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

Modeling containment of large wildfires using generalized linear mixed-model analysis

Treesearch

Mark Finney; Isaac C. Grenfell; Charles W. McHugh

2009-01-01

Billions of dollars are spent annually in the United States to contain large wildland fires, but the factors contributing to suppression success remain poorly understood. We used a regression model (generalized linear mixed-model) to model containment probability of individual fires, assuming that containment was a repeated-measures problem (fixed effect) and...

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

PubMed

Burkin, M M; Molchanova, E V

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

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Validation of test-day models for genetic evaluation of dairy goats in Norway.

PubMed

Andonov, S; Ødegård, J; Boman, I A; Svendsen, M; Holme, I J; Adnøy, T; Vukovic, V; Klemetsdal, G

2007-10-01

Test-day data for daily milk yield and fat, protein, and lactose content were sampled from the years 1988 to 2003 in 17 flocks belonging to 2 genetically well-tied buck circles. In total, records from 2,111 to 2,215 goats for content traits and 2,371 goats for daily milk yield were included in the analysis, averaging 2.6 and 4.8 observations per goat for the 2 groups of traits, respectively. The data were analyzed by using 4 test-day models with different modeling of fixed effects. Model [0] (the reference model) contained a fixed effect of year-season of kidding with regression on Ali-Schaeffer polynomials nested within the year-season classes, and a random effect of flock test-day. In model [1], the lactation curve effect from model [0] was replaced by a fixed effect of days in milk (in 3-d periods), the same for all year-seasons of kidding. Models [2] and [3] were obtained from model [1] by removing the fixed year-season of kidding effect and considering the flock test-day effect as either fixed or random, respectively. The models were compared by using 2 criteria: mean-squared error of prediction and a test of bias affecting the genetic trend. The first criterion indicated a preference for model [3], whereas the second criterion preferred model [1]. Mean-squared error of prediction is based on model fit, whereas the second criterion tests the ability of the model to produce unbiased genetic evaluation (i.e., its capability of separating environmental and genetic time trends). Thus, a fixed structure with year (year, year-season, or possibly flock-year) was indicated to appropriately separate time trends. Heritability estimates for daily milk yield and milk content were 0.26 and 0.24 to 0.27, respectively.

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

PubMed

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

2010-12-01

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

An Analysis of San Diego's Housing Market Using a Geographically Weighted Regression Approach

NASA Astrophysics Data System (ADS)

Grant, Christina P.

San Diego County real estate transaction data was evaluated with a set of linear models calibrated by ordinary least squares and geographically weighted regression (GWR). The goal of the analysis was to determine whether the spatial effects assumed to be in the data are best studied globally with no spatial terms, globally with a fixed effects submarket variable, or locally with GWR. 18,050 single-family residential sales which closed in the six months between April 2014 and September 2014 were used in the analysis. Diagnostic statistics including AICc, R2, Global Moran's I, and visual inspection of diagnostic plots and maps indicate superior model performance by GWR as compared to both global regressions.

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.

Comparison of statistical models to estimate parasite growth rate in the induced blood stage malaria model.

PubMed

Wockner, Leesa F; Hoffmann, Isabell; O'Rourke, Peter; McCarthy, James S; Marquart, Louise

2017-08-25

The efficacy of vaccines aimed at inhibiting the growth of malaria parasites in the blood can be assessed by comparing the growth rate of parasitaemia in the blood of subjects treated with a test vaccine compared to controls. In studies using induced blood stage malaria (IBSM), a type of controlled human malaria infection, parasite growth rate has been measured using models with the intercept on the y-axis fixed to the inoculum size. A set of statistical models was evaluated to determine an optimal methodology to estimate parasite growth rate in IBSM studies. Parasite growth rates were estimated using data from 40 subjects published in three IBSM studies. Data was fitted using 12 statistical models: log-linear, sine-wave with the period either fixed to 48 h or not fixed; these models were fitted with the intercept either fixed to the inoculum size or not fixed. All models were fitted by individual, and overall by study using a mixed effects model with a random effect for the individual. Log-linear models and sine-wave models, with the period fixed or not fixed, resulted in similar parasite growth rate estimates (within 0.05 log 10 parasites per mL/day). Average parasite growth rate estimates for models fitted by individual with the intercept fixed to the inoculum size were substantially lower by an average of 0.17 log 10 parasites per mL/day (range 0.06-0.24) compared with non-fixed intercept models. Variability of parasite growth rate estimates across the three studies analysed was substantially higher (3.5 times) for fixed-intercept models compared with non-fixed intercept models. The same tendency was observed in models fitted overall by study. Modelling data by individual or overall by study had minimal effect on parasite growth estimates. The analyses presented in this report confirm that fixing the intercept to the inoculum size influences parasite growth estimates. The most appropriate statistical model to estimate the growth rate of blood-stage parasites in IBSM studies appears to be a log-linear model fitted by individual and with the intercept estimated in the log-linear regression. Future studies should use this model to estimate parasite growth rates.

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

PubMed Central

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

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

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

PubMed

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

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

Does grandchild care influence grandparents' self-rated health? Evidence from a fixed effects approach.

PubMed

Ates, Merih

2017-10-01

The present study aims to identify, whether and how supplementary grandchild care is causally related to grandparents' self-rated health (SRH). Based on longitudinal data drawn from the German Aging Survey (DEAS; 2008-2014), I compare the results of pooled OLS, pooled OLS with lagged dependant variables (POLS-LD), random and fixed effects (RE, FE) panel regression. The results show that there is a positive but small association between supplementary grandchild care and SRH in POLS, POLS-LD, and RE models. However, the fixed effects model shows that the intrapersonal change in grandchild care does not cause a change in grandparents' SRH. The FE findings indicate that supplementary grandchild care in Germany does not have a causal impact on grandparents' SRH, suggesting that models with between-variation components overestimate the influence of grandchild care on grandparents' health because they do not control for unobserved (time-constant) heterogeneity. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Oklahoma's Promise Program: A National Model to Promote College Persistence

ERIC Educational Resources Information Center

Mendoza, Pilar; Mendez, Jesse P.

2013-01-01

Using a multi-method approach involving fixed effects and logistic regressions, this study examined the effect of the Oklahoma's Promise Program on student persistence in relation to the Pell and Stafford federal programs and according to socio-economic characteristics and class level. The Oklahoma's Promise is a hybrid state program that pays…

On The Impact of Climate Change to Agricultural Productivity in East Java

NASA Astrophysics Data System (ADS)

Kuswanto, Heri; Salamah, Mutiah; Mumpuni Retnaningsih, Sri; Dwi Prastyo, Dedy

2018-03-01

Many researches showed that climate change has significant impact on agricultural sector, which threats the food security especially in developing countries. It has been observed also that the climate change increases the intensity of extreme events. This research investigated the impact climate to the agricultural productivity in East Java, as one of the main rice producers in Indonesia. Standard regression as well as panel regression models have been performed in order to find the best model which is able to describe the climate change impact. The analysis found that the fixed effect model of panel regression outperforms the others showing that climate change had negatively impacted the rice productivity in East Java. The effect in Malang and Pasuruan were almost the same, while the impact in Sumenep was the least one compared to other districts.

Random regression analyses using B-splines functions to model growth from birth to adult age in Canchim cattle.

PubMed

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

2010-12-01

The objective of this work was to estimate covariance functions using random regression models on B-splines functions of animal age, for weights from birth to adult age in Canchim cattle. Data comprised 49,011 records on 2435 females. The model of analysis included fixed effects of contemporary groups, age of dam as quadratic covariable and the population mean trend taken into account by a cubic regression on orthogonal polynomials of animal age. Residual variances were modelled through a step function with four classes. The direct and maternal additive genetic effects, and animal and maternal permanent environmental effects were included as random effects in the model. A total of seventeen analyses, considering linear, quadratic and cubic B-splines functions and up to seven knots, were carried out. B-spline functions of the same order were considered for all random effects. Random regression models on B-splines functions were compared to a random regression model on Legendre polynomials and with a multitrait model. Results from different models of analyses were compared using the REML form of the Akaike Information criterion and Schwarz' Bayesian Information criterion. In addition, the variance components and genetic parameters estimated for each random regression model were also used as criteria to choose the most adequate model to describe the covariance structure of the data. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most adequate to describe the covariance structure of the data. Random regression models using B-spline functions as base functions fitted the data better than Legendre polynomials, especially at mature ages, but higher number of parameters need to be estimated with B-splines functions. © 2010 Blackwell Verlag GmbH.

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

USGS Publications Warehouse

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

2016-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

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.

Framing the Future: Revisiting the Place of Educational Expectations in Status Attainment

ERIC Educational Resources Information Center

Bozick, Robert; Alexander, Karl; Entwisle, Doris; Dauber, Susan; Kerr, Kerri

2010-01-01

This study revisits the Wisconsin model of status attainment from a life course developmental perspective. Fixed-effects regression analyses lend strong support to the Wisconsin framework's core proposition that academic performance and significant others' influence shape educational expectations. However, investigating the process of expectation…

Breeding value accuracy estimates for growth traits using random regression and multi-trait models in Nelore cattle.

PubMed

Boligon, A A; Baldi, F; Mercadante, M E Z; Lobo, R B; Pereira, R J; Albuquerque, L G

2011-06-28

We quantified the potential increase in accuracy of expected breeding value for weights of Nelore cattle, from birth to mature age, using multi-trait and random regression models on Legendre polynomials and B-spline functions. A total of 87,712 weight records from 8144 females were used, recorded every three months from birth to mature age from the Nelore Brazil Program. For random regression analyses, all female weight records from birth to eight years of age (data set I) were considered. From this general data set, a subset was created (data set II), which included only nine weight records: at birth, weaning, 365 and 550 days of age, and 2, 3, 4, 5, and 6 years of age. Data set II was analyzed using random regression and multi-trait models. The model of analysis included the contemporary group as fixed effects and age of dam as a linear and quadratic covariable. In the random regression analyses, average growth trends were modeled using a cubic regression on orthogonal polynomials of age. Residual variances were modeled by a step function with five classes. Legendre polynomials of fourth and sixth order were utilized to model the direct genetic and animal permanent environmental effects, respectively, while third-order Legendre polynomials were considered for maternal genetic and maternal permanent environmental effects. Quadratic polynomials were applied to model all random effects in random regression models on B-spline functions. Direct genetic and animal permanent environmental effects were modeled using three segments or five coefficients, and genetic maternal and maternal permanent environmental effects were modeled with one segment or three coefficients in the random regression models on B-spline functions. For both data sets (I and II), animals ranked differently according to expected breeding value obtained by random regression or multi-trait models. With random regression models, the highest gains in accuracy were obtained at ages with a low number of weight records. The results indicate that random regression models provide more accurate expected breeding values than the traditionally finite multi-trait models. Thus, higher genetic responses are expected for beef cattle growth traits by replacing a multi-trait model with random regression models for genetic evaluation. B-spline functions could be applied as an alternative to Legendre polynomials to model covariance functions for weights from birth to mature age.

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.

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

PubMed

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

2005-08-01

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

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

PubMed

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

2009-09-01

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

Does the association of prostate cancer with night-shift work differ according to rotating vs. fixed schedule? A systematic review and meta-analysis.

PubMed

Mancio, Jennifer; Leal, Cátia; Ferreira, Marta; Norton, Pedro; Lunet, Nuno

2018-04-27

Recent studies suggested that the relation between night-shift work and prostate cancer may differ between rotating and fixed schedules. We aimed to quantify the independent association between night-shift work and prostate cancer, for rotating and fixed schedules. We searched MEDLINE for studies assessing the association of night-shift work, by rotating or fixed schedules, with prostate cancer. We computed summary relative risk (RR) estimates with 95% confidence intervals (95% CI) using the inverse variance method and quantified heterogeneity using the I 2 statistic. Meta-regression analysis was used to compare the summary RR estimates for rotating and fixed schedules, while reducing heterogeneity. A total of nine studies assessed the effect of rotating and, in addition, four of them provided the effect of fixed night-shift work, in relation to daytime workers. Rotating night-shift work was associated with a significantly increased risk of prostate cancer (RR = 1.06, 95% CI of 1.01 to 1.12; I 2  = 50%), but not fixed night-shift work (RR of 1.01, 95% CI of 0.81 to 1.26; I 2  = 33%). In meta-regression model including study design, type of population, and control of confounding, the summary RR was 20% higher for rotating vs. fixed schedule, with heterogeneity fully explained by these variables. This is the first meta-analysis suggesting that an increased risk of prostate cancer may be restricted to workers with rotating night shifts. However, the association was weak and additional studies are needed to further clarify this relation before it can be translated into measures for risk reduction in occupational settings.

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

PubMed

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

2014-09-12

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

Accounting for individual differences and timing of events: estimating the effect of treatment on criminal convictions in heroin users.

PubMed

Røislien, Jo; Clausen, Thomas; Gran, Jon Michael; Bukten, Anne

2014-05-17

The reduction of crime is an important outcome of opioid maintenance treatment (OMT). Criminal intensity and treatment regimes vary among OMT patients, but this is rarely adjusted for in statistical analyses, which tend to focus on cohort incidence rates and rate ratios. The purpose of this work was to estimate the relationship between treatment and criminal convictions among OMT patients, adjusting for individual covariate information and timing of events, fitting time-to-event regression models of increasing complexity. National criminal records were cross linked with treatment data on 3221 patients starting OMT in Norway 1997-2003. In addition to calculating cohort incidence rates, criminal convictions was modelled as a recurrent event dependent variable, and treatment a time-dependent covariate, in Cox proportional hazards, Aalen's additive hazards, and semi-parametric additive hazards regression models. Both fixed and dynamic covariates were included. During OMT, the number of days with criminal convictions for the cohort as a whole was 61% lower than when not in treatment. OMT was associated with reduced number of days with criminal convictions in all time-to-event regression models, but the hazard ratio (95% CI) was strongly attenuated when adjusting for covariates; from 0.40 (0.35, 0.45) in a univariate model to 0.79 (0.72, 0.87) in a fully adjusted model. The hazard was lower for females and decreasing with older age, while increasing with high numbers of criminal convictions prior to application to OMT (all p < 0.001). The strongest predictors were level of criminal activity prior to entering into OMT, and having a recent criminal conviction (both p < 0.001). The effect of several predictors was significantly time-varying with their effects diminishing over time. Analyzing complex observational data regarding to fixed factors only overlooks important temporal information, and naïve cohort level incidence rates might result in biased estimates of the effect of interventions. Applying time-to-event regression models, properly adjusting for individual covariate information and timing of various events, allows for more precise and reliable effect estimates, as well as painting a more nuanced picture that can aid health care professionals and policy makers.

Covariance functions for body weight from birth to maturity in Nellore cows.

PubMed

Boligon, A A; Mercadante, M E Z; Forni, S; Lôbo, R B; Albuquerque, L G

2010-03-01

The objective of this study was to estimate (co)variance functions using random regression models on Legendre polynomials for the analysis of repeated measures of BW from birth to adult age. A total of 82,064 records from 8,145 females were analyzed. Different models were compared. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered as random covariables. Eight models with polynomials of third to sixth order were used to describe additive direct and maternal effects, and animal and maternal permanent environmental effects. Residual effects were modeled using 1 (i.e., assuming homogeneity of variances across all ages) or 5 age classes. The model with 5 classes was the best to describe the trajectory of residuals along the growth curve. The model including fourth- and sixth-order polynomials for additive direct and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects were the best. Estimates of (co)variance obtained with the multi-trait and random regression models were similar. Direct heritability estimates obtained with the random regression models followed a trend similar to that obtained with the multi-trait model. The largest estimates of maternal heritability were those of BW taken close to 240 d of age. In general, estimates of correlation between BW from birth to 8 yr of age decreased with increasing distance between ages.

E-cigarette price sensitivity among middle- and high-school students: evidence from monitoring the future.

PubMed

Pesko, Michael F; Huang, Jidong; Johnston, Lloyd D; Chaloupka, Frank J

2018-05-01

We estimated associations between e-cigarette prices (both disposable and refill) and e-cigarette use among middle and high-school students in the United States. We also estimated associations between cigarette prices and e-cigarette use. We used regression models to estimate the associations between e-cigarette and cigarette prices and e-cigarette use. In our regression models, we exploited changes in e-cigarette and cigarette prices across four periods of time and across 50 markets. We report the associations as price elasticities. In our primary model, we controlled for socio-demographic characteristics, cigarette prices, tobacco control policies, market fixed effects and year-quarter fixed effects. United States of America. A total of 24 370 middle- and high-school students participating in the Monitoring the Future Survey in years 2014 and 2015. Self-reported e-cigarette use over the last 30 days. Average quarterly cigarette prices, e-cigarette disposable prices and e-cigarette refill prices were constructed from Nielsen retail data (inclusive of excise taxes) for 50 US markets. In a model with market fixed effects, we estimated that a 10% increase in e-cigarette disposable prices is associated with a reduction in the number of days vaping among e-cigarette users by approximately 9.7% [95% confidence interval (CI) = -17.7 to 1.8%; P = 0.02] and is associated with a reduction in the number of days vaping by the full sample by approximately 17.9% (95% CI = -31.5 to -4.2%; P = 0.01). Refill e-cigarette prices were not statistically significant predictors of vaping. Cigarette prices were not associated significantly with e-cigarette use regardless of the e-cigarette price used. However, in a model without market fixed effects, cigarette prices were a statistically significant positive predictor of total e-cigarette use. Higher e-cigarette disposable prices appear to be associated with reduced e-cigarette use among adolescents in the US. © 2017 Society for the Study of Addiction.

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.

Does work-to-family conflict really matter for health? Cross-sectional, prospective cohort and fixed-effects analyses.

