia; Stockman, Jamila K.; Silverman, Jay G.; Ojeda, Victoria D.

2013-01-01

This mixed methods study examined the prevalence and characteristics of physical and sexual abuse and depression symptoms among 624 injection drug-using female sex workers (FSW-IDUs) in Tijuana and Ciudad Juarez, Mexico; a subset of 47 from Tijuana also underwent qualitative interviews. Linear regressions identified correlates of current depression symptoms. In the interviews, FSW-IDUs identified drug use as a method of coping with the trauma they experienced from abuse that occurred before and after age 18 and during the course of sex work. In a multivariate linear regression model, two factors—ever experiencing forced sex and forced sex in the context of sex work—were significantly associated with higher levels of depression symptoms. Our findings suggest the need for integrated mental health and drug abuse services for FSW-IDUs addressing history of trauma as well as for further research on violence revictimization in the context of sex work in Mexico. PMID:23737808

Unequal views of inequality: Cross-national support for redistribution 1985-2011.

PubMed

VanHeuvelen, Tom

2017-05-01

This research examines public views on government responsibility to reduce income inequality, support for redistribution. While individual-level correlates of support for redistribution are relatively well understood, many questions remain at the country-level. Therefore, I examine how country-level characteristics affect aggregate support for redistribution. I test explanations of aggregate support using a unique dataset combining 18 waves of the International Social Survey Programme and European Social Survey. Results from mixed-effects logistic regression and fixed-effects linear regression models show two primary and contrasting effects. States that reduce inequality through bundles of tax and transfer policies are rewarded with more supportive publics. In contrast, economic development has a seemingly equivalent and dampening effect on public support. Importantly, the effect of economic development grows at higher levels of development, potentially overwhelming the amplifying effect of state redistribution. My results therefore suggest a fundamental challenge to proponents of egalitarian politics. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors.

PubMed

Ojo, Oluwatobi Blessing; Lougue, Siaka; Woldegerima, Woldegebriel Assefa

2017-01-01

TB is rated as one of the world's deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014.

Predicting Upscaled Behavior of Aqueous Reactants in Heterogeneous Porous Media

NASA Astrophysics Data System (ADS)

Wright, E. E.; Hansen, S. K.; Bolster, D.; Richter, D. H.; Vesselinov, V. V.

2017-12-01

When modeling reactive transport, reaction rates are often overestimated due to the improper assumption of perfect mixing at the support scale of the transport model. In reality, fronts tend to form between participants in thermodynamically favorable reactions, leading to segregation of reactants into islands or fingers. When such a configuration arises, reactions are limited to the interface between the reactive solutes. Closure methods for estimating control-volume-effective reaction rates in terms of quantities defined at the control volume scale do not presently exist, but their development is crucial for effective field-scale modeling. We attack this problem through a combination of analytical and numerical means. Specifically, we numerically study reactive transport through an ensemble of realizations of two-dimensional heterogeneous porous media. We then employ regression analysis to calibrate an analytically-derived relationship between reaction rate and various dimensionless quantities representing conductivity-field heterogeneity and the respective strengths of diffusion, reaction and advection.

Diagnostic efficiency of an ability-focused battery.

PubMed

Miller, Justin B; Fichtenberg, Norman L; Millis, Scott R

2010-05-01

An ability-focused battery (AFB) is a selected group of well-validated neuropsychological measures that assess the conventional range of cognitive domains. This study examined the diagnostic efficiency of an AFB for use in clinical decision making with a mixed sample composed of individuals with neurological brain dysfunction and individuals referred for cognitive assessment without evidence of neurological disorders. Using logistic regression analyses and ROC curve analysis, a five-domain model composed of attention, processing speed, visual-spatial reasoning, language/verbal reasoning, and memory domain scores was fitted that had an AUC of.89 (95% CI =.84-.95). A more parsimonious two-domain model using processing speed and memory was also fitted that had an AUC of.90 (95% confidence interval =.84-.95). A model composed of a global ability score calculated from the mean of the individual domain scores was also fitted with an AUC of.88 (95% CI =.82-.94).

Relevance of the c-statistic when evaluating risk-adjustment models in surgery.

PubMed

Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y

2012-05-01

The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become more homogenous. Although it remains an important tool, caution is advised when the c-statistic is advanced as the sole measure of a model performance. Copyright © 2012 American College of Surgeons. All rights reserved.

Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration.

PubMed

Jović, Ozren; Smrečki, Neven; Popović, Zora

2016-04-01

A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for p<0.05). Also, iRR can be a fast alternative to iPLS, especially in case of unknown degree of complexity of analyzed system, i.e. if upper limit of number of latent variables is not easily estimated for iPLS. Adulteration of hempseed (H) oil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEP<1.2%). This means that FTIR-ATR coupled with iRR can very rapidly and effectively determine the level of adulteration in the adulterated hempseed oil (R(2)>0.99). Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Searching for a neurologic injury's Wechsler Adult Intelligence Scale-Third Edition profile.

PubMed

Gonçalves, Marta A; Moura, Octávio; Castro-Caldas, Alexandre; Simões, Mário R

2017-01-01

This study aimed to investigate the presence of a Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) cognitive profile in a Portuguese neurologic injured sample. The Portuguese WAIS-III was administered to 81 mixed neurologic patients and 81 healthy matched controls selected from the Portuguese standardization sample. Although the mixed neurologic injury group performed significantly lower than the healthy controls for the majority of the WAIS-III scores (i.e., composite measures, discrepancies, and subtests), the mean scores were within the normal range and, therefore, at risk of being unobserved in a clinical evaluation. ROC curves analysis showed poor to acceptable diagnostic accuracy for the WAIS-III composite measures and subtests (Working Memory Index and Digit Span revealed the highest accuracy for discriminating between participants, respectively). Multiple regression analysis showed that both literacy and the presence of brain injury were significant predictors for all of the composite measures. In addition, multiple regression analysis also showed that literacy, age of injury onset, and years of survival predicted all seven composite measures for the mixed neurologic injured group. Despite the failure to find a WAIS-III cognitive profile for mixed neurologic patients, the results showed a significant influence of brain lesion and literacy in the performance of the WAIS-III.

Ensemble of trees approaches to risk adjustment for evaluating a hospital's performance.

PubMed

Liu, Yang; Traskin, Mikhail; Lorch, Scott A; George, Edward I; Small, Dylan

2015-03-01

A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expected outcome rate given its patient (case) mix and service. The process of calculating the hospital's expected outcome rate given its patient mix and service is called risk adjustment (Iezzoni 1997). Risk adjustment is critical for accurately evaluating and comparing hospitals' performances since we would not want to unfairly penalize a hospital just because it treats sicker patients. The key to risk adjustment is accurately estimating the probability of an Outcome given patient characteristics. For cases with binary outcomes, the method that is commonly used in risk adjustment is logistic regression. In this paper, we consider ensemble of trees methods as alternatives for risk adjustment, including random forests and Bayesian additive regression trees (BART). Both random forests and BART are modern machine learning methods that have been shown recently to have excellent performance for prediction of outcomes in many settings. We apply these methods to carry out risk adjustment for the performance of neonatal intensive care units (NICU). We show that these ensemble of trees methods outperform logistic regression in predicting mortality among babies treated in NICU, and provide a superior method of risk adjustment compared to logistic regression.

Shergottite Lead Isotope Signature in Chassigny and the Nakhlites

NASA Technical Reports Server (NTRS)

Jones, J. H.; Simon, J. I.

2017-01-01

The nakhlites/chassignites and the shergottites represent two differing suites of basaltic martian meteorites. The shergottites have ages less than or equal to 0.6 Ga and a large range of initial Sr-/Sr-86 and epsilon (Nd-143) ratios. Conversely, the nakhlites and chassignites cluster at 1.3-1.4 Ga and have a limited range of initial Sr-87/Sr-86 and epsilon (Nd-143). More importantly, the shergottites have epsilon (W-182) less than 1, whereas the nakhlites and chassignites have epsilon (W-182) approximately 3. This latter observation precludes the extraction of both meteorite groups from a single source region. However, recent Pb isotopic analyses indicate that there may have been interaction between shergottite and nakhlite/chassignite Pb reservoirs.Pb Analyses of Chassigny: Two different studies haveinvestigated 207Pb/204Pb vs. 206Pb/204Pb in Chassigny: (i)TIMS bulk-rock analyses of successive leaches and theirresidue [3]; and (ii) SIMS analysis of individual minerals[4]. The bulk-rock analyses fall along a regression of SIMSplagioclase analyses that define an errorchron that is olderthan the Solar System (4.61±0.1 Ga); i.e., these define amixing line between Chassigny’s principal Pb isotopic components(Fig. 1). Augites and olivines in Chassingy (notshown) also fall along or near the plagioclase regression [4].This agreement indicates that the whole-rock leachateslikely measure indigenous, martian Pb, not terrestrial contamination[5]. SIMS analyses of K-spars and sulfides definea separate, sub-parallel trend having higher 207Pb/206Pbvalues ([4]; Fig. 1). The good agreement between the bulkrockanalyses and the SIMS analyses of plagioclases alsoindicates that the Pb in the K-spars and sulfides cannot be amajor component of Chassigny.The depleted reservoir sampled by Chassigny plagioclaseis not the same as the solar system initial (PAT) andrequires a multi-stage origin. Here we show a two-stagemodel (Fig. 1) with a 238U/204Pb (µ) of 0.5 for 4.5-2.4 Gaand a µ of 7 for 2.4-1.4 Ga. This is not a unique model butdoes produce a Pb composition that falls on the plagioclaseregression at 1.4 Ga, the approximate igneous age of Chassigny [1]. It should be noted that low-µ single-stage modelsare not capable of producing sufficiently radiogenic 206Pb/204Pb at 1.4 Ga.Relation to Shergottites: The Chassigny K-spars and sulfides fall along a second mixing line defined by leachesand residues of depleted and intermediate shergottites [6]. This mixing line falls above the plagioclase regression.Therefore, we also interpret the radiogenic component of this mixing line to represent indigenous martian Pb. It ispossible that the depleted and intermediate shergottites and the Chassigny plagioclases sample radiogenic Pb from thethe same source, i.e., the mixing lines may intersect at high 206Pb/204Pb.Both K-spar and sulfide are late-stage phases. At the time of their crystallization, the Chassigny system appearsto have remained open to a depleted shergottite Pb reservoir. The depleted component of the shergottite mixing linecan be generated by a single-stage evolution from PAT (4.5 to 1.4 Ga) in a reservoir having a µ 2. A similar modelfor the most depleted shergottites is also possible: µ = 1.5 for 4.5 to 0.3 Ga.Nakhlites: Nakhlite analyses plot between the shergottite and Chassigny plagioclase regressions [3]. So again,members of the nakhlite/chassignite suite show affinities to shergottite Pb.

GC×GC measurements of C7-C11 aromatic and n-alkane hydrocarbons on Crete, in air from Eastern Europe during the MINOS campaign

NASA Astrophysics Data System (ADS)

Xu, X.; Williams, C.; Plass-Dülmer, H.; Berresheim, H.; Salisbury, G.; Lange, L.; Lelieveld, J.

2003-09-01

During the Mediterranean Intensive Oxidant Study (MINOS) campaign in August 2001 gas-phase organic compounds were measured using comprehensive two-dimensional gas chromatography (GCxGC) at the Finokalia ground station, Crete. In this paper, C7-C11 aromatic and n-alkane measurements are presented and interpreted. The mean mixing ratios of the hydrocarbons varied from 1±1 pptv (i-propylbenzene) to 43±36 pptv (toluene). The observed mixing ratios showed strong day-to-day variations and generally higher levels during the first half of the campaign. Mean diel profiles showed maxima at local midnight and late morning, and minima in the early morning and evening. Results from analysis using a simplified box model suggest that both the chemical sink (i.e. reaction with OH) and the variability of source strengths were the causes of the observed variations in hydrocarbon mixing ratios. The logarithms of hydrocarbon concentrations were negatively correlated with the OH concentrations integral over a day prior to the hydrocarbon measurements. Slopes of the regression lines derived from these correlations for different compounds are compared with literature rate constants for their reactions with OH. The slopes for most compounds agree reasonably well with the literature rate constants. A sequential reaction model has been applied to the interpretation of the relationship between ethylbenzene and two of its potential products, i.e. acetophenone and benzeneacetaldehyde. The model can explain the good correlation observed between [acetophenone]/[ethylbenzene] and [benzeneacetaldehyde]/[ethylbenzene]. The model results and field measurements suggest that the reactivity of benzeneacetaldehyde may lie between those of acetophenone and ethylbenzene and that the ratio between yields of acetophenone and benzeneacetaldehyde may be up to 28:1. Photochemical ages of trace gases sampled at Finokalia during the campaign are estimated using the sequential reaction model and related data. They lie in the range of about 0.5-2.5 days.

GC×GC measurements of C7-C11 aromatic and n-alkane hydrocarbons on Crete, in air from Eastern Europe during the MINOS campaign

NASA Astrophysics Data System (ADS)

Xu, X.; Williams, J.; Plass-Dülmer, C.; Berresheim, H.; Salisbury, G.; Lange, L.; Lelieveld, J.

2003-03-01

During the Mediterranean Intensive Oxidant Study (MINOS) campaign in August 2001 gas-phase organic compounds were measured using comprehensive two-dimensional gas chromatography (GC×GC) at the Finokalia ground station, Crete. In this paper, C7-C11 aromatic and n-alkane measurements are presented and interpreted. The mean mixing ratios of the hydrocarbons varied from 1+/-1 pptv (i-propylbenzene) to 43+/-36 pptv (toluene). The observed mixing ratios showed strong day-to-day variations and generally higher levels during the first half of the campaign. Mean diel profiles showed maxima at local midnight and late morning, and minima in the early morning and evening. Results from analysis using a simplified box model suggest that both the chemical sink (i.e. reaction with OH) and the variability of source strengths were the causes of the observed variations in hydrocarbon mixing ratios. The logarithms of hydrocarbon concentrations were negatively correlated with the OH concentrations integral over a day prior to the hydrocarbon measurements. Slopes of the regression lines derived from these correlations for different compounds are compared with literature rate constants for their reactions with OH. The slopes for most compounds agree reasonably well with the literature rate constants. A sequential reaction model has been applied to the interpretation of the relationship between ethylbenzene and two of its potential products, i.e. acetophenone and benzeneacetaldehyde. The model can explain the good correlation observed between [acetophenone]/[ethylbenzene] and [benzeneacetaldehyde]/[ethylbenzene]. The model results and field measurements suggest that the reactivity of benzeneacetaldehyde may lie between those of acetophenone and ethylbenzene and that the ratio between yields of acetophenone and benzeneacetaldehyde may be up to 28:1. Photochemical ages of trace gases sampled at Finokalia during the campaign are estimated using the sequential reaction model and related data. They lie in the range of about 0.5-2.5 days.

Landscape factors influencing the spatial distribution and abundance of mosquito vector Culex quinquefasciatus (Diptera: Culicidae) in a mixed residential-agricultural community in Hawai'i

USGS Publications Warehouse

Reiter, M.E.; Lapointe, D.A.

2007-01-01

Mosquito-borne avian diseases, principally avian malaria (Plasmodium relictum Grassi and Feletti) and avian pox (Avipoxvirus sp.) have been implicated as the key limiting factor associated with recent declines of endemic avifauna in the Hawaiian Island archipelago. We present data on the relative abundance, infection status, and spatial distribution of the primary mosquito vector Culex quinquefasciatus Say (Diptera: Culicidae) across a mixed, residential-agricultural community adjacent to Hawai'i Volcanoes National Park on Hawai'i Island. We modeled the effect of agriculture and forest fragmentation in determining relative abundance of adult Cx. quinquefasciatus in Volcano Village, and we implement our statistical model in a geographic information system to generate a probability of mosquito capture prediction surface for the study area. Our model was based on biweekly captures of adult mosquitoes from 20 locations within Volcano Village from October 2001 to April 2003. We used mixed effects logistic regression to model the probability of capturing a mosquito, and we developed a set of 17 competing models a priori to specifically evaluate the effect of agriculture and fragmentation (i.e., residential landscapes) at two spatial scales. In total, 2,126 mosquitoes were captured in CO 2-baited traps with an average probability of 0.27 (SE = 0.10) of capturing one or more mosquitoes per trap night. Twelve percent of mosquitoes captured were infected with P. relictum. Our data indicate that agricultural lands and forest fragmentation significantly increase the probability of mosquito capture. The prediction surface identified areas along the Hawai'i Volcanoes National Park boundary that may have high relative abundance of the vector. Our data document the potential of avian malaria transmission in residential-agricultural landscapes and support the need for vector management that extends beyond reserve boundaries and considers a reserve's spatial position in a highly heterogeneous landscape.