PubMed

Oshio, Takashi; Inoue, Akiomi; Tsutsumi, Akizumi

2017-02-01

It is well known that work-to-family conflict (WFC) is negatively associated with employees' health outcomes, including mental health and health behavior. However, the associations may be overstated because of insufficient control for unobserved individual attributes. To address this possibility, we compared the associations between WFC and health observed from a cross-sectional, prospective cohort and from fixed-effects regression models. We analyzed data from a Japanese occupational cohort survey of 15,102 observations from 7551 individuals (5947 men and 1604 women), which were collected in two waves with a one-year interval. We constructed a binary variable of high WFC and considered psychological distress measured using the Kessler 6 (K6) score, job and life dissatisfaction, and five types of health behavior (current smoking, problem drinking, leisure-time physical inactivity, sickness absence, and refraining from medical care). Results showed that for men, a high WFC increased the probability of reporting psychological distress (K6 score ≥ 5); this increased by 12.4% in a fixed-effects model. The association was substantially limited, as compared to the increase of 30.9% and 23.2% observed in cross-sectional and prospective cohort models, respectively; however, the association remained significant. Similar patterns were observed for job and life dissatisfaction. In contrast, the associations of WFC with all five types of health behavior were non-significant after controlling for fixed effects. We obtained generally similar results for women and found no substantial gender difference in the fixed-effects models. We concluded that the associations of WFC with employees' mental health and subjective well-being were robust, whereas the association between WFC and health behavior was generally limited. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Adolescent mental health and earnings inequalities in adulthood: evidence from the Young-HUNT Study.

PubMed

Evensen, Miriam; Lyngstad, Torkild Hovde; Melkevik, Ole; Reneflot, Anne; Mykletun, Arnstein

2017-02-01

Previous studies have shown that adolescent mental health problems are associated with lower employment probabilities and risk of unemployment. The evidence on how earnings are affected is much weaker, and few have addressed whether any association reflects unobserved characteristics and whether the consequences of mental health problems vary across the earnings distribution. A population-based Norwegian health survey linked to administrative registry data (N=7885) was used to estimate how adolescents' mental health problems (separate indicators of internalising, conduct, and attention problems and total sum scores) affect earnings (≥30 years) in young adulthood. We used linear regression with fixed-effects models comparing either students within schools or siblings within families. Unconditional quantile regressions were used to explore differentials across the earnings distribution. Mental health problems in adolescence reduce average earnings in adulthood, and associations are robust to control for observed family background and school fixed effects. For some, but not all mental health problems, associations are also robust in sibling fixed-effects models, where all stable family factors are controlled. Further, we found much larger earnings loss below the 25th centile. Adolescent mental health problems reduce adult earnings, especially among individuals in the lower tail of the earnings distribution. Preventing mental health problems in adolescence may increase future earnings. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

An application of the Health Action Process Approach model to oral hygiene behaviour and dental plaque in adolescents with fixed orthodontic appliances.

PubMed

Scheerman, Janneke F M; van Empelen, Pepijn; van Loveren, Cor; Pakpour, Amir H; van Meijel, Berno; Gholami, Maryam; Mierzaie, Zaher; van den Braak, Matheus C T; Verrips, Gijsbert H W

2017-11-01

The Health Action Process Approach (HAPA) model addresses health behaviours, but it has never been applied to model adolescents' oral hygiene behaviour during fixed orthodontic treatment. This study aimed to apply the HAPA model to explain adolescents' oral hygiene behaviour and dental plaque during orthodontic treatment with fixed appliances. In this cross-sectional study, 116 adolescents with fixed appliances from an orthodontic clinic situated in Almere (the Netherlands) completed a questionnaire assessing oral health behaviours and the psychosocial factors of the HAPA model. Linear regression analyses were performed to examine the factors associated with dental plaque, toothbrushing, and the use of a proxy brush. Stepwise regression analysis showed that lower amounts of plaque were significantly associated with higher frequency of the use of a proxy brush (R 2 = 45%), higher intention of the use of a proxy brush (R 2 = 5%), female gender (R 2 = 2%), and older age (R 2 = 2%). The multiple regression analyses revealed that higher action self-efficacy, intention, maintenance self-efficacy, and a higher education were significantly associated with the use of a proxy brush (R 2 = 45%). Decreased levels of dental plaque are mainly associated with increased use of a proxy brush that is subsequently associated with a higher intention and self-efficacy to use the proxy brush. © 2017 BSPD, IAPD and John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Simulation of parametric model towards the fixed covariate of right censored lung cancer data

NASA Astrophysics Data System (ADS)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

2017-09-01

In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

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

PubMed

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

2010-08-01

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

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

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.

Blending Multiple Nitrogen Dioxide Data Sources for Neighborhood Estimates of Long-Term Exposure for Health Research.

PubMed

Hanigan, Ivan C; Williamson, Grant J; Knibbs, Luke D; Horsley, Joshua; Rolfe, Margaret I; Cope, Martin; Barnett, Adrian G; Cowie, Christine T; Heyworth, Jane S; Serre, Marc L; Jalaludin, Bin; Morgan, Geoffrey G

2017-11-07

Exposure to traffic related nitrogen dioxide (NO 2 ) air pollution is associated with adverse health outcomes. Average pollutant concentrations for fixed monitoring sites are often used to estimate exposures for health studies, however these can be imprecise due to difficulty and cost of spatial modeling at the resolution of neighborhoods (e.g., a scale of tens of meters) rather than at a coarse scale (around several kilometers). The objective of this study was to derive improved estimates of neighborhood NO 2 concentrations by blending measurements with modeled predictions in Sydney, Australia (a low pollution environment). We implemented the Bayesian maximum entropy approach to blend data with uncertainty defined using informative priors. We compiled NO 2 data from fixed-site monitors, chemical transport models, and satellite-based land use regression models to estimate neighborhood annual average NO 2 . The spatial model produced a posterior probability density function of estimated annual average concentrations that spanned an order of magnitude from 3 to 35 ppb. Validation using independent data showed improvement, with root mean squared error improvement of 6% compared with the land use regression model and 16% over the chemical transport model. These estimates will be used in studies of health effects and should minimize misclassification bias.

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

PubMed

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

2015-01-01

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

Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models.

PubMed

Selapa, N W; Nephawe, K A; Maiwashe, A; Norris, D

2012-02-08

The aim of this study was to estimate genetic parameters for body weights of individually fed beef bulls measured at centralized testing stations in South Africa using random regression models. Weekly body weights of Bonsmara bulls (N = 2919) tested between 1999 and 2003 were available for the analyses. The model included a fixed regression of the body weights on fourth-order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84) for starting age and contemporary group effects. Random regressions on fourth-order orthogonal Legendre polynomials of the actual days on test were included for additive genetic effects and additional uncorrelated random effects of the weaning-herd-year and the permanent environment of the animal. Residual effects were assumed to be independently distributed with heterogeneous variance for each test day. Variance ratios for additive genetic, permanent environment and weaning-herd-year for weekly body weights at different test days ranged from 0.26 to 0.29, 0.37 to 0.44 and 0.26 to 0.34, respectively. The weaning-herd-year was found to have a significant effect on the variation of body weights of bulls despite a 28-day adjustment period. Genetic correlations amongst body weights at different test days were high, ranging from 0.89 to 1.00. Heritability estimates were comparable to literature using multivariate models. Therefore, random regression model could be applied in the genetic evaluation of body weight of individually fed beef bulls in South Africa.

Do Addtional Designations of Wilderness Result in Increases in Recreation Use?

Treesearch

John B. Loomis

1999-01-01

Designation of public lands as wilderness continues to be a contentious issue. With about 45 million acres designated as wilderness in the lower 48 states, the question of whether designation of additional wilderness would result in increased recreation use has been raised. We address this issue using a fixed-effects regression model for wilderness use at national...

Accounting for individual differences and timing of events: estimating the effect of treatment on criminal convictions in heroin users

PubMed Central

2014-01-01

Background The reduction of crime is an important outcome of opioid maintenance treatment (OMT). Criminal intensity and treatment regimes vary among OMT patients, but this is rarely adjusted for in statistical analyses, which tend to focus on cohort incidence rates and rate ratios. The purpose of this work was to estimate the relationship between treatment and criminal convictions among OMT patients, adjusting for individual covariate information and timing of events, fitting time-to-event regression models of increasing complexity. Methods National criminal records were cross linked with treatment data on 3221 patients starting OMT in Norway 1997–2003. In addition to calculating cohort incidence rates, criminal convictions was modelled as a recurrent event dependent variable, and treatment a time-dependent covariate, in Cox proportional hazards, Aalen’s additive hazards, and semi-parametric additive hazards regression models. Both fixed and dynamic covariates were included. Results During OMT, the number of days with criminal convictions for the cohort as a whole was 61% lower than when not in treatment. OMT was associated with reduced number of days with criminal convictions in all time-to-event regression models, but the hazard ratio (95% CI) was strongly attenuated when adjusting for covariates; from 0.40 (0.35, 0.45) in a univariate model to 0.79 (0.72, 0.87) in a fully adjusted model. The hazard was lower for females and decreasing with older age, while increasing with high numbers of criminal convictions prior to application to OMT (all p < 0.001). The strongest predictors were level of criminal activity prior to entering into OMT, and having a recent criminal conviction (both p < 0.001). The effect of several predictors was significantly time-varying with their effects diminishing over time. Conclusions Analyzing complex observational data regarding to fixed factors only overlooks important temporal information, and naïve cohort level incidence rates might result in biased estimates of the effect of interventions. Applying time-to-event regression models, properly adjusting for individual covariate information and timing of various events, allows for more precise and reliable effect estimates, as well as painting a more nuanced picture that can aid health care professionals and policy makers. PMID:24886472

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine.

PubMed

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

2018-01-01

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

Genetic Parameter Estimates for Metabolizing Two Common Pharmaceuticals in Swine

PubMed Central

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

2018-01-01

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

Learning by Doing, Scale Effects, or Neither? Cardiac Surgeons after Residency

PubMed Central

Huesch, Marco D

2009-01-01

Objective To examine impacts of operating surgeon scale and cumulative experience on postoperative outcomes for patients treated with coronary artery bypass grafts (CABG) by “new” surgeons. Pooled linear, fixed effects panel, and instrumented regressions were estimated. Data Sources The administrative data included comorbidities, procedures, and outcomes for 19,978 adult CABG patients in Florida in 1998–2006, and public data on 57 cardiac surgeons who completed residencies after 1997. Study Design Analysis was at the patient level. Controls for risk, hospital scale and scope, and operating surgeon characteristics were made. Patient choice model instruments were constructed. Experience was estimated allowing for “forgetting” effects. Principal Findings Panel regressions with surgeon fixed effects showed neither surgeon scale nor cumulative volumes significantly impacted mortality nor consistently impacted morbidity. Estimation of “forgetting” suggests that almost all prior experience is depreciated from one quarter to the next. Instruments were strong, but exogeneity of volume was not rejected. Conclusions In postresidency surgeons, no persuasive evidence is found for learning by doing, scale, or selection effects. More research is needed to support the cautious view that, for these “new” cardiac surgeons, patient volume could be redistributed based on realized outcomes without disruption. PMID:19732169

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.

Sick of our loans: Student borrowing and the mental health of young adults in the United States.

PubMed

Walsemann, Katrina M; Gee, Gilbert C; Gentile, Danielle

2015-01-01

Student loans are increasingly important and commonplace, especially among recent cohorts of young adults in the United States. These loans facilitate the acquisition of human capital in the form of education, but may also lead to stress and worries related to repayment. This study investigated two questions: 1) what is the association between the cumulative amount of student loans borrowed over the course of schooling and psychological functioning when individuals are 25-31 years old; and 2) what is the association between annual student loan borrowing and psychological functioning among currently enrolled college students? We also examined whether these relationships varied by parental wealth, college enrollment history (e.g. 2-year versus 4-year college), and educational attainment (for cumulative student loans only). We analyzed data from the National Longitudinal Survey of Youth 1997 (NLSY97), a nationally representative sample of young adults in the United States. Analyses employed multivariate linear regression and within-person fixed-effects models. Student loans were associated with poorer psychological functioning, adjusting for covariates, in both the multivariate linear regression and the within-person fixed effects models. This association varied by level of parental wealth in the multivariate linear regression models only, and did not vary by college enrollment history or educational attainment. The present findings raise novel questions for further research regarding student loan debt and the possible spillover effects on other life circumstances, such as occupational trajectories and health inequities. The study of student loans is even more timely and significant given the ongoing rise in the costs of higher education. Copyright © 2014 Elsevier Ltd. All rights reserved.

Parametric analysis for matched pair survival data.

PubMed

Manatunga, A K; Oakes, D

1999-12-01

Hougaard's (1986) bivariate Weibull distribution with positive stable frailties is applied to matched pairs survival data when either or both components of the pair may be censored and covariate vectors may be of arbitrary fixed length. When there is no censoring, we quantify the corresponding gain in Fisher information over a fixed-effects analysis. With the appropriate parameterization, the results take a simple algebraic form. An alternative marginal ("independence working model") approach to estimation is also considered. This method ignores the correlation between the two survival times in the derivation of the estimator, but provides a valid estimate of standard error. It is shown that when both the correlation between the two survival times is high, and the ratio of the within-pair variability to the between-pair variability of the covariates is high, the fixed-effects analysis captures most of the information about the regression coefficient but the independence working model does badly. When the correlation is low, and/or most of the variability of the covariates occurs between pairs, the reverse is true. The random effects model is applied to data on skin grafts, and on loss of visual acuity among diabetics. In conclusion some extensions of the methods are indicated and they are placed in a wider context of Generalized Estimation Equation methodology.

Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

NASA Astrophysics Data System (ADS)

Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi

2017-03-01

Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

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.

Modeling the safety impacts of driving hours and rest breaks on truck drivers considering time-dependent covariates.

PubMed

Chen, Chen; Xie, Yuanchang

2014-12-01

Driving hours and rest breaks are closely related to driver fatigue, which is a major contributor to truck crashes. This study investigates the effects of driving hours and rest breaks on commercial truck driver safety. A discrete-time logistic regression model is used to evaluate the crash odds ratios of driving hours and rest breaks. Driving time is divided into 11 one hour intervals. These intervals and rest breaks are modeled as dummy variables. In addition, a Cox proportional hazards regression model with time-dependent covariates is used to assess the transient effects of rest breaks, which consists of a fixed effect and a variable effect. Data collected from two national truckload carriers in 2009 and 2010 are used. The discrete-time logistic regression result indicates that only the crash odds ratio of the 11th driving hour is statistically significant. Taking one, two, and three rest breaks can reduce drivers' crash odds by 68%, 83%, and 85%, respectively, compared to drivers who did not take any rest breaks. The Cox regression result shows clear transient effects for rest breaks. It also suggests that drivers may need some time to adjust themselves to normal driving tasks after a rest break. Overall, the third rest break's safety benefit is very limited based on the results of both models. The findings of this research can help policy makers better understand the impact of driving time and rest breaks and develop more effective rules to improve commercial truck safety. Copyright © 2014 National Safety Council and Elsevier Ltd. All rights reserved.

Bayesian semi-parametric analysis of Poisson change-point regression models: application to policy making in Cali, Colombia.

PubMed

Park, Taeyoung; Krafty, Robert T; Sánchez, Alvaro I

2012-07-27

A Poisson regression model with an offset assumes a constant baseline rate after accounting for measured covariates, which may lead to biased estimates of coefficients in an inhomogeneous Poisson process. To correctly estimate the effect of time-dependent covariates, we propose a Poisson change-point regression model with an offset that allows a time-varying baseline rate. When the nonconstant pattern of a log baseline rate is modeled with a nonparametric step function, the resulting semi-parametric model involves a model component of varying dimension and thus requires a sophisticated varying-dimensional inference to obtain correct estimates of model parameters of fixed dimension. To fit the proposed varying-dimensional model, we devise a state-of-the-art MCMC-type algorithm based on partial collapse. The proposed model and methods are used to investigate an association between daily homicide rates in Cali, Colombia and policies that restrict the hours during which the legal sale of alcoholic beverages is permitted. While simultaneously identifying the latent changes in the baseline homicide rate which correspond to the incidence of sociopolitical events, we explore the effect of policies governing the sale of alcohol on homicide rates and seek a policy that balances the economic and cultural dependencies on alcohol sales to the health of the public.

Fixing Advising: A Model for Faculty Advising

ERIC Educational Resources Information Center

Crocker, Robert M.; Kahla, Marlene; Allen, Charlotte

2014-01-01

This paper addresses mandates to fix the advising process with a focus on faculty advising systems. Measures of student success and satisfaction, administrative issues, and faculty concerns are among the many factors discussed. Regression analysis is used to explore long-voiced faculty complaints that students do not follow advice. A case study is…

Application of response surface methodology and semi-mechanistic model to optimize fluoride removal using crushed concrete in a fixed-bed column.

PubMed

Gu, Bon-Wun; Lee, Chang-Gu; Park, Seong-Jik

2018-03-01

The aim of this study was to investigate the removal of fluoride from aqueous solutions by using crushed concrete fines as a filter medium under varying conditions of pH 3-7, flow rate of 0.3-0.7 mL/min, and filter depth of 10-20 cm. The performance of fixed-bed columns was evaluated on the basis of the removal ratio (Re), uptake capacity (qe), degree of sorbent used (DoSU), and sorbent usage rate (SUR) obtained from breakthrough curves (BTCs). Three widely used semi-mechanistic models, that is, Bohart-Adams, Thomas, and Yoon-Nelson models, were applied to simulate the BTCs and to derive the design parameters. The Box-Behnken design of response surface methodology (RSM) was used to elucidate the individual and interactive effects of the three operational parameters on the column performance and to optimize these parameters. The results demonstrated that pH is the most important factor in the performance of fluoride removal by a fixed-bed column. The flow rate had a significant negative influence on Re and DoSU, and the effect of filter depth was observed only in the regression model for DoSU. Statistical analysis indicated that the model attained from the RSM study is suitable for describing the semi-mechanistic model parameters.