Development of a data driven process-based model for remote sensing of terrestrial ecosystem productivity, evapotranspiration, and above-ground biomass

NASA Astrophysics Data System (ADS)

El Masri, Bassil

2011-12-01

Modeling terrestrial ecosystem functions and structure has been a subject of increasing interest because of the importance of the terrestrial carbon cycle in global carbon budget and climate change. In this study, satellite data were used to estimate gross primary production (GPP), evapotranspiration (ET) for two deciduous forests: Morgan Monroe State forest (MMSF) in Indiana and Harvard forest in Massachusetts. Also, above-ground biomass (AGB) was estimated for the MMSF and the Howland forest (mixed forest) in Maine. Surface reflectance and temperature, vegetation indices, soil moisture, tree height and canopy area derived from the Moderate Resolution Imagining Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMRS-E), LIDAR, and aerial imagery respectively, were used for this purpose. These variables along with others derived from remotely sensed data were used as inputs variables to process-based models which estimated GPP and ET and to a regression model which estimated AGB. The process-based models were BIOME-BGC and the Penman-Monteith equation. Measured values for the carbon and water fluxes obtained from the Eddy covariance flux tower were compared to the modeled GPP and ET. The data driven methods produced good estimation of GPP and ET with an average root mean square error (RMSE) of 0.17 molC/m2 and 0.40 mm/day, respectively for the MMSF and the Harvard forest. In addition, allometric data for the MMSF were used to develop the regression model relating AGB with stem volume. The performance of the AGB regression model was compared to site measurements using remotely sensed data for the MMSF and the Howland forest where the model AGB RMSE ranged between 2.92--3.30 Kg C/m2. Sensitivity analysis revealed that improvement in maintenance respiration estimation and remotely sensed maximum photosynthetic activity as well as accurate estimate of canopy resistance will result in improved GPP and ET predictions. Moreover, AGB estimates were found to decrease as large grid size is used in rasterizing LIDAR return points. The analysis suggested that this methodology could be used as an operational procedure for monitoring changes in terrestrial ecosystem functions and structure brought by environmental changes.

Results of a survey to determine demographic and business management factors associated with size and growth rate of rural mixed-animal veterinary practices.

PubMed

Brusk, Amy M; White, Brad J; Goehl, Dan R; Dhuyvetter, Kevin C

2010-12-15

To determine potential associations between demographic and business management factors and practice size and growth rate in rural mixed-animal veterinary practices. Cross-sectional survey. 54 mixed-animal practitioners. A cross-sectional survey (96 questions) was electronically disseminated. Responses were collected, and outcomes (number of veterinarians [NV], growth in number of veterinarians [NVG], gross practice income [GPI], growth in gross practice income [GPIG], gross practice income per veterinarian [GPIV], and growth in gross practice income per veterinarian [GPIVG]) were calculated. Bivariate analyses were performed and multivariable models created to determine associations between survey responses and outcomes of interest. Survey respondents were from mixed-animal practices, and most (46/54 [85.2%]) practiced in small communities (< 25,000 people). Study practices had a median ± SD NV of 2.3 ± 1.9 veterinarians, median GPI of $704,547 ± 754,839, and median GPIV of $282,065 ± 182,344. Multivariable regression analysis revealed several factors related to practice size, including the number of associate veterinarians and veterinary technicians in the practice, service fee structure, and employment of a business manager. Typically, practices had positive mean growth in NVG (4.4%), GPIG (8.5%), and GPIVG (8.1%), but growth rate was highly variable among practices. Factors associated with growth rate included main species interest, frequency for adjusting prices, use of a marketing plan, service fee structure, and sending a client newsletter. Mixed-animal practices had a large range in size and growth rate. Economic indices were impacted by common business management practices.

Acculturation, childhood trauma and the cortisol awakening response in Mexican-American adults.

PubMed

Mangold, Deborah; Wand, Gary; Javors, Martin; Mintz, James

2010-09-01

Exposure to chronic and traumatic stress has been associated with the dysregulation of crucial stress response systems. Acculturation has been associated with unique forms of chronic psychosocial stress. The purpose of this study was to examine the effects of exposure to early traumatic stress and acculturation on dysregulation of the cortisol awakening response (CAR) in Mexican-American adults. Salivary cortisol samples were collected at awakening and 30, 45, and 60 min thereafter, on two consecutive weekdays from 59 healthy Mexican-American adult males (26) and females (33), ages 18-38 years. Participants were assessed for level of acculturation and exposure to early trauma. Data were analyzed using a mixed effects regression model with repeated measures at four time points. Mixed effects regression results indicated a significant Early Trauma x Time interaction (p=.0029) and a significant Acculturation x Time interaction (p=.0015), after controlling for age and sex. Subsequent analyses of the interaction of Trauma x Acculturation x Time showed that more than minimal exposure to either risk factor was associated with attenuation of the awakening cortisol response (p=.0002). Higher levels of acculturation with greater Anglo-orientation were associated with attenuation of the CAR in Mexican-American adults. Both moderate and higher levels of exposure to early trauma were associated with an attenuated CAR. However, greater exposure to both risk factors was only incrementally worse than exposure to either one. Copyright (c) 2010 Elsevier Inc. All rights reserved.

A mixed-methods exploration of implementation of a comprehensive school healthy eating model one year after scale-up.

PubMed

Naylor, Patti-Jean; McKay, Heather A; Valente, Maria; Mâsse, Louise C

2016-04-01

To study the implementation of a school-based healthy eating (HE) model one year after scale-up in British Columbia (BC). Specifically, to examine implementation of Action Schools! BC (AS! BC) and its influence on implementation of classroom HE activities, and to explore factors associated with implementation. Diffusion of Innovations, Social Cognitive and Organizational Change theories guided our approach. We used a mixed-methods research design including focus group interviews (seven schools, sixty-two implementers) and a cross-sectional multistage survey to principals (n 36, 92 % response rate) and teachers of grades 4 to 7 (n 168, 70 % response rate). Self-reported implementation of classroom HE activities and reported use of specific AS! BC HE activities were primary implementation measures. Thematic analysis of focus group data and multilevel mixed-effect logistic regression analyses of survey data were conducted. Elementary schools across BC, Canada. Thirty-nine school districts, thirty-six principals, 168 grade 4 to 7 teachers. Forty-two per cent of teachers in registered schools were implementing AS! BC HE in their classrooms. Users were 6·25 times more likely to have delivered a HE lesson in the past week. Implementation facilitators were school champions, technical support and access to resources; barriers were lack of time, loss of leadership or momentum. Implementation predictors were teacher training, self-efficacy, experience with the physical activity component of AS! BC, supportive school climate and parental post-secondary education. Our findings reinforce that continued teacher training and support are important public health investments that contribute to successful implementation of school-based HE models after scale-up.

Genome analysis of Legionella pneumophila strains using a mixed-genome microarray.

PubMed

Euser, Sjoerd M; Nagelkerke, Nico J; Schuren, Frank; Jansen, Ruud; Den Boer, Jeroen W

2012-01-01

Legionella, the causative agent for Legionnaires' disease, is ubiquitous in both natural and man-made aquatic environments. The distribution of Legionella genotypes within clinical strains is significantly different from that found in environmental strains. Developing novel genotypic methods that offer the ability to distinguish clinical from environmental strains could help to focus on more relevant (virulent) Legionella species in control efforts. Mixed-genome microarray data can be used to perform a comparative-genome analysis of strain collections, and advanced statistical approaches, such as the Random Forest algorithm are available to process these data. Microarray analysis was performed on a collection of 222 Legionella pneumophila strains, which included patient-derived strains from notified cases in The Netherlands in the period 2002-2006 and the environmental strains that were collected during the source investigation for those patients within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm combined with a logistic regression model was used to select predictive markers and to construct a predictive model that could discriminate between strains from different origin: clinical or environmental. Four genetic markers were selected that correctly predicted 96% of the clinical strains and 66% of the environmental strains collected within the Dutch National Legionella Outbreak Detection Programme. The Random Forest algorithm is well suited for the development of prediction models that use mixed-genome microarray data to discriminate between Legionella strains from different origin. The identification of these predictive genetic markers could offer the possibility to identify virulence factors within the Legionella genome, which in the future may be implemented in the daily practice of controlling Legionella in the public health environment.

Serum estradiol does not differentiate stress, mixed and urge incontinent women around menopause. A report from the Women's Health in the Lund Area (WHILA) study.

PubMed

Hamer, Maria Andrada; Källén, Karin; Lidfeldt, Jonas; Samsioe, Göran; Teleman, Pia

2011-11-01

To outline serum estradiol levels in perimenopausal women with stress, mixed or urge incontinence. We believe the majority of urgency symptoms in perimenopausal women to be caused by a pelvic floor dysfunction and a hypermobility of the bladder neck. If this is the case, there would be no difference in estradiol levels between the groups. University hospital. In the observational Women's Health in the Lund Area study, a subset of 400/2221 women reporting urinary incontinence completed a detailed questionnaire regarding lower urinary tract symptoms and had their serum steroid hormone levels measured. Statistical analyses were made by Chi-square test, nonparametrical tests, ANOVA, multi- and univariate logistic regression analysis. Stress incontinence was reported by 196, mixed incontinence by 153 and urge incontinence by 43 women; in 369, serumestradiol values were available. Serum estradiol did not differ significantly between stress incontinent (median 49.5 pmo/l, range 2.63-875.4), urge incontinent (median 31.6 pmol/l, range 2.63-460.7) or mixed incontinent women (median 35.5 pmol/l, range 2.63-787.9, p=0.62). Logistic regression analysis correcting for age, parity, hormonal status, smoking, hysterectomy and BMI also failed to show any difference in estradiol levels between the groups (p=0.41-0.58). No significant differences in serum estradiol levels between stress, mixed or urge incontinent perimenopausal women could be demonstrated. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

The nonlinear relations of the approximate number system and mathematical language to early mathematics development.

PubMed

Purpura, David J; Logan, Jessica A R

2015-12-01

Both mathematical language and the approximate number system (ANS) have been identified as strong predictors of early mathematics performance. Yet, these relations may be different depending on a child's developmental level. The purpose of this study was to evaluate the relations between these domains across different levels of ability. Participants included 114 children who were assessed in the fall and spring of preschool on a battery of academic and cognitive tasks. Children were 3.12 to 5.26 years old (M = 4.18, SD = .58) and 53.6% were girls. Both mixed-effect and quantile regressions were conducted. The mixed-effect regressions indicated that mathematical language, but not the ANS, nor other cognitive domains, predicted mathematics performance. However, the quantile regression analyses revealed a more nuanced relation among domains. Specifically, it was found that mathematical language and the ANS predicted mathematical performance at different points on the ability continuum. These dual nonlinear relations indicate that different mechanisms may enhance mathematical acquisition dependent on children's developmental abilities. (c) 2015 APA, all rights reserved).

Case mix, outcome and activity for obstetric admissions to adult, general critical care units: a secondary analysis of the ICNARC Case Mix Programme Database

PubMed Central

2005-01-01

Introduction Risk prediction scores usually overestimate mortality in obstetric populations because mortality rates in this group are considerably lower than in others. Studies examining this effect were generally small and did not distinguish between obstetric and nonobstetric pathologies. We evaluated the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II model in obstetric admissions to critical care units contributing to the ICNARC Case Mix Programme. Methods All obstetric admissions were extracted from the ICNARC Case Mix Programme Database of 219,468 admissions to UK critical care units from 1995 to 2003 inclusive. Cases were divided into direct obstetric pathologies and indirect or coincidental pathologies, and compared with a control cohort of all women aged 16–50 years not included in the obstetric categories. The predictive ability of APACHE II was evaluated in the three groups. A prognostic model was developed for direct obstetric admissions to predict the risk for hospital mortality. A log-linear model was developed to predict the length of stay in the critical care unit. Results A total of 1452 direct obstetric admissions were identified, the most common pathologies being haemorrhage and hypertensive disorders of pregnancy. There were 278 admissions identified as indirect or coincidental and 22,938 in the nonpregnant control cohort. Hospital mortality rates were 2.2%, 6.0% and 19.6% for the direct obstetric group, the indirect or coincidental group, and the control cohort, respectively. Cox regression calibration analysis showed a reasonable fit of the APACHE II model for the nonpregnant control cohort (slope = 1.1, intercept = -0.1). However, the APACHE II model vastly overestimated mortality for obstetric admissions (mortality ratio = 0.25). Risk prediction modelling demonstrated that the Glasgow Coma Scale score was the best discriminator between survival and death in obstetric admissions. Conclusion This study confirms that APACHE II overestimates mortality in obstetric admissions to critical care units. This may be because of the physiological changes in pregnancy or the unique scoring profile of obstetric pathologies such as HELLP syndrome. It may be possible to recalibrate the APACHE II score for obstetric admissions or to devise an alternative score specifically for obstetric admissions.

Modeling the soil water retention curves of soil-gravel mixtures with regression method on the Loess Plateau of China.

PubMed

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present.

Modeling the Soil Water Retention Curves of Soil-Gravel Mixtures with Regression Method on the Loess Plateau of China

PubMed Central

Wang, Huifang; Xiao, Bo; Wang, Mingyu; Shao, Ming'an

2013-01-01

Soil water retention parameters are critical to quantify flow and solute transport in vadose zone, while the presence of rock fragments remarkably increases their variability. Therefore a novel method for determining water retention parameters of soil-gravel mixtures is required. The procedure to generate such a model is based firstly on the determination of the quantitative relationship between the content of rock fragments and the effective saturation of soil-gravel mixtures, and then on the integration of this relationship with former analytical equations of water retention curves (WRCs). In order to find such relationships, laboratory experiments were conducted to determine WRCs of soil-gravel mixtures obtained with a clay loam soil mixed with shale clasts or pebbles in three size groups with various gravel contents. Data showed that the effective saturation of the soil-gravel mixtures with the same kind of gravels within one size group had a linear relation with gravel contents, and had a power relation with the bulk density of samples at any pressure head. Revised formulas for water retention properties of the soil-gravel mixtures are proposed to establish the water retention curved surface models of the power-linear functions and power functions. The analysis of the parameters obtained by regression and validation of the empirical models showed that they were acceptable by using either the measured data of separate gravel size group or those of all the three gravel size groups having a large size range. Furthermore, the regression parameters of the curved surfaces for the soil-gravel mixtures with a large range of gravel content could be determined from the water retention data of the soil-gravel mixtures with two representative gravel contents or bulk densities. Such revised water retention models are potentially applicable in regional or large scale field investigations of significantly heterogeneous media, where various gravel sizes and different gravel contents are present. PMID:23555040

The understanding and experience of mixed emotions in 3-5-year-old children.

PubMed

Smith, Joshua P; Glass, Daniel J; Fireman, Gary

2015-01-01

The term mixed emotions refers to the presence of two opposite-valence emotions toward a single target. Identifying when children begin to report experiencing and understanding mixed emotions is critical in identifying how skills such as adaptive functioning, coping strategies, environmental understanding, and socioemotional competence emerge. Prior research has shown that children as young as 5 years old can understand and experience mixed emotion, but perhaps appropriately sensitive methodologies can reveal these abilities in younger children. The present study evaluated 57 children between 3 and 5 years old for mixed emotion experience and understanding using an animated video clip in which a character experiences a mixed emotional episode. Ordinal logistic regression was utilized to examine the relation of gender, attention, and understanding of content to experience and understanding of mixed emotion. While only 12% of children reported experiencing mixed emotion while watching the clip, 49% of children-some as young as 3 years old-were able to recognize the mixed emotional experience of the character. Thus, mixed emotion understanding emerges earlier than previously identified and the expression of understanding may develop independently of the ability to report mixed emotion experience. These findings are discussed in relation to cognitive and developmental considerations.

Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program.

PubMed

Chen, Robert; Sun, Jimeng; Dittus, Robert S; Fabbri, Daniel; Kirby, Jacqueline; Laffer, Cheryl L; McNaughton, Candace D; Malin, Bradley

2016-01-04

The goal of this study is to devise a machine learning framework to assist care coordination programs in prognostic stratification to design and deliver personalized care plans and to allocate financial and medical resources effectively. This study is based on a de-identified cohort of 2,521 hypertension patients from a chronic care coordination program at the Vanderbilt University Medical Center. Patients were modeled as vectors of features derived from electronic health records (EHRs) over a six-year period. We applied a stepwise regression to identify risk factors associated with a decrease in mean arterial pressure of at least 2 mmHg after program enrollment. The resulting features were subsequently validated via a logistic regression classifier. Finally, risk factors were applied to group the patients through model-based clustering. We identified a set of predictive features that consisted of a mix of demographic, medication, and diagnostic concepts. Logistic regression over these features yielded an area under the ROC curve (AUC) of 0.71 (95% CI: [0.67, 0.76]). Based on these features, four clinically meaningful groups are identified through clustering - two of which represented patients with more severe disease profiles, while the remaining represented patients with mild disease profiles. Patients with hypertension can exhibit significant variation in their blood pressure control status and responsiveness to therapy. Yet this work shows that a clustering analysis can generate more homogeneous patient groups, which may aid clinicians in designing and implementing customized care programs. The study shows that predictive modeling and clustering using EHR data can be beneficial for providing a systematic, generalized approach for care providers to tailor their management approach based upon patient-level factors.

Proximal sensing of within-field mycotoxin variation - a case study in Northeast Germany

NASA Astrophysics Data System (ADS)

Mueller, Marina; Koszinski, Sylvia; Bangs, Donovan E.; Wehrhan, Marc; Ullrich, Andreas; Verch, Gernot; Brenning, Alexander

2017-04-01

Fusarium head blight is a global problem in agriculture that results in yield losses and, more seriously, produces harmful toxins that enter the food chain. This study (Müller et al. 2016) builds on previous research identifying within-field humidity as an important factor in infection processes by Fusarium fungi and its mycotoxin production. Environmental variables describing topographic control of humidity (topographic wetness index TWI), soil texture and related moisture by electrical conductivity (ECa), and canopy humidity by density (normalized difference vegetation index NDVI) were explored in their relationship to the fungal infection rates and mycotoxin accumulation. Field studies at four sites in NE German Lowlands were performed in 2009 and 2011. Sites differed slightly in soil textural properties and, more pronounced, mean annual precipitation. Sampling positions were selected by usage of NDVI values range from remote sensing data base. Environmental data included elevation and its derivatives like topographic wetness index (TWI) from a DEM25, electrical conductivity distribution maps (5 x 5 m) based on EM38DD survey and, orthorectified RapidEye imagery (5 x 5 m2) with resulting NDVI distributions across the field sites. Grain yield, fungal infection rate, microbiological characteristics and mycotoxin accumulation were determined at 223 field positions. Statistical analysis incorporated Spearman rank order correlations and three regression methods (censored regression models, linear mixed-effects models and spatial linear mixed-effects models). Kriging was used to visualize the spatial patterns and trends. All analyses were performed by R software. In 2011, a more wet year than 2009, high Fusarium infection rates and a high concentration of mycotoxins were stated, the latter once exceeding EU threshold values. For both years associations between NDVI and microbiological variables were found, but being more pronounced and more often significant for 2011 than for 2009. ECa was only related with deoxynivalenol concentration (DON) and abundance of trichothecene-producing fusaria (tri6 gene copy number) in 2009 and, to DON and zearalenone (ZEA) in 2011. In contrast to former findings no correlations were found between TWI and mycological data. NDVI and, less importantly, ECa were essential predictors in all three regression models. Mycotoxins DON and ZEA distribution maps could be interpolated by kriging with internal drift based on these two proximal predictor variables. Providing spatial patterns of mycotoxigenic fungi and its effects may be used to infer mycotoxin hot spots, to develop models for risk assessment and, to manage plant and crop treatments or even harvest. Müller, M.E.H., Koszinski, S., Bangs, D.E. et al. Precision Agric (2016) 17: 698. doi:10.1007/s11119-016-9444-y