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

High dimensional linear regression models under long memory dependence and measurement error

NASA Astrophysics Data System (ADS)

Kaul, Abhishek

This dissertation consists of three chapters. The first chapter introduces the models under consideration and motivates problems of interest. A brief literature review is also provided in this chapter. The second chapter investigates the properties of Lasso under long range dependent model errors. Lasso is a computationally efficient approach to model selection and estimation, and its properties are well studied when the regression errors are independent and identically distributed. We study the case, where the regression errors form a long memory moving average process. We establish a finite sample oracle inequality for the Lasso solution. We then show the asymptotic sign consistency in this setup. These results are established in the high dimensional setup (p> n) where p can be increasing exponentially with n. Finally, we show the consistency, n½ --d-consistency of Lasso, along with the oracle property of adaptive Lasso, in the case where p is fixed. Here d is the memory parameter of the stationary error sequence. The performance of Lasso is also analysed in the present setup with a simulation study. The third chapter proposes and investigates the properties of a penalized quantile based estimator for measurement error models. Standard formulations of prediction problems in high dimension regression models assume the availability of fully observed covariates and sub-Gaussian and homogeneous model errors. This makes these methods inapplicable to measurement errors models where covariates are unobservable and observations are possibly non sub-Gaussian and heterogeneous. We propose weighted penalized corrected quantile estimators for the regression parameter vector in linear regression models with additive measurement errors, where unobservable covariates are nonrandom. The proposed estimators forgo the need for the above mentioned model assumptions. We study these estimators in both the fixed dimension and high dimensional sparse setups, in the latter setup, the dimensionality can grow exponentially with the sample size. In the fixed dimensional setting we provide the oracle properties associated with the proposed estimators. In the high dimensional setting, we provide bounds for the statistical error associated with the estimation, that hold with asymptotic probability 1, thereby providing the ℓ1-consistency of the proposed estimator. We also establish the model selection consistency in terms of the correctly estimated zero components of the parameter vector. A simulation study that investigates the finite sample accuracy of the proposed estimator is also included in this chapter.

Age and gender differences in the influence of social support on mental health: a longitudinal fixed-effects analysis using 13 annual waves of the HILDA cohort.

PubMed

Milner, A; Krnjacki, L; LaMontagne, A D

2016-11-01

Perceived social support is associated with better mental health. There has been limited attention to how these relationships are modified by age and gender. We assessed this topic using 13 years of cohort data. Prospective cohort study. The outcome was the Mental Health Inventory-5 (MHI-5), a reliable and valid screening instrument for mood disorders. The main exposure was a social support scale composed of 10 items. We used longitudinal fixed-effects regression modelling to investigate within-person changes in mental health. Analytic models controlled for within-person sources of bias. We controlled for time-related factors by including them into regression modelling. The provision of higher levels of social support was associated with greater improvements in mental health for people aged under 30 years than for older age groups. The mental health of females appeared to benefit slightly more from higher levels of social support than males. Improvements in the MHI-5 were on a scale that could be considered clinically significant. The benefits of social support for young people may be connected to age-related transitions in self-identity and peer friendship networks. Results for females may reflect their tendency to place greater emphasis on social networks than males. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Genome-wide Association Study to Identify Quantitative Trait Loci for Meat and Carcass Quality Traits in Berkshire

PubMed Central

Iqbal, Asif; Kim, You-Sam; Kang, Jun-Mo; Lee, Yun-Mi; Rai, Rajani; Jung, Jong-Hyun; Oh, Dong-Yup; Nam, Ki-Chang; Lee, Hak-Kyo; Kim, Jong-Joo

2015-01-01

Meat and carcass quality attributes are of crucial importance influencing consumer preference and profitability in the pork industry. A set of 400 Berkshire pigs were collected from Dasan breeding farm, Namwon, Chonbuk province, Korea that were born between 2012 and 2013. To perform genome wide association studies (GWAS), eleven meat and carcass quality traits were considered, including carcass weight, backfat thickness, pH value after 24 hours (pH24), Commission Internationale de l’Eclairage lightness in meat color (CIE L), redness in meat color (CIE a), yellowness in meat color (CIE b), filtering, drip loss, heat loss, shear force and marbling score. All of the 400 animals were genotyped with the Porcine 62K SNP BeadChips (Illumina Inc., USA). A SAS general linear model procedure (SAS version 9.2) was used to pre-adjust the animal phenotypes before GWAS with sire and sex effects as fixed effects and slaughter age as a covariate. After fitting the fixed and covariate factors in the model, the residuals of the phenotype regressed on additive effects of each single nucleotide polymorphism (SNP) under a linear regression model (PLINK version 1.07). The significant SNPs after permutation testing at a chromosome-wise level were subjected to stepwise regression analysis to determine the best set of SNP markers. A total of 55 significant (p<0.05) SNPs or quantitative trait loci (QTL) were detected on various chromosomes. The QTLs explained from 5.06% to 8.28% of the total phenotypic variation of the traits. Some QTLs with pleiotropic effect were also identified. A pair of significant QTL for pH24 was also found to affect both CIE L and drip loss percentage. The significant QTL after characterization of the functional candidate genes on the QTL or around the QTL region may be effectively and efficiently used in marker assisted selection to achieve enhanced genetic improvement of the trait considered. PMID:26580276

Changes in waist circumference and body mass index in the US CARDIA cohort: fixed-effects associations with self-reported experiences of racial/ethnic discrimination.

PubMed

Cunningham, Timothy J; Berkman, Lisa F; Kawachi, Ichiro; Jacobs, David R; Seeman, Teresa E; Kiefe, Catarina I; Gortmaker, Steven L

2013-03-01

Prior studies examining the association between self-reported experiences of racial/ethnic discrimination and obesity have had mixed results and primarily been cross-sectional. This study tests the hypothesis that an increase in self-reported experiences of racial/ethnic discrimination predicts gains in waist circumference and body mass index in Black and White women and men over eight years. In race/ethnicity- and gender-stratified models, this study examined whether change in self-reported experiences of racial/ethnic discrimination predicts changes in waist circumference and body mass index over time using a fixed-effects regression approach in SAS statistical software, providing control for both measured and unmeasured time-invariant covariates. Between 1992-93 and 2000-01, self-reported experiences of racial/ethnic discrimination decreased among 843 Black women (75% to 73%), 601 Black men (80% to 77%), 893 White women (30% to 23%) and 856 White men (28% to 23%). In fixed-effects regression models, controlling for all time-invariant covariates, social desirability bias, and changes in education and parity (women only) over time, an increase in self-reported experiences of racial/ethnic discrimination over time was significantly associated with an increase in waist circumference (β=1.09, 95% CI: 0.00-2.19, p=0.05) and an increase in body mass index (β=0.67, 95% CI: 0.19-1.16, p=0.007) among Black women. No associations were observed among Black men and White women and men. These findings suggest that an increase in self-reported experiences of racial/ethnic discrimination may be associated with increases in waist circumference and body mass index among Black women over time.

The moderating role of overcommitment in the relationship between psychological contract breach and employee mental health.

PubMed

Reimann, Mareike

2016-09-30

This study investigated whether the association between perceived psychological contract breach (PCB) and employee mental health is moderated by the cognitive-motivational pattern of overcommitment (OC). Linking the psychological contract approach to the effort-reward imbalance model, this study examines PCB as an imbalance in employment relationships that acts as a psychosocial stressor in the work environment and is associated with stress reactions that in turn negatively affect mental health. The analyses were based on a sample of 3,667 employees who participated in a longitudinal linked employer-employee survey representative of large organizations (with at least 500 employees who are subject so social security contributions) in Germany. Fixed-effects regression models, including PCB and OC, were estimated for employee mental health, and interaction effects between PCB and OC were assessed. The multivariate fixed-effects regression analyses showed a significant negative association between PCB and employee mental health. The results also confirmed that OC does indeed significantly increase the negative effect of PCB on mental health and that OC itself has a significant and negative effect on mental health. The results suggest that employees characterized by the cognitive-motivational pattern of OC are at an increased risk of developing poor mental health if they experience PCB compared with employees who are not overly committed to their work. The results of this study support the assumption that psychosocial work stressors play an important role in employee mental health.

The moderating role of overcommitment in the relationship between psychological contract breach and employee mental health

PubMed Central

Reimann, Mareike

2016-01-01

Objectives: This study investigated whether the association between perceived psychological contract breach (PCB) and employee mental health is moderated by the cognitive-motivational pattern of overcommitment (OC). Linking the psychological contract approach to the effort-reward imbalance model, this study examines PCB as an imbalance in employment relationships that acts as a psychosocial stressor in the work environment and is associated with stress reactions that in turn negatively affect mental health. Methods: The analyses were based on a sample of 3,667 employees who participated in a longitudinal linked employer-employee survey representative of large organizations (with at least 500 employees who are subject so social security contributions) in Germany. Fixed-effects regression models, including PCB and OC, were estimated for employee mental health, and interaction effects between PCB and OC were assessed. Results: The multivariate fixed-effects regression analyses showed a significant negative association between PCB and employee mental health. The results also confirmed that OC does indeed significantly increase the negative effect of PCB on mental health and that OC itself has a significant and negative effect on mental health. Conclusions: The results suggest that employees characterized by the cognitive-motivational pattern of OC are at an increased risk of developing poor mental health if they experience PCB compared with employees who are not overly committed to their work. The results of this study support the assumption that psychosocial work stressors play an important role in employee mental health. PMID:27488041

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

PubMed

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

2005-10-01

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

Bayesian structured additive regression modeling of epidemic data: application to cholera

PubMed Central

2012-01-01

Background A significant interest in spatial epidemiology lies in identifying associated risk factors which enhances the risk of infection. Most studies, however, make no, or limited use of the spatial structure of the data, as well as possible nonlinear effects of the risk factors. Methods We develop a Bayesian Structured Additive Regression model for cholera epidemic data. Model estimation and inference is based on fully Bayesian approach via Markov Chain Monte Carlo (MCMC) simulations. The model is applied to cholera epidemic data in the Kumasi Metropolis, Ghana. Proximity to refuse dumps, density of refuse dumps, and proximity to potential cholera reservoirs were modeled as continuous functions; presence of slum settlers and population density were modeled as fixed effects, whereas spatial references to the communities were modeled as structured and unstructured spatial effects. Results We observe that the risk of cholera is associated with slum settlements and high population density. The risk of cholera is equal and lower for communities with fewer refuse dumps, but variable and higher for communities with more refuse dumps. The risk is also lower for communities distant from refuse dumps and potential cholera reservoirs. The results also indicate distinct spatial variation in the risk of cholera infection. Conclusion The study highlights the usefulness of Bayesian semi-parametric regression model analyzing public health data. These findings could serve as novel information to help health planners and policy makers in making effective decisions to control or prevent cholera epidemics. PMID:22866662

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

PubMed

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

2017-06-01

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

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

PubMed

Stanley, T D; Doucouliagos, Hristos

2015-06-15

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

Unemployment and psychosocial outcomes to age 30: A fixed-effects regression analysis.

PubMed

Fergusson, David M; McLeod, Geraldine F; Horwood, L John

2014-08-01

We aimed to examine the associations between exposure to unemployment and psychosocial outcomes over the period from 16 to 30 years, using data from a well-studied birth cohort. Data were collected over the course of the Christchurch Health and Development Study, a longitudinal study of a birth cohort of 1265 children, born in Christchurch in 1977, who have been studied to age 30. Assessments of unemployment and psychosocial outcomes (mental health, substance abuse/dependence, criminal offending, adverse life events and life satisfaction) were obtained at ages 18, 21, 25 and 30. Prior to adjustment, an increasing duration of unemployment was associated with significant increases in the risk of all psychosocial outcomes. These associations were adjusted for confounding using conditional, fixed-effects regression techniques. The analyses showed significant (p < 0.05) or marginally significant (p < 0.10) associations between the duration of unemployment and major depression (p = 0.05), alcohol abuse/dependence (p = 0.043), illicit substance abuse/dependence (p = 0.017), property/violent offending (p < 0.001), arrests/convictions (p = 0.052), serious financial problems (p = 0.007) and life satisfaction (p = 0.092). To test for reverse causality, the fixed-effects regression models were extended to include lagged, time-dynamic variables representing the respondent's psychosocial burden prior to the experience of unemployment. The findings suggested that the association between unemployment and psychosocial outcomes was likely to involve a causal process in which unemployment led to increased risks of adverse psychosocial outcomes. Effect sizes were estimated using attributable risk; exposure to unemployment accounted for between 4.2 and 14.0% (median 10.8%) of the risk of experiencing the significant psychosocial outcomes. The findings of this study suggest that exposure to unemployment had small but pervasive effects on psychosocial adjustment in adolescence and young adulthood. © The Royal Australian and New Zealand College of Psychiatrists 2014.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

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

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

NASA Technical Reports Server (NTRS)

Gorinevsky, Dimitry; Matthews, Bryan L.; Martin, Rodney

2012-01-01

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Detection probability in aerial surveys of feral horses

USGS Publications Warehouse

Ransom, Jason I.

2011-01-01

Observation bias pervades data collected during aerial surveys of large animals, and although some sources can be mitigated with informed planning, others must be addressed using valid sampling techniques that carefully model detection probability. Nonetheless, aerial surveys are frequently employed to count large mammals without applying such methods to account for heterogeneity in visibility of animal groups on the landscape. This often leaves managers and interest groups at odds over decisions that are not adequately informed. I analyzed detection of feral horse (Equus caballus) groups by dual independent observers from 24 fixed-wing and 16 helicopter flights using mixed-effect logistic regression models to investigate potential sources of observation bias. I accounted for observer skill, population location, and aircraft type in the model structure and analyzed the effects of group size, sun effect (position related to observer), vegetation type, topography, cloud cover, percent snow cover, and observer fatigue on detection of horse groups. The most important model-averaged effects for both fixed-wing and helicopter surveys included group size (fixed-wing: odds ratio = 0.891, 95% CI = 0.850–0.935; helicopter: odds ratio = 0.640, 95% CI = 0.587–0.698) and sun effect (fixed-wing: odds ratio = 0.632, 95% CI = 0.350–1.141; helicopter: odds ratio = 0.194, 95% CI = 0.080–0.470). Observer fatigue was also an important effect in the best model for helicopter surveys, with detection probability declining after 3 hr of survey time (odds ratio = 0.278, 95% CI = 0.144–0.537). Biases arising from sun effect and observer fatigue can be mitigated by pre-flight survey design. Other sources of bias, such as those arising from group size, topography, and vegetation can only be addressed by employing valid sampling techniques such as double sampling, mark–resight (batch-marked animals), mark–recapture (uniquely marked and identifiable animals), sightability bias correction models, and line transect distance sampling; however, some of these techniques may still only partially correct for negative observation biases.

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit

PubMed Central

Hoover, Stephen; Jackson, Eric V.; Paul, David; Locke, Robert

2016-01-01

Summary Background Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Objective Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. Methods We used five years of retrospective daily NICU census data for model development (January 2008 – December 2012, N=1827 observations) and one year of data for validation (January – December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. Results The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Conclusions Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning. PMID:27437040

Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit.

PubMed

Capan, Muge; Hoover, Stephen; Jackson, Eric V; Paul, David; Locke, Robert

2016-01-01

Accurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations. Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach. We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics. The census showed a slightly increasing linear trend. Best fitting models included a non-seasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)x(1,1,2)7 and ARIMA(2,1,4)x(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach. Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support short- and long-term census forecasting, and inform staff resource planning.

Effect of Workplace Weight Management on Health Care Expenditures and Quality of Life.

PubMed

Michaud, Tzeyu L; Nyman, John A; Jutkowitz, Eric; Su, Dejun; Dowd, Bryan; Abraham, Jean M

2016-11-01

We examined the effectiveness of the weight management program used by the University of Minnesota in reducing health care expenditures and improving quality of life of its employees, and also in reducing their absenteeism during a 3-year intervention. A differences-in-differences regression approach was used to estimate the effect of weight management participation. We further applied ordinary least squares regression models with fixed effects to estimate the effect in an alternative analysis. Participation in the weight management program significantly reduced health care expenditures by $69 per month for employees, spouses, and dependents, and by $73 for employees only. Quality-of-life weights were 0.0045 points higher for participating employees than for nonparticipating ones. No significant effect was found for absenteeism. The workplace weight management used by the University of Minnesota reduced health care expenditures and improved quality of life.

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.

Socio-Economic Factors Influencing on Total Fertility Rate in Iran: A Panel Data Analysis for the Period of 2002–2012

PubMed Central

Jafari, Hasan; Jaafaripooyan, Ebrahim; Vedadhir, Abou Ali; Foroushani, Abbas Rahimi; Ahadinejad, Bahman; Pourreza, Abolghasem

2016-01-01

Introduction Over the last few decades, total fertility rate (TFR) has followed a downward trend in Iran. The consequences of this trend from the perspectives of some are negative. Considering the macro-population policies in recent years, this study aimed to examine the effect of some macro socio-economic variables, including divorce, marriage, urbanization, and unemployment rate on TFR in Iran from 2002 to 2012. Methods This time series research was conducted in 2015 using the databases of the National Organization for Civil Registration (NOCR) and the Statistical Center of Iran. The study population was the related data of provinces in the selected variables. The main methods used in the research were the common unit root test, Pedroni Cointegration test, redundant fixed effects tests, correlated random effects-Hausman test, and panel least squares of fixed effects. In order to determine the suitable model for estimating panel data, likelihood ratio and Huasman tests were done using Eviews software, and the fixed effects regression model was chosen as the dominant model. Results The results indicated that the divorce rate had a negative and significant effect on TFR (p < 0.05). A positive and significant relationship between marriage rate and TFR variables also was observed (p < 0.05). Urbanization rate (p = 0.24) and unemployment rate (p = 0.36) had no significant relationship with TFR. According to F statistic, significance of the overall model also was confirmed (p < 0.001). Conclusion Due to the lower effect of the studied factors on the reduction of TFR, it seems that variables other than the ones studied, as well as cultural factors and values, might be fundamental factors for this change in the country. PMID:27504172

Fuzzy Regression Prediction and Application Based on Multi-Dimensional Factors of Freight Volume

NASA Astrophysics Data System (ADS)

Xiao, Mengting; Li, Cheng

2018-01-01

Based on the reality of the development of air cargo, the multi-dimensional fuzzy regression method is used to determine the influencing factors, and the three most important influencing factors of GDP, total fixed assets investment and regular flight route mileage are determined. The system’s viewpoints and analogy methods, the use of fuzzy numbers and multiple regression methods to predict the civil aviation cargo volume. In comparison with the 13th Five-Year Plan for China’s Civil Aviation Development (2016-2020), it is proved that this method can effectively improve the accuracy of forecasting and reduce the risk of forecasting. It is proved that this model predicts civil aviation freight volume of the feasibility, has a high practical significance and practical operation.

Impact of State Public Health Spending on Disease Incidence in the United States from 1980 to 2009.

PubMed

Verma, Reetu; Clark, Samantha; Leider, Jonathon; Bishai, David

2017-02-01

To understand the relationship between state-level spending by public health departments and the incidence of three vaccine preventable diseases (VPDs): mumps, pertussis, and rubella in the United States from 1980 to 2009. This study uses state-level public health spending data from The Census Bureau and annual mumps, pertussis, and rubella incidence counts from the University of Pittsburgh's project Tycho. Ordinary least squares (OLS), fixed effects, and random effects regression models were tested, with results indicating that a fixed effects model would be most appropriate model for this analysis. Model output suggests a statistically significant, negative relationship between public health spending and mumps and rubella incidence. Lagging outcome variables indicate that public health spending actually has the greatest impact on VPD incidence in subsequent years, rather than the year in which the spending occurred. Results were robust to models with lagged spending variables, national time trends, and state time trends, as well as models with and without Medicaid and hospital spending. Our analysis indicates that there is evidence of a significant, negative relationship between a state's public health spending and the incidence of two VPDs, mumps and rubella, in the United States. © Health Research and Educational Trust.

Semiparametric time varying coefficient model for matched case-crossover studies.

PubMed

Ortega-Villa, Ana Maria; Kim, Inyoung; Kim, H

2017-03-15

In matched case-crossover studies, it is generally accepted that the covariates on which a case and associated controls are matched cannot exert a confounding effect on independent predictors included in the conditional logistic regression model. This is because any stratum effect is removed by the conditioning on the fixed number of sets of the case and controls in the stratum. Hence, the conditional logistic regression model is not able to detect any effects associated with the matching covariates by stratum. However, some matching covariates such as time often play an important role as an effect modification leading to incorrect statistical estimation and prediction. Therefore, we propose three approaches to evaluate effect modification by time. The first is a parametric approach, the second is a semiparametric penalized approach, and the third is a semiparametric Bayesian approach. Our parametric approach is a two-stage method, which uses conditional logistic regression in the first stage and then estimates polynomial regression in the second stage. Our semiparametric penalized and Bayesian approaches are one-stage approaches developed by using regression splines. Our semiparametric one stage approach allows us to not only detect the parametric relationship between the predictor and binary outcomes, but also evaluate nonparametric relationships between the predictor and time. We demonstrate the advantage of our semiparametric one-stage approaches using both a simulation study and an epidemiological example of a 1-4 bi-directional case-crossover study of childhood aseptic meningitis with drinking water turbidity. We also provide statistical inference for the semiparametric Bayesian approach using Bayes Factors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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.

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

PubMed

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

2012-02-01

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

Using regression methods to estimate stream phosphorus loads at the Illinois River, Arkansas

USGS Publications Warehouse

Haggard, B.E.; Soerens, T.S.; Green, W.R.; Richards, R.P.

2003-01-01

The development of total maximum daily loads (TMDLs) requires evaluating existing constituent loads in streams. Accurate estimates of constituent loads are needed to calibrate watershed and reservoir models for TMDL development. The best approach to estimate constituent loads is high frequency sampling, particularly during storm events, and mass integration of constituents passing a point in a stream. Most often, resources are limited and discrete water quality samples are collected on fixed intervals and sometimes supplemented with directed sampling during storm events. When resources are limited, mass integration is not an accurate means to determine constituent loads and other load estimation techniques such as regression models are used. The objective of this work was to determine a minimum number of water-quality samples needed to provide constituent concentration data adequate to estimate constituent loads at a large stream. Twenty sets of water quality samples with and without supplemental storm samples were randomly selected at various fixed intervals from a database at the Illinois River, northwest Arkansas. The random sets were used to estimate total phosphorus (TP) loads using regression models. The regression-based annual TP loads were compared to the integrated annual TP load estimated using all the data. At a minimum, monthly sampling plus supplemental storm samples (six samples per year) was needed to produce a root mean square error of less than 15%. Water quality samples should be collected at least semi-monthly (every 15 days) in studies less than two years if seasonal time factors are to be used in the regression models. Annual TP loads estimated from independently collected discrete water quality samples further demonstrated the utility of using regression models to estimate annual TP loads in this stream system.

Demand for Health Insurance by Military Retirees

DTIC Science & Technology

2015-05-01

Plans,” The Journal of Health Economics 16, No. 2 (1997): 231–247 and Bruce A. Strombom, Thomas C. Buchmueller, and Paul J. Feldstein, “Switching Costs...Initiative: Volume 3. Health Care Utilization and Costs,” R -4244/3-HA (Santa Monica, CA: RAND Corporation, 1993). 10 probit regression model for TRICARE...Solomon (1998) Stanford University employees, panel data, 1994–95 HMO vs. PPO and FFS Logit -0.29 Fixed-Effects Logit -0.97 Barringer and Mitchell

Working hours and depressive symptoms over 7 years: evidence from a Korean panel study.

PubMed

Ahn, Seoyeon

2018-04-01

This study aims to examine how working hours influence depressive symptoms and the association between working hours and depressive symptoms differently across genders. The sample consists of salaried workers aged 25-64 years who participated in two consecutive waves of the seven-wave Korean Welfare Panel Study (2007-2013) (n = 6813 individuals, 27,986 observations) which is a survey of a nationally representative sample of the South Korean population. I apply logit regression and fixed-effects logit regression to examine the causal relation between (intra-)individual changes of working hours and depressive symptoms over a 7-year period. Results from logit model and fixed-effects logit model show that less than 30 h of work per week and more than 60 h of work per week are associated with significantly higher levels of depressive symptoms. Sex-stratified analyses reveal that women who worked over 60 h per week were at increased risk of showing depressive symptoms compared with women who worked 30-40 h per week. No significant increase in depressive symptoms was seen in men who worked more than 60 h per week. However, men working less than 30 h per week are more likely to report higher levels of depressive symptoms. These results suggest that work arrangement affects the mental health of men and women differently.

Automated time series forecasting for biosurveillance.

PubMed

Burkom, Howard S; Murphy, Sean Patrick; Shmueli, Galit

2007-09-30

For robust detection performance, traditional control chart monitoring for biosurveillance is based on input data free of trends, day-of-week effects, and other systematic behaviour. Time series forecasting methods may be used to remove this behaviour by subtracting forecasts from observations to form residuals for algorithmic input. We describe three forecast methods and compare their predictive accuracy on each of 16 authentic syndromic data streams. The methods are (1) a non-adaptive regression model using a long historical baseline, (2) an adaptive regression model with a shorter, sliding baseline, and (3) the Holt-Winters method for generalized exponential smoothing. Criteria for comparing the forecasts were the root-mean-square error, the median absolute per cent error (MedAPE), and the median absolute deviation. The median-based criteria showed best overall performance for the Holt-Winters method. The MedAPE measures over the 16 test series averaged 16.5, 11.6, and 9.7 for the non-adaptive regression, adaptive regression, and Holt-Winters methods, respectively. The non-adaptive regression forecasts were degraded by changes in the data behaviour in the fixed baseline period used to compute model coefficients. The mean-based criterion was less conclusive because of the effects of poor forecasts on a small number of calendar holidays. The Holt-Winters method was also most effective at removing serial autocorrelation, with most 1-day-lag autocorrelation coefficients below 0.15. The forecast methods were compared without tuning them to the behaviour of individual series. We achieved improved predictions with such tuning of the Holt-Winters method, but practical use of such improvements for routine surveillance will require reliable data classification methods.

Particulate air pollution and panel studies in children: a systematic review

PubMed Central

Ward, D; Ayres, J

2004-01-01

Aims: To systematically review the results of such studies in children, estimate summary measures of effect, and investigate potential sources of heterogeneity. Methods: Studies were identified by searching electronic databases to June 2002, including those where outcomes and particulate level measurements were made at least daily for ⩾8 weeks, and analysed using an appropriate regression model. Study results were compared using forest plots, and fixed and random effects summary effect estimates obtained. Publication bias was considered using a funnel plot. Results: Twenty two studies were identified, all except two reporting PM10 (24 hour mean) >50 µg.m-3. Reported effects of PM10 on PEF were widely spread and smaller than those for PM2.5 (fixed effects summary: -0.012 v -0.063 l.min-1 per µg.m-3 rise). A similar pattern was evident for symptoms. Random effects models produced larger estimates. Overall, in between-study comparisons, panels of children with diagnosed asthma or pre-existing respiratory symptoms appeared less affected by PM10 levels than those without, and effect estimates were larger where studies were conducted in higher ozone conditions. Larger PM10 effect estimates were obtained from studies using generalised estimating equations to model autocorrelation and where results were derived by pooling subject specific regression coefficients. A funnel plot of PM10 results for PEF was markedly asymmetrical. Conclusions: The majority of identified studies indicate an adverse effect of particulate air pollution that is greater for PM2.5 than PM10. However, results show considerable heterogeneity and there is evidence consistent with publication bias, so limited confidence may be placed on summary estimates of effect. The possibility of interaction between particle and ozone effects merits further investigation, as does variability due to analytical differences that alter the interpretation of final estimates. PMID:15031404

Incorporation of prior information on parameters into nonlinear regression groundwater flow models: 1. Theory

USGS Publications Warehouse

Cooley, Richard L.

1982-01-01

Prior information on the parameters of a groundwater flow model can be used to improve parameter estimates obtained from nonlinear regression solution of a modeling problem. Two scales of prior information can be available: (1) prior information having known reliability (that is, bias and random error structure) and (2) prior information consisting of best available estimates of unknown reliability. A regression method that incorporates the second scale of prior information assumes the prior information to be fixed for any particular analysis to produce improved, although biased, parameter estimates. Approximate optimization of two auxiliary parameters of the formulation is used to help minimize the bias, which is almost always much smaller than that resulting from standard ridge regression. It is shown that if both scales of prior information are available, then a combined regression analysis may be made.

The association of parental education with childhood undernutrition in low- and middle-income countries: comparing the role of paternal and maternal education.

PubMed

Vollmer, Sebastian; Bommer, Christian; Krishna, Aditi; Harttgen, Kenneth; Subramanian, S V

2017-02-01

Most existing research on the association of parental education with childhood undernutrition focuses on maternal education and often ignores paternal education. We systematically investigate differences in maternal and paternal education and their association with childhood undernutrition. One hundred and eighty Demographic and Health Surveys from 62 countries performed between 1990 and 2014 were analysed. We used linear-probability models to predict childhood undernutrition prevalences, measured as stunting, underweight and wasting, for all combinations of maternal and paternal attainment in school. Models were adjusted for demographic and socio-economic covariates for the child, mother and household, country-level fixed effects and clustering. Additional specifications adjust for local area characteristics instead of country fixed effects. Both higher maternal and paternal education levels are associated with lower childhood undernutrition. In regressions adjusted for child age and sex as well as country-level fixed effects, the association is stronger for maternal education than for paternal education when their combined level of education is held constant. In the fully adjusted models, the observed differences in predicted undernutrition prevalences are strongly attenuated, suggesting a similar importance of maternal and paternal education. These findings are confirmed by the analysis of composite schooling indicators. We find that paternal education is similarly important for reducing childhood undernutrition as maternal education and should therefore receive increased attention in the literature. © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association

The association of parental education with childhood undernutrition in low- and middle-income countries: comparing the role of paternal and maternal education

PubMed Central

Vollmer, Sebastian; Bommer, Christian; Krishna, Aditi; Harttgen, Kenneth; Subramanian, SV

2017-01-01

Abstract Background: Most existing research on the association of parental education with childhood undernutrition focuses on maternal education and often ignores paternal education. We systematically investigate differences in maternal and paternal education and their association with childhood undernutrition. Methods: One hundred and eighty Demographic and Health Surveys from 62 countries performed between 1990 and 2014 were analysed. We used linear-probability models to predict childhood undernutrition prevalences, measured as stunting, underweight and wasting, for all combinations of maternal and paternal attainment in school. Models were adjusted for demographic and socio-economic covariates for the child, mother and household, country-level fixed effects and clustering. Additional specifications adjust for local area characteristics instead of country fixed effects. Results: Both higher maternal and paternal education levels are associated with lower childhood undernutrition. In regressions adjusted for child age and sex as well as country-level fixed effects, the association is stronger for maternal education than for paternal education when their combined level of education is held constant. In the fully adjusted models, the observed differences in predicted undernutrition prevalences are strongly attenuated, suggesting a similar importance of maternal and paternal education. These findings are confirmed by the analysis of composite schooling indicators. Conclusions: We find that paternal education is similarly important for reducing childhood undernutrition as maternal education and should therefore receive increased attention in the literature. PMID:27501820

Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression.

PubMed

Panayi, Efstathios; Peters, Gareth W; Kyriakides, George

2017-01-01

Quantifying the effects of environmental factors over the duration of the growing process on Agaricus Bisporus (button mushroom) yields has been difficult, as common functional data analysis approaches require fixed length functional data. The data available from commercial growers, however, is of variable duration, due to commercial considerations. We employ a recently proposed regression technique termed Variable-Domain Functional Regression in order to be able to accommodate these irregular-length datasets. In this way, we are able to quantify the contribution of covariates such as temperature, humidity and water spraying volumes across the growing process, and for different lengths of growing processes. Our results indicate that optimal oxygen and temperature levels vary across the growing cycle and we propose environmental schedules for these covariates to optimise overall yields.

Statistical modelling for precision agriculture: A case study in optimal environmental schedules for Agaricus Bisporus production via variable domain functional regression

PubMed Central

Panayi, Efstathios; Kyriakides, George

2017-01-01

Quantifying the effects of environmental factors over the duration of the growing process on Agaricus Bisporus (button mushroom) yields has been difficult, as common functional data analysis approaches require fixed length functional data. The data available from commercial growers, however, is of variable duration, due to commercial considerations. We employ a recently proposed regression technique termed Variable-Domain Functional Regression in order to be able to accommodate these irregular-length datasets. In this way, we are able to quantify the contribution of covariates such as temperature, humidity and water spraying volumes across the growing process, and for different lengths of growing processes. Our results indicate that optimal oxygen and temperature levels vary across the growing cycle and we propose environmental schedules for these covariates to optimise overall yields. PMID:28961254

Combining fixed effects and instrumental variable approaches for estimating the effect of psychosocial job quality on mental health: evidence from 13 waves of a nationally representative cohort study.

PubMed

Milner, Allison; Aitken, Zoe; Kavanagh, Anne; LaMontagne, Anthony D; Pega, Frank; Petrie, Dennis

2017-06-23

Previous studies suggest that poor psychosocial job quality is a risk factor for mental health problems, but they use conventional regression analytic methods that cannot rule out reverse causation, unmeasured time-invariant confounding and reporting bias. This study combines two quasi-experimental approaches to improve causal inference by better accounting for these biases: (i) linear fixed effects regression analysis and (ii) linear instrumental variable analysis. We extract 13 annual waves of national cohort data including 13 260 working-age (18-64 years) employees. The exposure variable is self-reported level of psychosocial job quality. The instruments used are two common workplace entitlements. The outcome variable is the Mental Health Inventory (MHI-5). We adjust for measured time-varying confounders. In the fixed effects regression analysis adjusted for time-varying confounders, a 1-point increase in psychosocial job quality is associated with a 1.28-point improvement in mental health on the MHI-5 scale (95% CI: 1.17, 1.40; P < 0.001). When the fixed effects was combined with the instrumental variable analysis, a 1-point increase psychosocial job quality is related to 1.62-point improvement on the MHI-5 scale (95% CI: -0.24, 3.48; P = 0.088). Our quasi-experimental results provide evidence to confirm job stressors as risk factors for mental ill health using methods that improve causal inference. © The Author 2017. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Genome-Wide Association Studies with a Genomic Relationship Matrix: A Case Study with Wheat and Arabidopsis

PubMed Central

Gianola, Daniel; Fariello, Maria I.; Naya, Hugo; Schön, Chris-Carolin

2016-01-01

Standard genome-wide association studies (GWAS) scan for relationships between each of p molecular markers and a continuously distributed target trait. Typically, a marker-based matrix of genomic similarities among individuals (G) is constructed, to account more properly for the covariance structure in the linear regression model used. We show that the generalized least-squares estimator of the regression of phenotype on one or on m markers is invariant with respect to whether or not the marker(s) tested is(are) used for building G, provided variance components are unaffected by exclusion of such marker(s) from G. The result is arrived at by using a matrix expression such that one can find many inverses of genomic relationship, or of phenotypic covariance matrices, stemming from removing markers tested as fixed, but carrying out a single inversion. When eigenvectors of the genomic relationship matrix are used as regressors with fixed regression coefficients, e.g., to account for population stratification, their removal from G does matter. Removal of eigenvectors from G can have a noticeable effect on estimates of genomic and residual variances, so caution is needed. Concepts were illustrated using genomic data on 599 wheat inbred lines, with grain yield as target trait, and on close to 200 Arabidopsis thaliana accessions. PMID:27520956

The relationship between cigarette taxes and child maltreatment.