Statistical and Biophysical Models for Predicting Total and Outdoor Water Use in Los Angeles

NASA Astrophysics Data System (ADS)

Mini, C.; Hogue, T. S.; Pincetl, S.

2012-04-01

Modeling water demand is a complex exercise in the choice of the functional form, techniques and variables to integrate in the model. The goal of the current research is to identify the determinants that control total and outdoor residential water use in semi-arid cities and to utilize that information in the development of statistical and biophysical models that can forecast spatial and temporal urban water use. The City of Los Angeles is unique in its highly diverse socio-demographic, economic and cultural characteristics across neighborhoods, which introduces significant challenges in modeling water use. Increasing climate variability also contributes to uncertainties in water use predictions in urban areas. Monthly individual water use records were acquired from the Los Angeles Department of Water and Power (LADWP) for the 2000 to 2010 period. Study predictors of residential water use include socio-demographic, economic, climate and landscaping variables at the zip code level collected from US Census database. Climate variables are estimated from ground-based observations and calculated at the centroid of each zip code by inverse-distance weighting method. Remotely-sensed products of vegetation biomass and landscape land cover are also utilized. Two linear regression models were developed based on the panel data and variables described: a pooled-OLS regression model and a linear mixed effects model. Both models show income per capita and the percentage of landscape areas in each zip code as being statistically significant predictors. The pooled-OLS model tends to over-estimate higher water use zip codes and both models provide similar RMSE values.Outdoor water use was estimated at the census tract level as the residual between total water use and indoor use. This residual is being compared with the output from a biophysical model including tree and grass cover areas, climate variables and estimates of evapotranspiration at very high spatial resolution. A genetic algorithm based model (Shuffled Complex Evolution-UA; SCE-UA) is also being developed to provide estimates of the predictions and parameters uncertainties and to compare against the linear regression models. Ultimately, models will be selected to undertake predictions for a range of climate change and landscape scenarios. Finally, project results will contribute to a better understanding of water demand to help predict future water use and implement targeted landscaping conservation programs to maintain sustainable water needs for a growing population under uncertain climate variability.

The Impact of Pay on Navy Physician Retention in a Health Care Reform Environment.

DTIC Science & Technology

1998-03-01

Analyses 33 6. Generalist Physician Supply Under Alternative Specialty Mix 33 7. Specialist Physician Supply Under Alternative Specialty Mix 34 8...Fiscal Years 1992 to 1997 40 5. Comparison of proposed alternative pay plans 54 6. Evaluation of expected outcomes of alternative pay plans 55 7...Explanatory variables with the expected sign of regression coefficient 76 8. Comparison of Navy physicians’ pay to alternative civilian pay for selected

Exploring the effects of coexisting amyloid in subcortical vascular cognitive impairment.

PubMed

Dao, Elizabeth; Hsiung, Ging-Yuek Robin; Sossi, Vesna; Jacova, Claudia; Tam, Roger; Dinelle, Katie; Best, John R; Liu-Ambrose, Teresa

2015-10-12

Mixed pathology, particularly Alzheimer's disease with cerebrovascular lesions, is reported as the second most common cause of dementia. Research on mixed dementia typically includes people with a primary AD diagnosis and hence, little is known about the effects of co-existing amyloid pathology in people with vascular cognitive impairment (VCI). The purpose of this study was to understand whether individual differences in amyloid pathology might explain variations in cognitive impairment among individuals with clinical subcortical VCI (SVCI). Twenty-two participants with SVCI completed an (11)C Pittsburgh compound B (PIB) position emission tomography (PET) scan to quantify global amyloid deposition. Cognitive function was measured using: 1) MOCA; 2) ADAS-Cog; 3) EXIT-25; and 4) specific executive processes including a) Digits Forward and Backwards Test, b) Stroop-Colour Word Test, and c) Trail Making Test. To assess the effect of amyloid deposition on cognitive function we conducted Pearson bivariate correlations to determine which cognitive measures to include in our regression models. Cognitive variables that were significantly correlated with PIB retention values were entered in a hierarchical multiple linear regression analysis to determine the unique effect of amyloid on cognitive function. We controlled for age, education, and ApoE ε4 status. Bivariate correlation results showed that PIB binding was significantly correlated with ADAS-Cog (p < 0.01) and MOCA (p < 0.01); increased PIB binding was associated with worse cognitive function on both cognitive measures. PIB binding was not significantly correlated with the EXIT-25 or with specific executive processes (p > 0.05). Regression analyses controlling for age, education, and ApoE ε4 status indicated an independent association between PIB retention and the ADAS-Cog (adjusted R-square change of 15.0%, Sig F Change = 0.03). PIB retention was also independently associated with MOCA scores (adjusted R-Square Change of 27.0%, Sig F Change = 0.02). We found that increased global amyloid deposition was significantly associated with greater memory and executive dysfunctions as measured by the ADAS-Cog and MOCA. Our findings point to the important role of co-existing amyloid deposition for cognitive function in those with a primary SVCI diagnosis. As such, therapeutic approaches targeting SVCI must consider the potential role of amyloid for the optimal care of those with mixed dementia. NCT01027858.

Low modeled ozone production suggests underestimation of precursor emissions (especially NOx) in Europe

NASA Astrophysics Data System (ADS)

Oikonomakis, Emmanouil; Aksoyoglu, Sebnem; Ciarelli, Giancarlo; Baltensperger, Urs; Prévôt, André Stephan Henry

2018-02-01

High surface ozone concentrations, which usually occur when photochemical ozone production takes place, pose a great risk to human health and vegetation. Air quality models are often used by policy makers as tools for the development of ozone mitigation strategies. However, the modeled ozone production is often not or not enough evaluated in many ozone modeling studies. The focus of this work is to evaluate the modeled ozone production in Europe indirectly, with the use of the ozone-temperature correlation for the summer of 2010 and to analyze its sensitivity to precursor emissions and meteorology by using the regional air quality model, the Comprehensive Air Quality Model with Extensions (CAMx). The results show that the model significantly underestimates the observed high afternoon surface ozone mixing ratios (≥ 60 ppb) by 10-20 ppb and overestimates the lower ones (< 40 ppb) by 5-15 ppb, resulting in a misleading good agreement with the observations for average ozone. The model also underestimates the ozone-temperature regression slope by about a factor of 2 for most of the measurement stations. To investigate the impact of emissions, four scenarios were tested: (i) increased volatile organic compound (VOC) emissions by a factor of 1.5 and 2 for the anthropogenic and biogenic VOC emissions, respectively, (ii) increased nitrogen oxide (NOx) emissions by a factor of 2, (iii) a combination of the first two scenarios and (iv) increased traffic-only NOx emissions by a factor of 4. For southern, eastern, and central (except the Benelux area) Europe, doubling NOx emissions seems to be the most efficient scenario to reduce the underestimation of the observed high ozone mixing ratios without significant degradation of the model performance for the lower ozone mixing ratios. The model performance for ozone-temperature correlation is also better when NOx emissions are doubled. In the Benelux area, however, the third scenario (where both NOx and VOC emissions are increased) leads to a better model performance. Although increasing only the traffic NOx emissions by a factor of 4 gave very similar results to the doubling of all NOx emissions, the first scenario is more consistent with the uncertainties reported by other studies than the latter, suggesting that high uncertainties in NOx emissions might originate mainly from the road-transport sector rather than from other sectors. The impact of meteorology was examined with three sensitivity tests: (i) increased surface temperature by 4 °C, (ii) reduced wind speed by 50 % and (iii) doubled wind speed. The first two scenarios led to a consistent increase in all surface ozone mixing ratios, thus improving the model performance for the high ozone values but significantly degrading it for the low ozone values, while the third scenario had exactly the opposite effects. Overall, the modeled ozone is predicted to be more sensitive to its precursor emissions (especially traffic NOx) and therefore their uncertainties, which seem to be responsible for the model underestimation of the observed high ozone mixing ratios and ozone production.

An in-depth assessment of a diagnosis-based risk adjustment model based on national health insurance claims: the application of the Johns Hopkins Adjusted Clinical Group case-mix system in Taiwan.

PubMed

Chang, Hsien-Yen; Weiner, Jonathan P

2010-01-18

Diagnosis-based risk adjustment is becoming an important issue globally as a result of its implications for payment, high-risk predictive modelling and provider performance assessment. The Taiwanese National Health Insurance (NHI) programme provides universal coverage and maintains a single national computerized claims database, which enables the application of diagnosis-based risk adjustment. However, research regarding risk adjustment is limited. This study aims to examine the performance of the Adjusted Clinical Group (ACG) case-mix system using claims-based diagnosis information from the Taiwanese NHI programme. A random sample of NHI enrollees was selected. Those continuously enrolled in 2002 were included for concurrent analyses (n = 173,234), while those in both 2002 and 2003 were included for prospective analyses (n = 164,562). Health status measures derived from 2002 diagnoses were used to explain the 2002 and 2003 health expenditure. A multivariate linear regression model was adopted after comparing the performance of seven different statistical models. Split-validation was performed in order to avoid overfitting. The performance measures were adjusted R2 and mean absolute prediction error of five types of expenditure at individual level, and predictive ratio of total expenditure at group level. The more comprehensive models performed better when used for explaining resource utilization. Adjusted R2 of total expenditure in concurrent/prospective analyses were 4.2%/4.4% in the demographic model, 15%/10% in the ACGs or ADGs (Aggregated Diagnosis Group) model, and 40%/22% in the models containing EDCs (Expanded Diagnosis Cluster). When predicting expenditure for groups based on expenditure quintiles, all models underpredicted the highest expenditure group and overpredicted the four other groups. For groups based on morbidity burden, the ACGs model had the best performance overall. Given the widespread availability of claims data and the superior explanatory power of claims-based risk adjustment models over demographics-only models, Taiwan's government should consider using claims-based models for policy-relevant applications. The performance of the ACG case-mix system in Taiwan was comparable to that found in other countries. This suggested that the ACG system could be applied to Taiwan's NHI even though it was originally developed in the USA. Many of the findings in this paper are likely to be relevant to other diagnosis-based risk adjustment methodologies.

Trends in stratospheric ozone profiles using functional mixed models

NASA Astrophysics Data System (ADS)

Park, A. Y.; Guillas, S.; Petropavlovskikh, I.

2013-05-01

This paper is devoted to the modeling of altitude-dependent patterns of ozone variations over time. Umkher ozone profiles (quarter of Umkehr layer) from 1978 to 2011 are investigated at two locations: Boulder (USA) and Arosa (Switzerland). The study consists of two statistical stages. First we approximate ozone profiles employing an appropriate basis. To capture primary modes of ozone variations without losing essential information, a functional principal component analysis is performed as it penalizes roughness of the function and smooths excessive variations in the shape of the ozone profiles. As a result, data driven basis functions are obtained. Secondly we estimate the effects of covariates - month, year (trend), quasi biennial oscillation, the Solar cycle, arctic oscillation and the El Niño/Southern Oscillation cycle - on the principal component scores of ozone profiles over time using generalized additive models. The effects are smooth functions of the covariates, and are represented by knot-based regression cubic splines. Finally we employ generalized additive mixed effects models incorporating a more complex error structure that reflects the observed seasonality in the data. The analysis provides more accurate estimates of influences and trends, together with enhanced uncertainty quantification. We are able to capture fine variations in the time evolution of the profiles such as the semi-annual oscillation. We conclude by showing the trends by altitude over Boulder. The strongly declining trends over 2003-2011 for altitudes of 32-64 hPa show that stratospheric ozone is not yet fully recovering.

Modeling Manpower and Equipment Productivity in Tall Building Construction Projects

NASA Astrophysics Data System (ADS)

Mudumbai Krishnaswamy, Parthasarathy; Rajiah, Murugasan; Vasan, Ramya

2017-12-01

Tall building construction projects involve two critical resources of manpower and equipment. Their usage, however, widely varies due to several factors affecting their productivity. Currently, no systematic study for estimating and increasing their productivity is available. What is prevalent is the use of empirical data, experience of similar projects and assumptions. As tall building projects are here to stay and increase, to meet the emerging demands in ever shrinking urban spaces, it is imperative to explore ways and means of scientific productivity models for basic construction activities: concrete, reinforcement, formwork, block work and plastering for the input of specific resources in a mixed environment of manpower and equipment usage. Data pertaining to 72 tall building projects in India were collected and analyzed. Then, suitable productivity estimation models were developed using multiple linear regression analysis and validated using independent field data. It is hoped that the models developed in the study will be useful for quantity surveyors, cost engineers and project managers to estimate productivity of resources in tall building projects.

Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions.

PubMed

Choi, Yoonha; Liu, Tiffany Ting; Pankratz, Daniel G; Colby, Thomas V; Barth, Neil M; Lynch, David A; Walsh, P Sean; Raghu, Ganesh; Kennedy, Giulia C; Huang, Jing

2018-05-09

We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects. We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~ 0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set. We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.

Modeling of Anaerobic Digestion with a Focus on Estimation of Hydrolysis Constants at 35, 55, and 60 °C.

PubMed

Haghighatafshar, Salar; Ossiansson, Elin; Koch, Konrad; Kjerstadius, Hamse; Jansen, Jes la Cour; Davidsson, Åsa

2015-07-01

Hydrolysis constants of mixed sludge at 35, 55, and 60 °C were found to be 0.32, 0.44, and 0.50 1/d, respectively, in pilot-scale, semicontinuously operated anaerobic digesters. The hydrolysis constants and estimated chemical oxygen demand fractions in the feed were introduced to a mathematical model for anaerobic digestion published by Siegrist et al. (2002), which is similar to Anaerobic Digestion Model No. 1. First-order and Monod-type kinetics were tested for estimation of hydrolysis constants. The applied kinetics were found to affect the outcome of the regression study. Moreover, the free ammonia inhibition model was excluded for both propionate oxidation and acetate conversion, thanks to the apparent acclimatized biomass. No substantial accumulation of volatile fatty acids was observed in the reactors at 35, 55, and 60 °C, corresponding to free ammonia nitrogen concentrations of about 20, 110, and 130 g N/m³, respectively.

MixSIAR: advanced stable isotope mixing models in R

EPA Science Inventory

Background/Question/Methods The development of stable isotope mixing models has coincided with modeling products (e.g. IsoSource, MixSIR, SIAR), where methodological advances are published in parity with software packages. However, while mixing model theory has recently been ex...

Geographical variation in the incidence of childhood leukaemia in Manitoba.

PubMed

Torabi, Mahmoud; Singh, Harminder; Galloway, Katie; Israels, Sara J

2015-11-01

Identification of geographical areas and ecological factors associated with higher incidence of childhood leukaemias can direct further study for preventable factors and location of health services to manage such individuals. The aim of this study was to describe the geographical variation and the socio-demographic factors associated with childhood leukaemia in Manitoba. Information on childhood leukaemia incidence between 1992 and 2008 was obtained from the Canadian Cancer Registry and the socio-demographic characteristics for the area of residence from the 2006 Canadian Census. Bayesian spatial Poisson mixed models were used to describe the geographical variation of childhood leukaemia and to determine the association between childhood leukaemia and socio-demographic factors. The south-eastern part of the province had a higher incidence of childhood leukaemia than other parts of the province. In the age and sex-adjusted Poisson regression models, areas with higher proportions of visible minorities and immigrant residents had higher childhood leukaemia incidence rate ratios. In the saturated Poisson regression model, the childhood leukaemia rates were higher in areas with higher proportions of immigrant residents. Unemployment rates were not a significant factor in leukaemia incidence. In Manitoba, areas with higher proportions of immigrants experience higher incidence rates of childhood leukaemia. We have identified geographical areas with higher incidence, which require further study and attention. © 2015 The Authors. Journal of Paediatrics and Child Health © 2015 Paediatrics and Child Health Division (Royal Australasian College of Physicians).

IL-8 predicts pediatric oncology patients with febrile neutropenia at low risk for bacteremia.

PubMed

Cost, Carrye R; Stegner, Martha M; Leonard, David; Leavey, Patrick

2013-04-01

Despite a low bacteremia rate, pediatric oncology patients are frequently admitted for febrile neutropenia. A pediatric risk prediction model with high sensitivity to identify patients at low risk for bacteremia is not available. We performed a single-institution prospective cohort study of pediatric oncology patients with febrile neutropenia to create a risk prediction model using clinical factors, respiratory viral infection, and cytokine expression. Pediatric oncology patients with febrile neutropenia were enrolled between March 30, 2010 and April 1, 2011 and managed per institutional protocol. Blood samples for C-reactive protein and cytokine expression and nasopharyngeal swabs for respiratory viral testing were obtained. Medical records were reviewed for clinical data. Statistical analysis utilized mixed multiple logistic regression modeling. During the 12-month period, 195 febrile neutropenia episodes were enrolled. There were 24 (12%) episodes of bacteremia. Univariate analysis revealed several factors predictive for bacteremia, and interleukin (IL)-8 was the most predictive variable in the multivariate stepwise logistic regression. Low serum IL-8 predicted patients at low risk for bacteremia with a sensitivity of 0.9 and negative predictive value of 0.98. IL-8 is a highly sensitive predictor for patients at low risk for bacteremia. IL-8 should be utilized in a multi-institution prospective trial to assign risk stratification to pediatric patients admitted with febrile neutropenia.