PubMed

McLaughlin, Michael

2018-05-01

Prior research suggests that income and child maltreatment are related, but questions remain about the specific types of economic factors that affect the risk of maltreatment. The need to understand the role of economics in child welfare is critical, given the significant public health costs of child maltreatment. One factor that has been overlooked is regressive taxation. This study addresses this need by examining whether state-level changes in cigarette tax rates predict changes in state-level child maltreatment rates. The results of both a fixed effects (FE) and a fixed effects instrumental variables (FE-IV) estimator show that increases in state cigarette tax rates are followed by increases in child abuse and neglect. An additional test finds that increases in the sales tax (another tax deemed to be regressive) also predict increases in child maltreatment rates. Taken as a whole, the findings suggest that regressive taxes have a significant effect on the risk of child maltreatment. Copyright © 2018 Elsevier Ltd. All rights reserved.

The study of correlation among different scattering parameters in an aggregate dust model

NASA Astrophysics Data System (ADS)

Mazarbhuiya, A. M.; Das, H. S.

2017-09-01

We study the light scattering properties of aggregate particles in a wide range of complex refractive indices (m = n + i k, where 1.4 ≤ n ≤ 2.0, 0.001 ≤ k ≤1.0) and wavelengths (0.45 ≤ λ≤1.25 μ m) to investigate the correlation among different parameters e.g., the positive polarization maximum (P_{max}), the amplitude of the negative polarization (P_{min}), geometric albedo (A), (n,k) and λ. Numerical computations are performed by the Superposition T-matrix code with Ballistic Cluster-Cluster Aggregate (BCCA) particles of 128 monomers and Ballistic Aggregates (BA) particles of 512 monomers, where monomer's radius of aggregates is considered to be 0.1 μm. At a fixed value of k, P_{max} and n are correlated via a quadratic regression equation and this nature is observed at all wavelengths. Further, P_{max} and k are found to be related via a polynomial regression equation when n is taken to be fixed. The degree of the equation depends on the wavelength, higher the wavelength lower is the degree. We find that A and P_{max} are correlated via a cubic regression at λ= 0.45 μ m whereas this correlation is quadratic at higher wavelengths. We notice that |P_{min}| increases with the decrease of P_{max} and a strong linear correlation between them is observed when n is fixed at some value and k is changed from higher to lower value. Further, at a fix value of k, P_{min} and P_{max} can be fitted well via a quartic regression equation when n is changed from higher to lower value. We also find that P_{max} increases with λ and they are correlated via a quartic regression.

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

PubMed

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

2014-01-01

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

Controlling for selection effects in the relationship between child behavior problems and exposure to intimate partner violence.

PubMed

Emery, Clifton R

2011-05-01

This article used the Project on Human Development in Chicago Neighborhoods (PHDCN) data to examine the relationship between exposure to intimate partner violence (IPV) and child behavior problems (externalizing and internalizing), truancy, grade repetition, smoking, drinking, and use of marijuana. Longitudinal data analysis was conducted on 1,816 primary caregivers and their children. Fixed-effects regression models were employed to address concerns with selection bias. IPV was associated with significantly greater internalizing behavior, externalizing behavior, and truancy. Findings from age interaction models suggested that the relationship between IPV and child behavior problems may attenuate as the age of the child at time of exposure increases.

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.

Evaluation of Cox's model and logistic regression for matched case-control data with time-dependent covariates: a simulation study.

PubMed

Leffondré, Karen; Abrahamowicz, Michal; Siemiatycki, Jack

2003-12-30

Case-control studies are typically analysed using the conventional logistic model, which does not directly account for changes in the covariate values over time. Yet, many exposures may vary over time. The most natural alternative to handle such exposures would be to use the Cox model with time-dependent covariates. However, its application to case-control data opens the question of how to manipulate the risk sets. Through a simulation study, we investigate how the accuracy of the estimates of Cox's model depends on the operational definition of risk sets and/or on some aspects of the time-varying exposure. We also assess the estimates obtained from conventional logistic regression. The lifetime experience of a hypothetical population is first generated, and a matched case-control study is then simulated from this population. We control the frequency, the age at initiation, and the total duration of exposure, as well as the strengths of their effects. All models considered include a fixed-in-time covariate and one or two time-dependent covariate(s): the indicator of current exposure and/or the exposure duration. Simulation results show that none of the models always performs well. The discrepancies between the odds ratios yielded by logistic regression and the 'true' hazard ratio depend on both the type of the covariate and the strength of its effect. In addition, it seems that logistic regression has difficulty separating the effects of inter-correlated time-dependent covariates. By contrast, each of the two versions of Cox's model systematically induces either a serious under-estimation or a moderate over-estimation bias. The magnitude of the latter bias is proportional to the true effect, suggesting that an improved manipulation of the risk sets may eliminate, or at least reduce, the bias. Copyright 2003 JohnWiley & Sons, Ltd.

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

PubMed

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

2017-12-01

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

Impact of wearing fixed orthodontic appliances on quality of life among adolescents: Case-control study.

PubMed

Costa, Andréa A; Serra-Negra, Júnia M; Bendo, Cristiane B; Pordeus, Isabela A; Paiva, Saul M

2016-01-01

To investigate the impact of wearing a fixed orthodontic appliance on oral health-related quality of life (OHRQoL) among adolescents. A case-control study (1 ∶ 2) was carried out with a population-based randomized sample of 327 adolescents aged 11 to 14 years enrolled at public and private schools in the City of Brumadinho, southeast of Brazil. The case group (n  =  109) was made up of adolescents with a high negative impact on OHRQoL, and the control group (n  =  218) was made up of adolescents with a low negative impact. The outcome variable was the impact on OHRQoL measured by the Brazilian version of the Child Perceptions Questionnaire (CPQ 11-14) - Impact Short Form (ISF:16). The main independent variable was wearing fixed orthodontic appliances. Malocclusion and the type of school were identified as possible confounding variables. Bivariate and multiple conditional logistic regressions were employed in the statistical analysis. A multiple conditional logistic regression model demonstrated that adolescents wearing fixed orthodontic appliances had a 4.88-fold greater chance of presenting high negative impact on OHRQoL (95% CI: 2.93-8.13; P < .001) than those who did not wear fixed orthodontic appliances. A bivariate conditional logistic regression demonstrated that malocclusion was significantly associated with OHRQoL (P  =  .017), whereas no statistically significant association was found between the type of school and OHRQoL (P  =  .108). Adolescents who wore fixed orthodontic appliances had a greater chance of reporting a negative impact on OHRQoL than those who did not wear such appliances.

Continuity of cannabis use and violent offending over the life course.

PubMed

Schoeler, T; Theobald, D; Pingault, J-B; Farrington, D P; Jennings, W G; Piquero, A R; Coid, J W; Bhattacharyya, S

2016-06-01

Although the association between cannabis use and violence has been reported in the literature, the precise nature of this relationship, especially the directionality of the association, is unclear. Young males from the Cambridge Study of Delinquent Development (n = 411) were followed up between the ages of 8 and 56 years to prospectively investigate the association between cannabis use and violence. A multi-wave (eight assessments, T1-T8) follow-up design was employed that allowed temporal sequencing of the variables of interest and the analysis of violent outcome measures obtained from two sources: (i) criminal records (violent conviction); and (ii) self-reports. A combination of analytic approaches allowing inferences as to the directionality of associations was employed, including multivariate logistic regression analysis, fixed-effects analysis and cross-lagged modelling. Multivariable logistic regression revealed that compared with never-users, continued exposure to cannabis (use at age 18, 32 and 48 years) was associated with a higher risk of subsequent violent behaviour, as indexed by convictions [odds ratio (OR) 7.1, 95% confidence interval (CI) 2.19-23.59] or self-reports (OR 8.9, 95% CI 2.37-46.21). This effect persisted after controlling for other putative risk factors for violence. In predicting violence, fixed-effects analysis and cross-lagged modelling further indicated that this effect could not be explained by other unobserved time-invariant factors. Furthermore, these analyses uncovered a bi-directional relationship between cannabis use and violence. Together, these results provide strong indication that cannabis use predicts subsequent violent offending, suggesting a possible causal effect, and provide empirical evidence that may have implications for public policy.

Educational Attainment of 25 Year Old Norwegians According to Birth Order and Gender

ERIC Educational Resources Information Center

Kristensen, Petter; Bjerkedal, Tor

2010-01-01

This register-based longitudinal study of 392 969 Norwegians examined associations between birth order, gender and educational attainment at age 25 years within families (fixed effects regression) and between families (ordinary OLS regression). Data were retrieved from national registers for births of mothers with single births only and a first…

Applicability of Monte Carlo cross validation technique for model development and validation using generalised least squares regression

NASA Astrophysics Data System (ADS)

Haddad, Khaled; Rahman, Ataur; A Zaman, Mohammad; Shrestha, Surendra

2013-03-01

SummaryIn regional hydrologic regression analysis, model selection and validation are regarded as important steps. Here, the model selection is usually based on some measurements of goodness-of-fit between the model prediction and observed data. In Regional Flood Frequency Analysis (RFFA), leave-one-out (LOO) validation or a fixed percentage leave out validation (e.g., 10%) is commonly adopted to assess the predictive ability of regression-based prediction equations. This paper develops a Monte Carlo Cross Validation (MCCV) technique (which has widely been adopted in Chemometrics and Econometrics) in RFFA using Generalised Least Squares Regression (GLSR) and compares it with the most commonly adopted LOO validation approach. The study uses simulated and regional flood data from the state of New South Wales in Australia. It is found that when developing hydrologic regression models, application of the MCCV is likely to result in a more parsimonious model than the LOO. It has also been found that the MCCV can provide a more realistic estimate of a model's predictive ability when compared with the LOO.

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

PubMed

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

2011-05-23

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

The association between height and birth order: evidence from 652,518 Swedish men.

PubMed

Myrskylä, Mikko; Silventoinen, Karri; Jelenkovic, Aline; Tynelius, Per; Rasmussen, Finn

2013-07-01

Birth order is associated with outcomes such as birth weight and adult socioeconomic position (SEP), but little is known about the association with adult height. This potential birth order-height association is important because height predicts health, and because the association may help explain population-level height trends. We studied the birth order-height association and whether it varies by family characteristics or birth cohort. We used the Swedish Military Conscription Register to analyse adult height among 652,518 men born in 1951-1983 using fixed effects regression models that compare brothers and account for genetic and social factors shared by brothers. We stratified the analysis by family size, parental SEP and birth cohort. We compared models with and without birth weight and birth length controls. Unadjusted analyses showed no differences between the first two birth orders but in the fixed effects regression, birth orders 2, 3 and 4 were associated with 0.4, 0.7 and 0.8 cm (p<0.001 for each) shorter height than birth order 1, respectively. The associations were similar in large and small and high-SEP and low-SEP families, but were attenuated in recent cohorts. Birth characteristics did not explain these associations. Birth order is an important determinant of height. The height difference between birth orders 3 and 1 is larger than the population-level height increase achieved over 10 years. The attenuation of the effect over cohorts may reflect improvements in living standards. Decreases in family size may explain some of the secular-height increases in countries with decreasing fertility.

Long-term skeletal effects of high-pull headgear followed by fixed appliances for the treatment of Class II malocclusions.

PubMed

Bilbo, E Erin; Marshall, Steven D; Southard, Karin A; Allareddy, Verrasathpurush; Holton, Nathan; Thames, Allyn M; Otsby, Marlene S; Southard, Thomas E

2018-04-18

The long-term skeletal effects of Class II treatment in growing individuals using high-pull facebow headgear and fixed edgewise appliances have not been reported. The purpose of this study was to evaluate the long-term skeletal effects of treatment using high-pull headgear followed by fixed orthodontic appliances compared to an untreated control group. Changes in anteroposterior and vertical cephalometric measurements of 42 Class II subjects (n = 21, mean age = 10.7 years) before treatment, after headgear correction to Class I molar relationship, after treatment with fixed appliances, and after long-term retention (mean 4.1 years), were compared to similar changes in a matched control group (n = 21, mean age = 10.9 years) by multivariable linear regression models. Compared to control, the study group displayed significant long-term horizontal restriction of A-point (SNA = -1.925°, P < .0001; FH-NA = -3.042°, P < .0001; linear measurement A-point to Vertical Reference = -3.859 mm, P < .0001) and reduction of the ANB angle (-1.767°, P < .0001), with no effect on mandibular horizontal growth or maxillary and mandibular vertical skeletal changes. A-point horizontal restriction and forward mandibular horizontal growth accompanied the study group correction to Class I molar, and these changes were stable long term. One phase treatment for Class II malocclusion with high-pull headgear followed by fixed orthodontic appliances resulted in correction to Class I molar through restriction of horizontal maxillary growth with continued horizontal mandibular growth and vertical skeletal changes unaffected. The anteroposterior molar correction and skeletal effects of this treatment were stable long term.

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

PubMed

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

2016-12-01

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

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

PubMed Central

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

2016-01-01

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

CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T

2016-02-01

The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.

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

PubMed

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

2013-09-01

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

Targeting trachoma control through risk mapping: the example of Southern Sudan.

PubMed

Clements, Archie C A; Kur, Lucia W; Gatpan, Gideon; Ngondi, Jeremiah M; Emerson, Paul M; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H

2010-08-17

Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1-9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention.

Targeting Trachoma Control through Risk Mapping: The Example of Southern Sudan

PubMed Central

Clements, Archie C. A.; Kur, Lucia W.; Gatpan, Gideon; Ngondi, Jeremiah M.; Emerson, Paul M.; Lado, Mounir; Sabasio, Anthony; Kolaczinski, Jan H.

2010-01-01

Background Trachoma is a major cause of blindness in Southern Sudan. Its distribution has only been partially established and many communities in need of intervention have therefore not been identified or targeted. The present study aimed to develop a tool to improve targeting of survey and control activities. Methods/Principal Findings A national trachoma risk map was developed using Bayesian geostatistics models, incorporating trachoma prevalence data from 112 geo-referenced communities surveyed between 2001 and 2009. Logistic regression models were developed using active trachoma (trachomatous inflammation follicular and/or trachomatous inflammation intense) in 6345 children aged 1–9 years as the outcome, and incorporating fixed effects for age, long-term average rainfall (interpolated from weather station data) and land cover (i.e. vegetation type, derived from satellite remote sensing), as well as geostatistical random effects describing spatial clustering of trachoma. The model predicted the west of the country to be at no or low trachoma risk. Trachoma clusters in the central, northern and eastern areas had a radius of 8 km after accounting for the fixed effects. Conclusion In Southern Sudan, large-scale spatial variation in the risk of active trachoma infection is associated with aridity. Spatial prediction has identified likely high-risk areas to be prioritized for more data collection, potentially to be followed by intervention. PMID:20808910

Estimating the "impact" of out-of-home placement on child well-being: approaching the problem of selection bias.

PubMed

Berger, Lawrence M; Bruch, Sarah K; Johnson, Elizabeth I; James, Sigrid; Rubin, David

2009-01-01

This study used data on 2,453 children aged 4-17 from the National Survey of Child and Adolescent Well-Being and 5 analytic methods that adjust for selection factors to estimate the impact of out-of-home placement on children's cognitive skills and behavior problems. Methods included ordinary least squares (OLS) regressions and residualized change, simple change, difference-in-difference, and fixed effects models. Models were estimated using the full sample and a matched sample generated by propensity scoring. Although results from the unmatched OLS and residualized change models suggested that out-of-home placement is associated with increased child behavior problems, estimates from models that more rigorously adjust for selection bias indicated that placement has little effect on children's cognitive skills or behavior problems.

Performance of time-varying predictors in multilevel models under an assumption of fixed or random effects.

PubMed

Baird, Rachel; Maxwell, Scott E

2016-06-01

Time-varying predictors in multilevel models are a useful tool for longitudinal research, whether they are the research variable of interest or they are controlling for variance to allow greater power for other variables. However, standard recommendations to fix the effect of time-varying predictors may make an assumption that is unlikely to hold in reality and may influence results. A simulation study illustrates that treating the time-varying predictor as fixed may allow analyses to converge, but the analyses have poor coverage of the true fixed effect when the time-varying predictor has a random effect in reality. A second simulation study shows that treating the time-varying predictor as random may have poor convergence, except when allowing negative variance estimates. Although negative variance estimates are uninterpretable, results of the simulation show that estimates of the fixed effect of the time-varying predictor are as accurate for these cases as for cases with positive variance estimates, and that treating the time-varying predictor as random and allowing negative variance estimates performs well whether the time-varying predictor is fixed or random in reality. Because of the difficulty of interpreting negative variance estimates, 2 procedures are suggested for selection between fixed-effect and random-effect models: comparing between fixed-effect and constrained random-effect models with a likelihood ratio test or fitting a fixed-effect model when an unconstrained random-effect model produces negative variance estimates. The performance of these 2 procedures is compared. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Robust inference under the beta regression model with application to health care studies.

PubMed

Ghosh, Abhik

2017-01-01

Data on rates, percentages, or proportions arise frequently in many different applied disciplines like medical biology, health care, psychology, and several others. In this paper, we develop a robust inference procedure for the beta regression model, which is used to describe such response variables taking values in (0, 1) through some related explanatory variables. In relation to the beta regression model, the issue of robustness has been largely ignored in the literature so far. The existing maximum likelihood-based inference has serious lack of robustness against outliers in data and generate drastically different (erroneous) inference in the presence of data contamination. Here, we develop the robust minimum density power divergence estimator and a class of robust Wald-type tests for the beta regression model along with several applications. We derive their asymptotic properties and describe their robustness theoretically through the influence function analyses. Finite sample performances of the proposed estimators and tests are examined through suitable simulation studies and real data applications in the context of health care and psychology. Although we primarily focus on the beta regression models with a fixed dispersion parameter, some indications are also provided for extension to the variable dispersion beta regression models with an application.