Heterogeneity in drug abuse among juvenile offenders: is mixture regression more informative than standard regression?

PubMed

Montgomery, Katherine L; Vaughn, Michael G; Thompson, Sanna J; Howard, Matthew O

2013-11-01

Research on juvenile offenders has largely treated this population as a homogeneous group. However, recent findings suggest that this at-risk population may be considerably more heterogeneous than previously believed. This study compared mixture regression analyses with standard regression techniques in an effort to explain how known factors such as distress, trauma, and personality are associated with drug abuse among juvenile offenders. Researchers recruited 728 juvenile offenders from Missouri juvenile correctional facilities for participation in this study. Researchers investigated past-year substance use in relation to the following variables: demographic characteristics (gender, ethnicity, age, familial use of public assistance), antisocial behavior, and mental illness symptoms (psychopathic traits, psychiatric distress, and prior trauma). Results indicated that standard and mixed regression approaches identified significant variables related to past-year substance use among this population; however, the mixture regression methods provided greater specificity in results. Mixture regression analytic methods may help policy makers and practitioners better understand and intervene with the substance-related subgroups of juvenile offenders.

Lung functions among patients with pulmonary tuberculosis in Dar es Salaam - a cross-sectional study.

PubMed

Manji, Mohamed; Shayo, Grace; Mamuya, Simon; Mpembeni, Rose; Jusabani, Ahmed; Mugusi, Ferdinand

2016-04-23

Approximately 40-60 % of patients remain sufferers of sequela of obstructive, restrictive or mixed patterns of lung disease despite treatment for pulmonary tuberculosis (PTB). The prevalence of these abnormalities in Tanzania remains unknown. A descriptive cross-sectional study was carried out among 501 patients with PTB who had completed at least 20 weeks of treatment. These underwent spirometry and their lung functions were classified as normal or abnormal (obstructive, restrictive or mixed). Logistic regression models were used to explore factors associated with abnormal lung functions. Abnormal lung functions were present in 371 (74 %) patients. There were 210 (42 %) patients with obstructive, 65 (13 %) patients with restrictive and 96 (19 %) patients with mixed patterns respectively. Significant factors associated with abnormal lung functions included recurrent PTB (Adj OR 2.8, CI 1.274 - 6.106), Human Immunodeficiency Virus (HIV) negative status (Adj OR 1.7, CI 1.055 - 2.583), age more than 40 years (Adj OR 1.7, CI 1.080 - 2.804) and male sex (Adj OR 1.7, CI 1.123 - 2.614). The prevalence of abnormal lung functions is high and it is associated with male sex, age older than 40 years, recurrent PTB and HIV negative status.

An optimal proportion of mixing broad-leaved forest for enhancing the effective productivity of moso bamboo.

PubMed

Cheng, Xiao-Fei; Shi, Pei-Jian; Hui, Cang; Wang, Fu-Sheng; Liu, Guo-Hua; Li, Bai-Lian

2015-04-01

Moso bamboos (Phyllostachys edulis) are important forestry plants in southern China, with substantial roles to play in regional economic and ecological systems. Mixing broad-leaved forests and moso bamboos is a common management practice in China, and it is fundamental to elucidate the interactions between broad-leaved trees and moso bamboos for ensuring the sustainable provision of ecosystem services. We examine how the proportion of broad-leaved forest in a mixed managed zone, topology, and soil profile affects the effective productivity of moso bamboos (i.e., those with significant economic value), using linear regression and generalized additive models. Bamboo's diameter at breast height follows a Weibull distribution. The importance of these variables to bamboo productivity is, respectively, slope (25.9%), the proportion of broad-leaved forest (24.8%), elevation (23.3%), gravel content by volume (16.6%), slope location (8.3%), and soil layer thickness (1.2%). Highest productivity is found on the 25° slope, with a 600-m elevation, and 30% broad-leaved forest. As such, broad-leaved forest in the upper slope can have a strong influence on the effective productivity of moso bamboo, ranking only after slope and before elevation. These factors can be considered in future management practice.

Occupational dermatitis in Danish gardeners and greenhouse workers (III). Compositae-related symptoms.

PubMed

Paulsen, E; Søgaard, J; Andersen, K E

1998-03-01

The clinical part of the study aimed at describing epidemiological and diagnostic aspects of occupational Compositae dermatitis. Patch testing with the sesquiterpene lactone (SL) and Compositae mixes, feverfew extract and supplementary allergens in 250 selected gardeners showed Compositae allergy in 25, 17 females and 8 males. 24 were possibly occupationally sensitized. The mean age was lower and the preponderance of women higher compared to classical Compositae dermatitis, and the distribution and course of the dermatitis most often did not differ from other occupational plant dermatoses. The Compositae mix detected 2x as many as the SL mix, and the overall detection rate with both was 76%, making aimed patch testing necessary. Chrysanthemum (Dendranthema), marguerite daisy (Argyranthemum frutescens) and lettuce (Lactuca sativa) were frequent sensitizers. Occupational type I allergy to Compositae comprised sensitization to Gerbera, chrysanthemum, lettuce, Senecio cruentus and Aster. Among 1657 respondents in the questionnaire part of the study, 824 had worked with Compositae, and 160 (19%) reported occupational Compositae-related symptoms of skin and mucous membranes. Possible risk factors for the development of these were assessed in a stepwise logistic regression model and a history of childhood eczema, hay fever and duration of exposure were significantly associated with Compositae-related irritant and allergic symptoms in both sexes.

Bayesian Poisson hierarchical models for crash data analysis: Investigating the impact of model choice on site-specific predictions.

PubMed

Khazraee, S Hadi; Johnson, Valen; Lord, Dominique

2018-08-01

The Poisson-gamma (PG) and Poisson-lognormal (PLN) regression models are among the most popular means for motor vehicle crash data analysis. Both models belong to the Poisson-hierarchical family of models. While numerous studies have compared the overall performance of alternative Bayesian Poisson-hierarchical models, little research has addressed the impact of model choice on the expected crash frequency prediction at individual sites. This paper sought to examine whether there are any trends among candidate models predictions e.g., that an alternative model's prediction for sites with certain conditions tends to be higher (or lower) than that from another model. In addition to the PG and PLN models, this research formulated a new member of the Poisson-hierarchical family of models: the Poisson-inverse gamma (PIGam). Three field datasets (from Texas, Michigan and Indiana) covering a wide range of over-dispersion characteristics were selected for analysis. This study demonstrated that the model choice can be critical when the calibrated models are used for prediction at new sites, especially when the data are highly over-dispersed. For all three datasets, the PIGam model would predict higher expected crash frequencies than would the PLN and PG models, in order, indicating a clear link between the models predictions and the shape of their mixing distributions (i.e., gamma, lognormal, and inverse gamma, respectively). The thicker tail of the PIGam and PLN models (in order) may provide an advantage when the data are highly over-dispersed. The analysis results also illustrated a major deficiency of the Deviance Information Criterion (DIC) in comparing the goodness-of-fit of hierarchical models; models with drastically different set of coefficients (and thus predictions for new sites) may yield similar DIC values, because the DIC only accounts for the parameters in the lowest (observation) level of the hierarchy and ignores the higher levels (regression coefficients). Copyright © 2018. Published by Elsevier Ltd.

Longitudinal comparative evaluation of the equivalence of an integrated peer-support and clinical staffing model for residential mental health rehabilitation: a mixed methods protocol incorporating multiple stakeholder perspectives.

PubMed

Parker, Stephen; Dark, Frances; Newman, Ellie; Korman, Nicole; Meurk, Carla; Siskind, Dan; Harris, Meredith

2016-06-02

A novel staffing model integrating peer support workers and clinical staff within a unified team is being trialled at community based residential rehabilitation units in Australia. A mixed-methods protocol for the longitudinal evaluation of the outcomes, expectations and experiences of care by consumers and staff under this staffing model in two units will be compared to one unit operating a traditional clinical staffing. The study is unique with regards to the context, the longitudinal approach and consideration of multiple stakeholder perspectives. The longitudinal mixed methods design integrates a quantitative evaluation of the outcomes of care for consumers at three residential rehabilitation units with an applied qualitative research methodology. The quantitative component utilizes a prospective cohort design to explore whether equivalent outcomes are achieved through engagement at residential rehabilitation units operating integrated and clinical staffing models. Comparative data will be available from the time of admission, discharge and 12-month period post-discharge from the units. Additionally, retrospective data for the 12-month period prior to admission will be utilized to consider changes in functioning pre and post engagement with residential rehabilitation care. The primary outcome will be change in psychosocial functioning, assessed using the total score on the Health of the Nation Outcome Scales (HoNOS). Planned secondary outcomes will include changes in symptomatology, disability, recovery orientation, carer quality of life, emergency department presentations, psychiatric inpatient bed days, and psychological distress and wellbeing. Planned analyses will include: cohort description; hierarchical linear regression modelling of the predictors of change in HoNOS following CCU care; and descriptive comparisons of the costs associated with the two staffing models. The qualitative component utilizes a pragmatic approach to grounded theory, with collection of data from consumers and staff at multiple time points exploring their expectations, experiences and reflections on the care provided by these services. It is expected that the new knowledge gained through this study will guide the adaptation of these and similar services. For example, if differential outcomes are achieved for consumers under the integrated and clinical staffing models this may inform staffing guidelines.

Racial bullying and adolescent substance use: An examination of school-attending young adolescents in the United States.

PubMed

Stone, Andrea L; Carlisle, Shauna K

2017-01-01

This article examines the association between race and racial bullying (bullying due to one's race), in relation to youth substance use in school attending young adolescents in the United States. Weighted unadjusted and adjusted logistic regression models were run to assess if racial bullying involvement was associated with youth substance use. Data for this study come from the Health Behaviors in School-Aged Children survey (n = 7,585). An association between racial bullying status (not involve, bullying victim, bullying perpetrator, or mixed bullying victim/perpetrator) and youth substance was identified in this study. Racial bully perpetrators were most likely to have used cigarettes, alcohol, and marijuana, followed by youth in the mixed victim/perpetrator group. When analyses were stratified by race, non-Hispanic White and Hispanic youth experienced an increased risk of cigarette, alcohol, and marijuana use if in the perpetrator or mixed group (compared to those not involved with racial bullying). Non-Hispanic White and Asian youth were also more likely to report marijuana use if in the victim group. Non-Hispanic Black youth were more likely to use alcohol and marijuana if they were a perpetrator or in the mixed group, but they were not more likely to use cigarettes. Differences appear to exist in relation to racial bullying experience and substance across racial/ethnic group among youth in grades 7-10. Implications for prevention and educational professionals are discussed.

The influence of maternal and paternal history on stone composition and clinical course of calcium nephrolithiasis in subjects aged between 15 and 25.

PubMed

Guerra, Angela; Ticinesi, Andrea; Allegri, Franca; Nouvenne, Antonio; Pinelli, Silvana; Folesani, Giuseppina; Lauretani, Fulvio; Maggio, Marcello; Borghi, Loris; Meschi, Tiziana

2016-11-01

Our aim was to compare the influence of maternal history of stones (MHS) and paternal history of stones (PHS) on composition of calculi and disease course in a group of patients with calcium nephrolithiasis (CN) aged between 15 and 25, the age range with the maximal influence of family history on disease expression. One-hundred thirty-five patients (68 F) with CN and one stone-forming parent were retrospectively selected from the database of our outpatient stone clinic, and categorized according to MHS or PHS. Data about stone disease course and composition of passed calculi, determined by chemical analysis or Fourier-transformed infrared spectrophotometry, were collected together with information on blood chemistry and 24-h urinary profile of lithogenic risk. The characteristics of disease course and stone composition were compared using logistic regression tests adjusted for age, sex, and BMI or analysis of covariance where appropriate. Patients with MHS (n = 46) had significantly higher urinary calcium/creatinine ratio and ammonium, a higher prevalence of urological treatments (57 vs 27 %, p < 0.001) and mixed calcium oxalate/calcium phosphate stone composition (69 vs 35 %, p = 0.002) than those with PHS. At multivariate logistic regression models, MHS was independently associated with urological treatments (OR 4.5, 95 %CI 1.9-10.7, p < 0.001) and the formation of calculi with mixed calcium oxalate/calcium phosphate composition (OR 5.8, 95 %CI 1.9-17.9, p = 0.002). The method of stone analysis did not affect this result. In conclusion, in subjects aged 15-25, MHS is associated with mixed calcium stones and with a higher risk for urological procedures, and should be, therefore, considered in the management of urolithiasis.

Realist explanatory theory building method for social epidemiology: a protocol for a mixed method multilevel study of neighbourhood context and postnatal depression.

PubMed

Eastwood, John G; Jalaludin, Bin B; Kemp, Lynn A

2014-01-01

A recent criticism of social epidemiological studies, and multi-level studies in particular has been a paucity of theory. We will present here the protocol for a study that aims to build a theory of the social epidemiology of maternal depression. We use a critical realist approach which is trans-disciplinary, encompassing both quantitative and qualitative traditions, and that assumes both ontological and hierarchical stratification of reality. We describe a critical realist Explanatory Theory Building Method comprising of an: 1) emergent phase, 2) construction phase, and 3) confirmatory phase. A concurrent triangulated mixed method multilevel cross-sectional study design is described. The Emergent Phase uses: interviews, focus groups, exploratory data analysis, exploratory factor analysis, regression, and multilevel Bayesian spatial data analysis to detect and describe phenomena. Abductive and retroductive reasoning will be applied to: categorical principal component analysis, exploratory factor analysis, regression, coding of concepts and categories, constant comparative analysis, drawing of conceptual networks, and situational analysis to generate theoretical concepts. The Theory Construction Phase will include: 1) defining stratified levels; 2) analytic resolution; 3) abductive reasoning; 4) comparative analysis (triangulation); 5) retroduction; 6) postulate and proposition development; 7) comparison and assessment of theories; and 8) conceptual frameworks and model development. The strength of the critical realist methodology described is the extent to which this paradigm is able to support the epistemological, ontological, axiological, methodological and rhetorical positions of both quantitative and qualitative research in the field of social epidemiology. The extensive multilevel Bayesian studies, intensive qualitative studies, latent variable theory, abductive triangulation, and Inference to Best Explanation provide a strong foundation for Theory Construction. The study will contribute to defining the role that realism and mixed methods can play in explaining the social determinants and developmental origins of health and disease.

Logistic regression function for detection of suspicious performance during baseline evaluations using concussion vital signs.

PubMed

Hill, Benjamin David; Womble, Melissa N; Rohling, Martin L

2015-01-01

This study utilized logistic regression to determine whether performance patterns on Concussion Vital Signs (CVS) could differentiate known groups with either genuine or feigned performance. For the embedded measure development group (n = 174), clinical patients and undergraduate students categorized as feigning obtained significantly lower scores on the overall test battery mean for the CVS, Shipley-2 composite score, and California Verbal Learning Test-Second Edition subtests than did genuinely performing individuals. The final full model of 3 predictor variables (Verbal Memory immediate hits, Verbal Memory immediate correct passes, and Stroop Test complex reaction time correct) was significant and correctly classified individuals in their known group 83% of the time (sensitivity = .65; specificity = .97) in a mixed sample of young-adult clinical cases and simulators. The CVS logistic regression function was applied to a separate undergraduate college group (n = 378) that was asked to perform genuinely and identified 5% as having possibly feigned performance indicating a low false-positive rate. The failure rate was 11% and 16% at baseline cognitive testing in samples of high school and college athletes, respectively. These findings have particular relevance given the increasing use of computerized test batteries for baseline cognitive testing and return-to-play decisions after concussion.

Methods of estimating or accounting for neighborhood associations with health using complex survey data.

PubMed

Brumback, Babette A; Cai, Zhuangyu; Dailey, Amy B

2014-05-15

Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. The goal of the present article is to review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and 2 epidemiologic data analyses. The first analysis addresses determinants of repeat mammography screening use using data from the 2005 National Health Interview Survey. The second analysis addresses disparities in preventive oral health care using data from the 2008 Florida Behavioral Risk Factor Surveillance System Survey.

BIODEGRADATION PROBABILITY PROGRAM (BIODEG)

EPA Science Inventory

The Biodegradation Probability Program (BIODEG) calculates the probability that a chemical under aerobic conditions with mixed cultures of microorganisms will biodegrade rapidly or slowly. It uses fragment constants developed using multiple linear and non-linear regressions and d...

Monitoring outcomes for the Medicare Advantage program: methods and application of the VR-12 for evaluation of plans.

PubMed

Kazis, Lewis E; Selim, Alfredo J; Rogers, William; Qian, Shirley X; Brazier, John

2012-01-01

The Veterans RAND 12-Item Health Survey (VR-12) is one of the major patient-reported outcomes for ranking the Medicare Advantage (MA) plans in the Health Outcomes Survey (HOS). Approaches for scoring physical and mental health are given using contemporary norms and regression estimators. A new metric approach for the VR-12 called the "VR-6D" is presented with case-mix adjustments for monitoring plans that combine utilities and mortality. Results show that the models for ranking health outcomes of the plans are robust and credible. Future directions include the use of utilities for evaluating and ranking of MA plans.