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

PubMed

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

2017-08-01

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

Regression Discontinuity Designs: A Guide to Practice. NBER Working Paper No. 13039

ERIC Educational Resources Information Center

Imbens, Guido; Lemieux, Thomas

2007-01-01

In Regression Discontinuity (RD) designs for evaluating causal effects of interventions, assignment to a treatment is determined at least partly by the value of an observed covariate lying on either side of a fixed threshold. These designs were first introduced in the evaluation literature by Thistlewaite and Campbell (1960). With the exception of…

Educational attainment and cigarette smoking: a causal association?†

PubMed Central

Gilman, Stephen E; Martin, Laurie T; Abrams, David B; Kawachi, Ichiro; Kubzansky, Laura; Loucks, Eric B; Rende, Richard; Rudd, Rima; Buka, Stephen L

2016-01-01

Background Despite abundant evidence that lower education is associated with a higher risk of smoking, whether the association is causal has not been convincingly established. Methods We investigated the association between education and lifetime smoking patterns in a birth cohort established in 1959 and followed through adulthood (n = 1311). We controlled for a wide range of potential confounders that were measured prior to school entry, and also estimated sibling fixed effects models to control for unmeasured familial vulnerability to smoking. Results In the full sample of participants, regression analyses adjusting for multiple childhood factors (including socioeconomic status, IQ, behavioural problems, and medical conditions) indicated that the number of pack-years smoked was higher among individuals with less than high school education [rate ratio (RR) = 1.58, confidence interval (CI) = 1.31, 1.91]. However, in the sibling fixed effects analysis the RR was 1.23 (CI = 0.80, 1.93). Similarly, adjusted models estimated in the full sample showed that individuals with less than high school education had fewer short-term (RR = 0.40; CI = 0.23, 0.69) and long-term (RR = 0.59; CI = 0.42, 0.83) quit attempts, and were less likely to quit smoking (odds ratio = 0.34; CI = 0.19, 0.62). The effects of education on quitting smoking were attenuated in the sibling fixed effects models that controlled for familial vulnerability to smoking. Conclusions A substantial portion of the education differential in smoking that has been repeatedly observed is attributable to factors shared by siblings that contribute to shortened educational careers and to lifetime smoking trajectories. Reducing disparities in cigarette smoking, including educational disparities, may therefore require approaches that focus on factors early in life that influence smoking risk over the adult life span. PMID:18180240

Genome-Wide Association Studies with a Genomic Relationship Matrix: A Case Study with Wheat and Arabidopsis.

PubMed

Gianola, Daniel; Fariello, Maria I; Naya, Hugo; Schön, Chris-Carolin

2016-10-13

Standard genome-wide association studies (GWAS) scan for relationships between each of p molecular markers and a continuously distributed target trait. Typically, a marker-based matrix of genomic similarities among individuals ( G: ) is constructed, to account more properly for the covariance structure in the linear regression model used. We show that the generalized least-squares estimator of the regression of phenotype on one or on m markers is invariant with respect to whether or not the marker(s) tested is(are) used for building G,: provided variance components are unaffected by exclusion of such marker(s) from G: The result is arrived at by using a matrix expression such that one can find many inverses of genomic relationship, or of phenotypic covariance matrices, stemming from removing markers tested as fixed, but carrying out a single inversion. When eigenvectors of the genomic relationship matrix are used as regressors with fixed regression coefficients, e.g., to account for population stratification, their removal from G: does matter. Removal of eigenvectors from G: can have a noticeable effect on estimates of genomic and residual variances, so caution is needed. Concepts were illustrated using genomic data on 599 wheat inbred lines, with grain yield as target trait, and on close to 200 Arabidopsis thaliana accessions. Copyright © 2016 Gianola et al.

Understanding bias in relationships between the food environment and diet quality: the Coronary Artery Risk Development in Young Adults (CARDIA) study.

PubMed

Rummo, Pasquale E; Guilkey, David K; Ng, Shu Wen; Meyer, Katie A; Popkin, Barry M; Reis, Jared P; Shikany, James M; Gordon-Larsen, Penny

2017-12-01

The relationship between food environment exposures and diet behaviours is unclear, possibly because the majority of studies ignore potential residual confounding. We used 20 years (1985-1986, 1992-1993 2005-2006) of data from the Coronary Artery Risk Development in Young Adults (CARDIA) study across four US cities (Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; Oakland, California) and instrumental variables (IV) regression to obtain causal estimates of longitudinal associations between the percentage of neighbourhood food outlets (per total food outlets within 1 km network distance of respondent residence) and an a priori diet quality score, with higher scores indicating higher diet quality. To assess the presence and magnitude of bias related to residual confounding, we compared results from causal models (IV regression) to non-causal models, including ordinary least squares regression, which does not account for residual confounding at all and fixed-effects regression, which only controls for time-invariant unmeasured characteristics. The mean diet quality score across follow-up was 63.4 (SD=12.7). A 10% increase in fast food restaurants (relative to full-service restaurants) was associated with a lower diet quality score over time using IV regression (β=-1.01, 95% CI -1.99 to -0.04); estimates were attenuated using non-causal models. The percentage of neighbourhood convenience and grocery stores (relative to supermarkets) was not associated with diet quality in any model, but estimates from non-causal models were similarly attenuated compared with causal models. Ignoring residual confounding may generate biased estimated effects of neighbourhood food outlets on diet outcomes and may have contributed to weak findings in the food environment literature. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

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

HMO Penetration, Hospital Competition, and Growth of Ambulatory Surgery Centers

PubMed Central

Bian, John; Morrisey, Michael A.

2006-01-01

Using metropolitan statistical area (MSA) panel data from 1992-2001 constructed from the 2002 Medicare Online Survey Certification and Reporting (OSCAR) System, we estimate the market effects of health maintenance organization (HMO) penetration and hospital competition on the growth of freestanding ambulatory surgery centers (ASCs). Our regression models with MSA and year fixed effects suggest that a 10-percentage-point increase in HMO penetration is associated with a decrease of 3 ASCs per 1 million population. A decrease from 5 to 4 equal-market-shared hospitals in a market is associated with an increase of 2.5 ASCs per 1 million population. PMID:17290661

HMO penetration, hospital competition, and growth of ambulatory surgery centers.

PubMed

Bian, John; Morrisey, Michael A

2006-01-01

Using metropolitan statistical area (MSA) panel data from 1992-2001 constructed from the 2002 Medicare Online Survey Certification and Reporting (OSCAR) System, we estimate the market effects of health maintenance organization (HMO) penetration and hospital competition on the growth of freestanding ambulatory surgery centers (ASCs). Our regression models with MSA and year fixed effects suggest that a 10-percentage-point increase in HMO penetration is associated with a decrease of 3 ASCs per 1 million population. A decrease from 5 to 4 equal-market-shared hospitals in a market is associated with an increase of 2.5 ASCs per 1 million population.

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

PubMed

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

2016-02-01

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

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

PubMed

Liu, Xian; Engel, Charles C

2012-12-20

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

Using fixed-parameter and random-parameter ordered regression models to identify significant factors that affect the severity of drivers' injuries in vehicle-train collisions.

PubMed

Dabbour, Essam; Easa, Said; Haider, Murtaza

2017-10-01

This study attempts to identify significant factors that affect the severity of drivers' injuries when colliding with trains at railroad-grade crossings by analyzing the individual-specific heterogeneity related to those factors over a period of 15 years. Both fixed-parameter and random-parameter ordered regression models were used to analyze records of all vehicle-train collisions that occurred in the United States from January 1, 2001 to December 31, 2015. For fixed-parameter ordered models, both probit and negative log-log link functions were used. The latter function accounts for the fact that lower injury severity levels are more probable than higher ones. Separate models were developed for heavy and light-duty vehicles. Higher train and vehicle speeds, female, and young drivers (below the age of 21 years) were found to be consistently associated with higher severity of drivers' injuries for both heavy and light-duty vehicles. Furthermore, favorable weather, light-duty trucks (including pickup trucks, panel trucks, mini-vans, vans, and sports-utility vehicles), and senior drivers (above the age of 65 years) were found be consistently associated with higher severity of drivers' injuries for light-duty vehicles only. All other factors (e.g. air temperature, the type of warning devices, darkness conditions, and highway pavement type) were found to be temporally unstable, which may explain the conflicting findings of previous studies related to those factors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating the price elasticity of beer: meta-analysis of data with heterogeneity, dependence, and publication bias.

PubMed

Nelson, Jon P

2014-01-01

Precise estimates of price elasticities are important for alcohol tax policy. Using meta-analysis, this paper corrects average beer elasticities for heterogeneity, dependence, and publication selection bias. A sample of 191 estimates is obtained from 114 primary studies. Simple and weighted means are reported. Dependence is addressed by restricting number of estimates per study, author-restricted samples, and author-specific variables. Publication bias is addressed using funnel graph, trim-and-fill, and Egger's intercept model. Heterogeneity and selection bias are examined jointly in meta-regressions containing moderator variables for econometric methodology, primary data, and precision of estimates. Results for fixed- and random-effects regressions are reported. Country-specific effects and sample time periods are unimportant, but several methodology variables help explain the dispersion of estimates. In models that correct for selection bias and heterogeneity, the average beer price elasticity is about -0.20, which is less elastic by 50% compared to values commonly used in alcohol tax policy simulations. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

Health shocks adversely impact participation in the labour force in a working age population: a longitudinal analysis.

PubMed

Carter, Kristie N; Gunasekara, Fiona Imlach; Blakely, Tony; Richardson, Ken

2013-06-01

It is well understood that health affects labour force participation (LFP). However, much of the published research has been on older (retiring age) populations and using subjective health measures. This paper aims to assess the impact of an objective measure of 'health shock' (cancer registration or hospitalisation) on LFP in a working age population using longitudinal panel study data and fixed effect regression analyses. Seven waves of data from 2002-09 from the longitudinal Survey of Family, Income and Employment (SoFIE) were used, including working aged individuals who consented to have their survey information linked to health records (n=6,780). Fixed effect conditional logistic regression was used to model the impact of health shocks (hospitalisation or cancer registration) in the previous year on labour force participation at date of annual interview. Models were stratified by gender, age group (25-39 years, 40-54 years) and gender by age group. A health shock was associated with a significantly increased risk of subsequent non-participation in the labour force (odds ratio 1.54, 95%CI 1.30-1.82). Although interactions of age, sex and age by sex with health shock were not statistically significant, the association was largest in younger men and women. Using an objective measure of health, we have shown that a health shock adversely affects subsequent labour force participation. There are a number of policy and practice implications relating to support for working age people who have hospitalisations. © 2013 The Authors. ANZJPH © 2013 Public Health Association of Australia.

Genetic parameters for stayability to consecutive calvings in Zebu cattle.

PubMed

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

2017-12-22

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

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

PubMed Central

2011-01-01

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

Social-Emotional Effects of Early Childhood Education Programs in Tulsa

ERIC Educational Resources Information Center

Gormley, William T., Jr.; Phillips, Deborah A.; Newmark, Katie; Welti, Kate; Adelstein, Shirley

2011-01-01

This article assesses the effects of Tulsa, Oklahoma's early childhood education programs on social-emotional outcomes, examining teacher ratings of children's behavior from the Adjustment Scales for Preschool Intervention and a measure of attentiveness using fixed effects regressions with propensity score matching. The sample includes 2,832…

Genetic analysis of longitudinal measurements of performance traits in selection lines for residual feed intake in Yorkshire swine.

PubMed

Cai, W; Kaiser, M S; Dekkers, J C M

2011-05-01

A 5-generation selection experiment in Yorkshire pigs for feed efficiency consists of a line selected for low residual feed intake (LRFI) and a random control line (CTRL). The objectives of this study were to use random regression models to estimate genetic parameters for daily feed intake (DFI), BW, backfat (BF), and loin muscle area (LMA) along the growth trajectory and to evaluate the effect of LRFI selection on genetic curves for DFI and BW. An additional objective was to compare random regression models using polynomials (RRP) and spline functions (RRS). Data from approximately 3 to 8 mo of age on 586 boars and 495 gilts across 5 generations were used. The average number of measurements was 85, 14, 5, and 5 for DFI, BW, BF, and LMA. The RRP models for these 4 traits were fitted with pen × on-test group as a fixed effect, second-order Legendre polynomials of age as fixed curves for each generation, and random curves for additive genetic and permanent environmental effects. Different residual variances were used for the first and second halves of the test period. The RRS models were fitted with the same fixed effects and residual variance structure as the RRP models and included genetic and permanent environmental random effects for both splines and linear Legendre polynomials of age. The RRP model was used for further analysis because the RRS model had erratic estimates of phenotypic variance and heritability, despite having a smaller Bayesian information criterion than the RRP model. From 91 to 210 d of age, estimates of heritability from the RRP model ranged from 0.10 to 0.37 for boars and 0.14 to 0.26 for gilts for DFI, from 0.39 to 0.58 for boars and 0.55 to 0.61 for gilts for BW, from 0.48 to 0.61 for boars and 0.61 to 0.79 for gilts for BF, and from 0.46 to 0.55 for boars and 0.63 to 0.81 for gilts for LMA. In generation 5, LRFI pigs had lower average genetic curves than CTRL pigs for DFI and BW, especially toward the end of the test period; estimated line differences (CTRL-LRFI) for DFI were 0.04 kg/d for boars and 0.12 kg/d for gilts at 105 d and 0.20 kg/d for boars and 0.24 kg/d for gilts at 195 d. Line differences for BW were 0.17 kg for boars and 0.69 kg for gilts at 105 d and 3.49 kg for boars and 8.96 kg for gilts at 195 d. In conclusion, selection for LRFI has resulted in a lower feed intake curve and a lower BW curve toward maturity.

Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques

PubMed Central

Jones, Kelly W.; Lewis, David J.

2015-01-01

Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented—from protected areas to payments for ecosystem services (PES)—to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing ‘matching’ to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods—an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators—due to the presence of unobservable bias—that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that if time-invariant unobservables are not present, matching combined with differences in means or cross-sectional regression leads to similar estimates of program effectiveness as matching combined with fixed effects panel regression. These results highlight the importance of considering observable and unobservable forms of bias and the methodological assumptions across estimators when designing an impact evaluation of conservation programs. PMID:26501964

Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques.

PubMed

Jones, Kelly W; Lewis, David J

2015-01-01

Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented--from protected areas to payments for ecosystem services (PES)--to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing 'matching' to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods--an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators--due to the presence of unobservable bias--that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that if time-invariant unobservables are not present, matching combined with differences in means or cross-sectional regression leads to similar estimates of program effectiveness as matching combined with fixed effects panel regression. These results highlight the importance of considering observable and unobservable forms of bias and the methodological assumptions across estimators when designing an impact evaluation of conservation programs.

Friendships Lost: The Social Consequences of Violent Victimization.

PubMed

Wallace, Lacey N; Ménard, Kim S

2017-01-01

Few studies have examined the impact of violent victimization on friendship networks. This study used two waves of data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) to examine the effects of violent victimization on number peer- and self-reported friendships. Guided by stigma theory (Goffman, 1963), fixed-effect regression models controlling for depression, delinquency, substance use, and school engagement were completed to predict changes in number of friends following victimization. Consistent with the theory, results indicate that experiencing violent victimization (e.g., jumped, stabbed, shot at) was associated with a decrease in number of friends. These effects were magnified for females and for individuals with a greater number of depressive symptoms. These results were consistent even when models were run separately for each individual type of victimization. Treatment and prevention implications are discussed.

Nonlinear isochrones in murine left ventricular pressure-volume loops: how well does the time-varying elastance concept hold?

PubMed

Claessens, T E; Georgakopoulos, D; Afanasyeva, M; Vermeersch, S J; Millar, H D; Stergiopulos, N; Westerhof, N; Verdonck, P R; Segers, P

2006-04-01

The linear time-varying elastance theory is frequently used to describe the change in ventricular stiffness during the cardiac cycle. The concept assumes that all isochrones (i.e., curves that connect pressure-volume data occurring at the same time) are linear and have a common volume intercept. Of specific interest is the steepest isochrone, the end-systolic pressure-volume relationship (ESPVR), of which the slope serves as an index for cardiac contractile function. Pressure-volume measurements, achieved with a combined pressure-conductance catheter in the left ventricle of 13 open-chest anesthetized mice, showed a marked curvilinearity of the isochrones. We therefore analyzed the shape of the isochrones by using six regression algorithms (two linear, two quadratic, and two logarithmic, each with a fixed or time-varying intercept) and discussed the consequences for the elastance concept. Our main observations were 1) the volume intercept varies considerably with time; 2) isochrones are equally well described by using quadratic or logarithmic regression; 3) linear regression with a fixed intercept shows poor correlation (R(2) < 0.75) during isovolumic relaxation and early filling; and 4) logarithmic regression is superior in estimating the fixed volume intercept of the ESPVR. In conclusion, the linear time-varying elastance fails to provide a sufficiently robust model to account for changes in pressure and volume during the cardiac cycle in the mouse ventricle. A new framework accounting for the nonlinear shape of the isochrones needs to be developed.

Efficient Regressions via Optimally Combining Quantile Information*

PubMed Central

Zhao, Zhibiao; Xiao, Zhijie

2014-01-01

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As the number of quantiles increases, this upper bound decreases and the asymptotic variance of the proposed estimator approaches the Cramér-Rao lower bound under appropriate conditions. In the case of non-regular statistical estimation, the proposed estimator leads to super-efficient estimation. We illustrate the proposed method for several widely used regression models. Both asymptotic theory and Monte Carlo experiments show the superior performance over existing methods. PMID:25484481

Does government expenditure reduce inequalities in infant mortality rates in low- and middle-income countries?: A time-series, ecological analysis of 48 countries from 1993 to 2013.

PubMed

Baker, Peter; Hone, Thomas; Reeves, Aaron; Avendano, Mauricio; Millett, Christopher

2018-06-27

Inequalities in infant mortality rates (IMRs) are rising in some low- and middle-income countries (LMICs) and decreasing in others, but the explanation for these divergent trends is unclear. We investigate whether government expenditures and redistribution are associated with reductions in inequalities in IMRs. We estimated country-level fixed-effects panel regressions for 48 LMICs (142 country observations). Slope and Relative Indices of Inequality in IMRs (SII and RII) were calculated from Demographic and Health Surveys between 1993 and 2013. RII and SII were regressed on government expenditure (total, health and non-health) and redistribution, controlling for gross domestic product (GDP), private health expenditures, a democracy indicator, country fixed effects and time. Mean SII and RII was 39.12 and 0.69, respectively. In multivariate models, a 1 percentage point increase in total government expenditure (% of GDP) was associated with a decrease in SII of -2.468 [95% confidence intervals (CIs): -4.190, -0.746] and RII of -0.026 (95% CIs: -0.048, -0.004). Lower inequalities were associated with higher non-health government expenditure, but not higher government health expenditure. Associations with inequalities were non-significant for GDP, government redistribution, and private health expenditure. Understanding how non-health government expenditure reduces inequalities in IMR, and why health expenditures may not, will accelerate progress towards the Sustainable Development Goals.

Birth weight and cognitive development in adolescence: causal relationship or social selection?

PubMed

Gorman, Bridget K

2002-01-01

Using data from the National Longitudinal Survey of Adolescent Health (Add Health), I investigate the relationship between birth weight and cognitive development among adolescents aged 12-17. Initial OLS regression models reveal a significant, positive relationship between low birth weight and verbal ability. Controlling for demographic, socioeconomic, and other adolescent characteristics modifies, but does not eliminate, this relationship. Additional models that stratify the sample by parental education illustrate the greater importance of other family and adolescent characteristics for cognitive development in adolescence, and a diminished role of birth weight. In the final section of the paper, fixed effects models of non-twin full siblings indicate no significant association between birth weight and verbal ability, suggesting that traditional cross-sectional models overstate the influence of birth weight for cognitive development in adolescence.

Financial hardship, mastery and social support: Explaining poor mental health amongst the inadequately employed using data from the HILDA survey.

PubMed

Crowe, Laura; Butterworth, Peter; Leach, Liana

2016-12-01

This study analysed data from the Household Income and Labour Dynamics in Australia (HILDA) Survey to examine the relationship between employment status and mental health, and the mediating effects of financial hardship, mastery and social support. In addition, the study sought to explore the effects of duration of unemployment on mental health. The primary analysis used three waves of data from the HILDA Survey with 4965 young adult respondents. Longitudinal population-averaged logistic regression models assessed the association of employment status and mental health, including the contribution of mastery, financial hardship and social support in explaining this association between employment groups (unemployed vs. employed; under employed vs. employed). Sensitivity analyses utilised a fixed-effects approach and also considered the full-range of working-age respondents. Regression analysis was used to explore the effect of duration of unemployment on mental health. Respondents' who identified as unemployed or underemployed were at higher risk of poor mental health outcomes when compared to their employed counterparts. This association was ameliorated when accounting for mastery, financial hardship and social support for the unemployed, and was fully mediated for the underemployed. The fixed-effects models showed the transition to unemployment was associated with a decline in mental health and that mastery in particular contributed to that change. The same results were found with a broader age range of respondents. Finally, the relationship between duration of unemployment and mental health was not linear, with mental health showing marked decline across the first 9 weeks of unemployment. Mastery, social support and financial hardship are important factors in understanding the association of poor mental health with both unemployment and underemployment. Furthermore, the results suggest that the most deleterious effects on mental health may occur in the first two months of unemployment before plateauing. In order to prevent deterioration in mental health, these findings suggest intervention should commence immediately following job loss.

Trans-ethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution

PubMed Central

Mägi, Reedik; Horikoshi, Momoko; Sofer, Tamar; Mahajan, Anubha; Kitajima, Hidetoshi; Franceschini, Nora; McCarthy, Mark I.; Morris, Andrew P.

2017-01-01

Abstract Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals. PMID:28911207

Sensitivity of breeding values for carcass traits of meat-type quail to changes in dietary (methionine + cystine):lysine ratio using reaction norm models.

PubMed

Miranda, J A; Pires, A V; Abreu, L R A; Mota, L F M; Silva, M A; Bonafé, C M; Lima, H J D; Martins, P G M A

2016-12-01

Our objective was to evaluate changes in breeding values for carcass traits of two meat-type quail (Coturnix coturnix) strains (LF1 and LF2) to changes in the dietary (methionine + cystine):lysine ([Met + Cys]:Lys) ratio due to genotype by environment (G × E) interaction via reaction norm. A total of 7000 records of carcass weight and yield were used for analyses. During the initial phase (from hatching to day 21), five diets with increasing (Met + Cys):Lys ratios (0.61, 0.66, 0.71, 0.76 and 0.81), containing 26.1% crude protein and 2900 kcal ME/kg, were evaluated. Analyses were performed using random regression models that included linear functions of sex (fixed effect) and breeding value (random effect) for carcass weight and yield, without and with heterogeneous residual variance adjustment. Both fixed and random effects were modelled using Legendre polynomials of second order. Genetic variance and heritability estimates were affected by both (Met + Cys):Lys ratio and strain. We observed that a G × E interaction was present, with changes in the breeding value ranking. Therefore, genetic evaluation for carcass traits should be performed under the same (Met + Cys):Lys ratio in which quails are raised. © 2016 Blackwell Verlag GmbH.

a Comparison Between Two Ols-Based Approaches to Estimating Urban Multifractal Parameters

NASA Astrophysics Data System (ADS)

Huang, Lin-Shan; Chen, Yan-Guang

Multifractal theory provides a new spatial analytical tool for urban studies, but many basic problems remain to be solved. Among various pending issues, the most significant one is how to obtain proper multifractal dimension spectrums. If an algorithm is improperly used, the parameter spectrums will be abnormal. This paper is devoted to investigating two ordinary least squares (OLS)-based approaches for estimating urban multifractal parameters. Using empirical study and comparative analysis, we demonstrate how to utilize the adequate linear regression to calculate multifractal parameters. The OLS regression analysis has two different approaches. One is that the intercept is fixed to zero, and the other is that the intercept is not limited. The results of comparative study show that the zero-intercept regression yields proper multifractal parameter spectrums within certain scale range of moment order, while the common regression method often leads to abnormal multifractal parameter values. A conclusion can be reached that fixing the intercept to zero is a more advisable regression method for multifractal parameters estimation, and the shapes of spectral curves and value ranges of fractal parameters can be employed to diagnose urban problems. This research is helpful for scientists to understand multifractal models and apply a more reasonable technique to multifractal parameter calculations.

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

PubMed

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

2015-12-01

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

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

PubMed

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

2015-06-01

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

Model Selection with the Linear Mixed Model for Longitudinal Data

ERIC Educational Resources Information Center

Ryoo, Ji Hoon

2011-01-01

Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…

The risk of traumatic lumbar punctures in children with acute lymphoblastic leukaemia.

PubMed

Shaikh, Furqan; Voicu, Laura; Tole, Soumitra; To, Teresa; Doria, Andrea S; Sung, Lillian; Alexander, Sarah

2014-05-01

Traumatic lumbar punctures with blasts (TLP+) in children with acute lymphoblastic leukaemia (ALL) obscure central nervous system status and are associated with a poorer event-free survival (EFS). We conducted a retrospective cohort study of all lumbar punctures (LPs) for children with ALL diagnosed at our institution from 2005 to 2009. We utilised random-effects and fixed-effects repeated-measures logistic regression analyses to identify risk factors for TLPs. Fixed-effects models use each patient as his or her own control. We used survival analysis to describe outcomes after a TLP+. 264 children underwent 5267 evaluable lumbar punctures (LPs), of which 944 (17.9%) were traumatic. In the multivariable random-effects model, variables significantly associated with TLPs were age <1year (odds ratio (OR) 3.46, 95% confidence interval (CI) 2.06-5.81) or age ⩾10years (OR 2.00, CI 1.66-2.40); body mass index percentile ⩾95 (OR 1.44, CI 1.19-1.75); platelet count <100×10(3)/μL (OR 1.49, CI 1.08-20.7); fewer days since previous LP (OR 5.13, CI 2.34-11.25 for ⩾16days versus 0-3days); and a preceding TLP (OR 1.43, CI 1.19-1.73). In the fixed-effects model, image-guidance reduced the odds of TLP (OR 0.55, CI 0.32-0.95). The 5-year EFS (±SE) for children with TLP+ (77±8%) was significantly lower than for children with CNS1 status (93±2%; p=0.002). The frequency of TLP remains high. Consistent with previous studies, a TLP+ at diagnosis was associated with a poorer EFS. These risk factors can allow identifying interventions to reduce TLPs and directing interventions to those at highest risk. Copyright © 2014 Elsevier Ltd. All rights reserved.

Effects of macroeconomic trends on social security spending due to sickness and disability.

PubMed

Khan, Jahangir; Gerdtham, Ulf-G; Jansson, Bjarne

2004-11-01

We analyzed the relationship between macroeconomic conditions, measured as unemployment rate and social security spending, from 4 social security schemes and total spending due to sickness and disability. We obtained aggregated panel data from 13 Organization for Economic Cooperation and Development member countries for 1980-1996. We used regression analysis and fixed effect models to examine spending on sickness benefits, disability pensions, occupational-injury benefits, survivor's pensions, and total spending. A decline in unemployment increased sickness benefits spending and reduced disability pension spending. These effects reversed direction after 4 years of unemployment. Inclusion of mortality rate as an additional variable in the analysis did not affect the findings. Macroeconomic conditions influence some reimbursements from social security schemes but not total spending.

Effect of vitamins C and E on insulin resistance in diabetes: a meta-analysis study.

PubMed

Khodaeian, Mehrnoosh; Tabatabaei-Malazy, Ozra; Qorbani, Mostafa; Farzadfar, Farshad; Amini, Peyvand; Larijani, Bagher

2015-11-01

Data regarding the effect of vitamin C (VC) and vitamin E (VE) supplementation on insulin resistance in type 2 diabetes mellitus (T2DM) are controversial. We aimed to systematically review the current data on this topic. All randomized controlled trials (RCTs) conducted to assess the effect of VC and/or VE on insulin resistance in diabetes published in Google Scholar and PubMed web databases until January 2014 were included. Exclusion criteria were studies conducted in animal, type 1 DM, children or pregnant women. Main outcome measure was insulin resistance by homoeostasis model assessment (HOMA) index. According to degree of heterogeneity, fixed- or random-effect model was employed by stata software (11.0). We selected 14 RCTs involving 735 patients with T2DM. VE or mixture-mode supplementation did not have any significant effect on HOMA with a standardized mean difference (SMD): 0·017, 95% CI: -0·376 to 0·411 (P = 0·932); and SMD: -0·035, 95% CI: -0·634 to 0·025 (P = 0·070), respectively, by random-effect model. VC supplement alone did not improve insulin resistance with a SMD: -0·150, 95% CI: -0·494 to 0·194 (P = 0·391), by fixed-effect model. Meta-regression test demonstrated that HOMA index may have not been influenced by the year of publication, dosage or duration of treatment. The sole intake of VC, VE or their combination with other antioxidants could not improve insulin resistance in diabetes. © 2015 Stichting European Society for Clinical Investigation Journal Foundation.

M5 model tree based predictive modeling of road accidents on non-urban sections of highways in India.

PubMed

Singh, Gyanendra; Sachdeva, S N; Pal, Mahesh

2016-11-01

This work examines the application of M5 model tree and conventionally used fixed/random effect negative binomial (FENB/RENB) regression models for accident prediction on non-urban sections of highway in Haryana (India). Road accident data for a period of 2-6 years on different sections of 8 National and State Highways in Haryana was collected from police records. Data related to road geometry, traffic and road environment related variables was collected through field studies. Total two hundred and twenty two data points were gathered by dividing highways into sections with certain uniform geometric characteristics. For prediction of accident frequencies using fifteen input parameters, two modeling approaches: FENB/RENB regression and M5 model tree were used. Results suggest that both models perform comparably well in terms of correlation coefficient and root mean square error values. M5 model tree provides simple linear equations that are easy to interpret and provide better insight, indicating that this approach can effectively be used as an alternative to RENB approach if the sole purpose is to predict motor vehicle crashes. Sensitivity analysis using M5 model tree also suggests that its results reflect the physical conditions. Both models clearly indicate that to improve safety on Indian highways minor accesses to the highways need to be properly designed and controlled, the service roads to be made functional and dispersion of speeds is to be brought down. Copyright © 2016 Elsevier Ltd. All rights reserved.

Young Women’s Dynamic Family Size Preferences in the Context of Transitioning Fertility

PubMed Central

Yeatman, Sara; Sennott, Christie; Culpepper, Steven

2013-01-01

Dynamic theories of family size preferences posit that they are not a fixed and stable goal but rather are akin to a moving target that changes within individuals over time. Nonetheless, in high-fertility contexts, changes in family size preferences tend to be attributed to low construct validity and measurement error instead of genuine revisions in preferences. To address the appropriateness of this incongruity, the present study examines evidence for the sequential model of fertility among a sample of young Malawian women living in a context of transitioning fertility. Using eight waves of closely spaced data and fixed-effects models, we find that these women frequently change their reported family size preferences and that these changes are often associated with changes in their relationship and reproductive circumstances. The predictability of change gives credence to the argument that ideal family size is a meaningful construct, even in this higher-fertility setting. Changes are not equally predictable across all women, however, and gamma regression results demonstrate that women for whom reproduction is a more distant goal change their fertility preferences in less-predictable ways. PMID:23619999

Young women's dynamic family size preferences in the context of transitioning fertility.

PubMed

Yeatman, Sara; Sennott, Christie; Culpepper, Steven

2013-10-01

Dynamic theories of family size preferences posit that they are not a fixed and stable goal but rather are akin to a moving target that changes within individuals over time. Nonetheless, in high-fertility contexts, changes in family size preferences tend to be attributed to low construct validity and measurement error instead of genuine revisions in preferences. To address the appropriateness of this incongruity, the present study examines evidence for the sequential model of fertility among a sample of young Malawian women living in a context of transitioning fertility. Using eight waves of closely spaced data and fixed-effects models, we find that these women frequently change their reported family size preferences and that these changes are often associated with changes in their relationship and reproductive circumstances. The predictability of change gives credence to the argument that ideal family size is a meaningful construct, even in this higher-fertility setting. Changes are not equally predictable across all women, however, and gamma regression results demonstrate that women for whom reproduction is a more distant goal change their fertility preferences in less-predictable ways.

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle

PubMed Central

Cho, C. I.; Alam, M.; Choi, T. J.; Choy, Y. H.; Choi, J. G.; Lee, S. S.; Cho, K. H.

2016-01-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3–L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea. PMID:26954184

Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle.

PubMed

Cho, C I; Alam, M; Choi, T J; Choy, Y H; Choi, J G; Lee, S S; Cho, K H

2016-05-01

The objectives of the study were to estimate genetic parameters for milk production traits of Holstein cattle using random regression models (RRMs), and to compare the goodness of fit of various RRMs with homogeneous and heterogeneous residual variances. A total of 126,980 test-day milk production records of the first parity Holstein cows between 2007 and 2014 from the Dairy Cattle Improvement Center of National Agricultural Cooperative Federation in South Korea were used. These records included milk yield (MILK), fat yield (FAT), protein yield (PROT), and solids-not-fat yield (SNF). The statistical models included random effects of genetic and permanent environments using Legendre polynomials (LP) of the third to fifth order (L3-L5), fixed effects of herd-test day, year-season at calving, and a fixed regression for the test-day record (third to fifth order). The residual variances in the models were either homogeneous (HOM) or heterogeneous (15 classes, HET15; 60 classes, HET60). A total of nine models (3 orders of polynomials×3 types of residual variance) including L3-HOM, L3-HET15, L3-HET60, L4-HOM, L4-HET15, L4-HET60, L5-HOM, L5-HET15, and L5-HET60 were compared using Akaike information criteria (AIC) and/or Schwarz Bayesian information criteria (BIC) statistics to identify the model(s) of best fit for their respective traits. The lowest BIC value was observed for the models L5-HET15 (MILK; PROT; SNF) and L4-HET15 (FAT), which fit the best. In general, the BIC values of HET15 models for a particular polynomial order was lower than that of the HET60 model in most cases. This implies that the orders of LP and types of residual variances affect the goodness of models. Also, the heterogeneity of residual variances should be considered for the test-day analysis. The heritability estimates of from the best fitted models ranged from 0.08 to 0.15 for MILK, 0.06 to 0.14 for FAT, 0.08 to 0.12 for PROT, and 0.07 to 0.13 for SNF according to days in milk of first lactation. Genetic variances for studied traits tended to decrease during the earlier stages of lactation, which were followed by increases in the middle and decreases further at the end of lactation. With regards to the fitness of the models and the differential genetic parameters across the lactation stages, we could estimate genetic parameters more accurately from RRMs than from lactation models. Therefore, we suggest using RRMs in place of lactation models to make national dairy cattle genetic evaluations for milk production traits in Korea.

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

Blomberg, Simon P; Lefevre, James G; Wells, Jessie A; Waterhouse, Mary

2012-05-01

We prove that the slope parameter of the ordinary least squares regression of phylogenetically independent contrasts (PICs) conducted through the origin is identical to the slope parameter of the method of generalized least squares (GLSs) regression under a Brownian motion model of evolution. This equivalence has several implications: 1. Understanding the structure of the linear model for GLS regression provides insight into when and why phylogeny is important in comparative studies. 2. The limitations of the PIC regression analysis are the same as the limitations of the GLS model. In particular, phylogenetic covariance applies only to the response variable in the regression and the explanatory variable should be regarded as fixed. Calculation of PICs for explanatory variables should be treated as a mathematical idiosyncrasy of the PIC regression algorithm. 3. Since the GLS estimator is the best linear unbiased estimator (BLUE), the slope parameter estimated using PICs is also BLUE. 4. If the slope is estimated using different branch lengths for the explanatory and response variables in the PIC algorithm, the estimator is no longer the BLUE, so this is not recommended. Finally, we discuss whether or not and how to accommodate phylogenetic covariance in regression analyses, particularly in relation to the problem of phylogenetic uncertainty. This discussion is from both frequentist and Bayesian perspectives.

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

PubMed

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

2018-05-12

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

The effect of the water tariff structures on the water consumption in Mallorcan hotels

NASA Astrophysics Data System (ADS)

Deyà-Tortella, Bartolomé; Garcia, Celso; Nilsson, William; Tirado, Dolores

2016-08-01

Tourism increases water demand, especially in coastal areas and on islands, and can also cause water shortages during the dry season and the degradation of the water supply. The aim of this study is to evaluate the impact of water price structures on hotel water consumption on the island of Mallorca (Spain). All tourist municipalities on the island use different pricing structures, such as flat or block rates, and different tariffs. This exogenous variation is used to evaluate the effect of prices on water consumption for a sample of 134 hotels. The discontinuity of the water tariff structure and the fixed rate, which depends on the number of hotel beds, generate endogeneity problems. We propose an econometric model, an instrumental variable quantile regression for within artificial blocks transformed data, to solve both problems. The coefficients corresponding to the price variables are not found to be significantly different from zero. The sign of the effect is negative, but the magnitude is negligible: a 1% increase in all prices would reduce consumption by an average of only 0.024%. This result is probably due to the small share of water costs with respect to the total hotel operational costs (around 4%). Our regression model concludes that the introduction of water-saving initiatives constitutes an effective way to reduce consumption.

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

PubMed

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

2018-04-01

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

Evaluation of aerial survey methods for Dall's sheep

USGS Publications Warehouse

Udevitz, Mark S.; Shults, Brad S.; Adams, Layne G.; Kleckner, Christopher

2006-01-01

Most Dall's sheep (Ovis dalli dalli) population-monitoring efforts use intensive aerial surveys with no attempt to estimate variance or adjust for potential sightability bias. We used radiocollared sheep to assess factors that could affect sightability of Dall's sheep in standard fixed-wing and helicopter surveys and to evaluate feasibility of methods that might account for sightability bias. Work was conducted in conjunction with annual aerial surveys of Dall's sheep in the western Baird Mountains, Alaska, USA, in 2000–2003. Overall sightability was relatively high compared with other aerial wildlife surveys, with 88% of the available, marked sheep detected in our fixed-wing surveys. Total counts from helicopter surveys were not consistently larger than counts from fixed-wing surveys of the same units, and detection probabilities did not differ for the 2 aircraft types. Our results suggest that total counts from helicopter surveys cannot be used to obtain reliable estimates of detection probabilities for fixed-wing surveys. Groups containing radiocollared sheep often changed in size and composition before they could be observed by a second crew in units that were double-surveyed. Double-observer methods that require determination of which groups were detected by each observer will be infeasible unless survey procedures can be modified so that groups remain more stable between observations. Mean group sizes increased during our study period, and our logistic regression sightability model indicated that detection probabilities increased with group size. Mark–resight estimates of annual population sizes were similar to sightability-model estimates, and confidence intervals overlapped broadly. We recommend the sightability-model approach as the most effective and feasible of the alternatives we considered for monitoring Dall's sheep populations.

Comparison of regression coefficient and GIS-based methodologies for regional estimates of forest soil carbon stocks.

PubMed

Campbell, J Elliott; Moen, Jeremie C; Ney, Richard A; Schnoor, Jerald L

2008-03-01

Estimates of forest soil organic carbon (SOC) have applications in carbon science, soil quality studies, carbon sequestration technologies, and carbon trading. Forest SOC has been modeled using a regression coefficient methodology that applies mean SOC densities (mass/area) to broad forest regions. A higher resolution model is based on an approach that employs a geographic information system (GIS) with soil databases and satellite-derived landcover images. Despite this advancement, the regression approach remains the basis of current state and federal level greenhouse gas inventories. Both approaches are analyzed in detail for Wisconsin forest soils from 1983 to 2001, applying rigorous error-fixing algorithms to soil databases. Resulting SOC stock estimates are 20% larger when determined using the GIS method rather than the regression approach. Average annual rates of increase in SOC stocks are 3.6 and 1.0 million metric tons of carbon per year for the GIS and regression approaches respectively.

Measuring Work Environment and Performance in Nursing Homes

PubMed Central

Temkin-Greener, Helena; Zheng, Nan (Tracy); Katz, Paul; Zhao, Hongwei; Mukamel, Dana B.

2008-01-01

Background Qualitative studies of the nursing home work environment have long suggested that such attributes as leadership and communication may be related to nursing home performance, including residents' outcomes. However, empirical studies examining these relationships have been scant. Objectives This study is designed to: develop an instrument for measuring nursing home work environment and perceived work effectiveness; test the reliability and validity of the instrument; and identify individual and facility-level factors associated with better facility performance. Research Design and Methods The analysis was based on survey responses provided by managers (N=308) and direct care workers (N=7,418) employed in 162 facilities throughout New York State. Exploratory factor analysis, Chronbach's alphas, analysis of variance, and regression models were used to assess instrument reliability and validity. Multivariate regression models, with fixed facility effects, were used to examine factors associated with work effectiveness. Results The reliability and the validity of the survey instrument for measuring work environment and perceived work effectiveness has been demonstrated. Several individual (e.g. occupation, race) and facility characteristics (e.g. management style, workplace conditions, staffing) that are significant predictors of perceived work effectiveness were identified. Conclusions The organizational performance model used in this study recognizes the multidimensionality of the work environment in nursing homes. Our findings suggest that efforts at improving work effectiveness must also be multifaceted. Empirical findings from such a line of research may provide insights for improving the quality of the work environment and ultimately the quality of residents' care. PMID:19330892

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

Education and Entrepreneurship in Canada: Evidence from (Repeated) Cross-Sectional Data

ERIC Educational Resources Information Center

Masakure, Oliver

2015-01-01

This paper estimates the causal effect of education on entrepreneurship choice in Canada taking into account the endogeneity of education. The data come from the General and Social Surveys (2000-2009). We consider the effect of two extreme education levels: university and some/no education. Regressions are based on fixed effects with two-stage…

Global Potential Net Prmary Production Predicted from Vegetation Class, Precipitation, and Temperature

USDA-ARS?s Scientific Manuscript database

Net Primary Production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approxima...

Determination of Landslide and Driftwood Potentials by Fixed-wing UAV-Borne RGB and NIR images: A Case Study of Shenmu Area in Taiwan

NASA Astrophysics Data System (ADS)

Chen, Su-Chin; Hsiao, Yu-Shen; Chung, Ta-Hsien

2015-04-01

This study is aimed at determining the landslide and driftwood potentials at Shenmu area in Taiwan by Unmanned Aerial Vehicle (UAV). High-resolution orthomosaics and digital surface models (DSMs) are both obtained from several UAV practical surveys by using a red-green-blue(RGB) camera and a near-infrared(NIR) one, respectively. Couples of artificial aerial survey targets are used for ground control in photogrammtry. The algorithm for this study is based on Logistic regression. 8 main factors, which are elevations, terrain slopes, terrain aspects, terrain reliefs, terrain roughness, distances to roads, distances to rivers, land utilizations, are taken into consideration in our Logistic regression model. The related results from UAV are compared with those from traditional photogrammetry. Overall, the study is focusing on monitoring the distribution of the areas with high-risk landslide and driftwood potentials in Shenmu area by Fixed-wing UAV-Borne RGB and NIR images. We also further analyze the relationship between forests, landslides, disaster potentials and upper river areas.

Mixed effects versus fixed effects modelling of binary data with inter-subject variability.

PubMed

Murphy, Valda; Dunne, Adrian

2005-04-01

The question of whether or not a mixed effects model is required when modelling binary data with inter-subject variability and within subject correlation was reported in this journal by Yano et al. (J. Pharmacokin. Pharmacodyn. 28:389-412 [2001]). That report used simulation experiments to demonstrate that, under certain circumstances, the use of a fixed effects model produced more accurate estimates of the fixed effect parameters than those produced by a mixed effects model. The Laplace approximation to the likelihood was used when fitting the mixed effects model. This paper repeats one of those simulation experiments, with two binary observations recorded for every subject, and uses both the Laplace and the adaptive Gaussian quadrature approximations to the likelihood when fitting the mixed effects model. The results show that the estimates produced using the Laplace approximation include a small number of extreme outliers. This was not the case when using the adaptive Gaussian quadrature approximation. Further examination of these outliers shows that they arise in situations in which the Laplace approximation seriously overestimates the likelihood in an extreme region of the parameter space. It is also demonstrated that when the number of observations per subject is increased from two to three, the estimates based on the Laplace approximation no longer include any extreme outliers. The root mean squared error is a combination of the bias and the variability of the estimates. Increasing the sample size is known to reduce the variability of an estimator with a consequent reduction in its root mean squared error. The estimates based on the fixed effects model are inherently biased and this bias acts as a lower bound for the root mean squared error of these estimates. Consequently, it might be expected that for data sets with a greater number of subjects the estimates based on the mixed effects model would be more accurate than those based on the fixed effects model. This is borne out by the results of a further simulation experiment with an increased number of subjects in each set of data. The difference in the interpretation of the parameters of the fixed and mixed effects models is discussed. It is demonstrated that the mixed effects model and parameter estimates can be used to estimate the parameters of the fixed effects model but not vice versa.

Predictors of postretention stability of mandibular dental arch dimensions in patients treated with a lip bumper during mixed dentition followed by fixed appliances.

PubMed

Raucci, Gaetana; Pachêco-Pereira, Camila; Elyasi, Maryam; d'Apuzzo, Fabrizia; Flores-Mir, Carlos; Perillo, Letizia

2017-03-01

To identify which dental and/or cephalometric variables were predictors of postretention mandibular dental arch stability in patients who underwent treatment with transpalatal arch and lip bumper during mixed dentition followed by full fixed appliances in the permanent dentition. Thirty-one patients were divided into stable and relapse groups based on the postretention presence or absence of relapse. Intercuspid, interpremolar, and intermolar widths; arch length and perimeter; crowding; and lower incisor proclination were evaluated before treatment (T0), after lip bumper treatment (T1), after fixed appliance treatment (T2), and a minimum of 3 years after removal of the full fixed appliance (T3). Logistic regression analyses were performed to evaluate the effect of changes between T0 and T1, as predictive variables, on the occurrence of relapse at T3. The model explained 53.5 % of the variance in treatment stability and correctly classified 80.6 % of the sample. Of the seven prediction variables, intermolar and interpremolar changes between T0 and T1 (P = .024 and P = .034, respectively) were statistically significant. For every millimeter of increase in intermolar and interpremolar widths there was a 1.52 and 2.70 times increase, respectively, in the odds of having stability. There was also weak evidence for the effect of sex (P = .047). The best predictors of an average 4-year postretention mandibular dental arch stability after treatment with a lip bumper followed by full fixed appliances were intermolar and interpremolar width increases during lip bumper therapy. The amount of relapse in this crowding could be considered clinically irrelevant.

Taste clusters of music and drugs: evidence from three analytic levels.

PubMed

Vuolo, Mike; Uggen, Christopher; Lageson, Sarah

2014-09-01

This article examines taste clusters of musical preferences and substance use among adolescents and young adults. Three analytic levels are considered: fixed effects analyses of aggregate listening patterns and substance use in US radio markets, logistic regressions of individual genre preferences and drug use from a nationally representative survey of US youth, and arrest and seizure data from a large American concert venue. A consistent picture emerges from all three levels: rock music is positively associated with substance use, with some substance-specific variability across rock sub-genres. Hip hop music is also associated with higher use, while pop and religious music are associated with lower use. These results are robust to fixed effects models that account for changes over time in radio markets, a comprehensive battery of controls in the individual-level survey, and concert data establishing the co-occurrence of substance use and music listening in the same place and time. The results affirm a rich tradition of qualitative and experimental studies, demonstrating how symbolic boundaries are simultaneously drawn around music and drugs. © London School of Economics and Political Science 2014.

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

Gingival enlargement in orthodontic patients: Effect of treatment duration.

PubMed

Pinto, Alice Souza; Alves, Luana Severo; Zenkner, Júlio Eduardo do Amaral; Zanatta, Fabrício Batistin; Maltz, Marisa

2017-10-01

In this study, we aimed to assess the effect of the duration of fixed orthodontic treatment on gingival enlargement (GE) in adolescents and young adults. The sample consisted of 260 subjects (ages, 10-30 years) divided into 4 groups: patients with no fixed orthodontic appliances (G0) and patients undergoing orthodontic treatment for 1 year (G1), 2 years (G2), or 3 years (G3). Participants completed a structured questionnaire on sociodemographic characteristics and oral hygiene habits. Clinical examinations were conducted by a calibrated examiner and included the plaque index, the gingival index, and the Seymour index. Poisson regression models were used to assess the association between group and GE. We observed increasing means of plaque, gingivitis, and GE in G0, G1, and G2. No significant differences were observed between G2 and G3. Adjusted Poisson regression analysis showed that patients undergoing orthodontic treatment had a 20 to 28-fold increased risk for GE than did those without orthodontic appliances (G1, rate ratio [RR] = 20.2, 95% CI = 9.0-45.3; G2, RR = 27.0, 95% CI = 12.1-60.3; G3 = 28.1; 95% CI = 12.6-62.5). The duration of orthodontic treatment significantly influenced the occurrence of GE. Oral hygiene instructions and motivational activities should target adolescents and young adults undergoing orthodontic treatment. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Association of Birth Order with Cardiovascular Disease Risk Factors in Young Adulthood: A Study of One Million Swedish Men

PubMed Central

Jelenkovic, Aline; Silventoinen, Karri; Tynelius, Per; Myrskylä, Mikko; Rasmussen, Finn

2013-01-01

Background Birth order has been suggested to be linked to several cardiovascular disease (CVD) risk factors, but the evidence is still inconsistent. We aim to determine the associations of birth order with body mass index (BMI), muscle strength and blood pressure. Further we will analyse whether these relationships are affected by family characteristics. Methods BMI, elbow flexion, hand grip and knee extension strength and systolic and diastolic blood pressure were measured at conscription examination in 1 065 710 Swedish young men born between 1951 and 1975. The data were analysed using linear multivariate and fixed effects regression models; the latter compare siblings and account for genetic and social factors shared by brothers. Results Fixed effect regression analysis showed that birth order was inversely associated with BMI: second and third born had 0.8% and 1.1% (p<0.001) lower BMI than first-born, respectively. The association pattern differed among muscle strengths. After adjustment for BMI, first-born presented lower elbow flexion and hand grip strength than second-born (−5.9 N and −3.8 N, respectively, p<0.001). Knee extension strength was inversely related to birth order though not always significantly. The association between birth order and blood pressure was not significant. Conclusions Birth order is negatively associated with BMI and knee extension strength, positively with elbow flexion and hand grip strength, and is not associated with blood pressure among young men. Although the effects are small, the link between birth order and some CVD risk factors is already detectable in young adulthood. PMID:23696817

Association of birth order with cardiovascular disease risk factors in young adulthood: a study of one million Swedish men.

PubMed

Jelenkovic, Aline; Silventoinen, Karri; Tynelius, Per; Myrskylä, Mikko; Rasmussen, Finn

2013-01-01

Birth order has been suggested to be linked to several cardiovascular disease (CVD) risk factors, but the evidence is still inconsistent. We aim to determine the associations of birth order with body mass index (BMI), muscle strength and blood pressure. Further we will analyse whether these relationships are affected by family characteristics. BMI, elbow flexion, hand grip and knee extension strength and systolic and diastolic blood pressure were measured at conscription examination in 1,065,710 Swedish young men born between 1951 and 1975. The data were analysed using linear multivariate and fixed effects regression models; the latter compare siblings and account for genetic and social factors shared by brothers. Fixed effect regression analysis showed that birth order was inversely associated with BMI: second and third born had 0.8% and 1.1% (p<0.001) lower BMI than first-born, respectively. The association pattern differed among muscle strengths. After adjustment for BMI, first-born presented lower elbow flexion and hand grip strength than second-born (-5.9 N and -3.8 N, respectively, p<0.001). Knee extension strength was inversely related to birth order though not always significantly. The association between birth order and blood pressure was not significant. Birth order is negatively associated with BMI and knee extension strength, positively with elbow flexion and hand grip strength, and is not associated with blood pressure among young men. Although the effects are small, the link between birth order and some CVD risk factors is already detectable in young adulthood.

Influence of optic disc size on the diagnostic performance of macular ganglion cell complex and peripapillary retinal nerve fiber layer analyses in glaucoma.

PubMed

Cordeiro, Daniela Valença; Lima, Verônica Castro; Castro, Dinorah P; Castro, Leonardo C; Pacheco, Maria Angélica; Lee, Jae Min; Dimantas, Marcelo I; Prata, Tiago Santos

2011-01-01

To evaluate the influence of optic disc size on the diagnostic accuracy of macular ganglion cell complex (GCC) and conventional peripapillary retinal nerve fiber layer (pRNFL) analyses provided by spectral domain optical coherence tomography (SD-OCT) in glaucoma. Eighty-two glaucoma patients and 30 healthy subjects were included. All patients underwent GCC (7 × 7 mm macular grid, consisting of RNFL, ganglion cell and inner plexiform layers) and pRNFL thickness measurement (3.45 mm circular scan) by SD-OCT. One eye was randomly selected for analysis. Initially, receiver operating characteristic (ROC) curves were generated for different GCC and pRNFL parameters. The effect of disc area on the diagnostic accuracy of these parameters was evaluated using a logistic ROC regression model. Subsequently, 1.5, 2.0, and 2.5 mm(2) disc sizes were arbitrarily chosen (based on data distribution) and the predicted areas under the ROC curves (AUCs) and sensitivities were compared at fixed specificities for each. Average mean deviation index for glaucomatous eyes was -5.3 ± 5.2 dB. Similar AUCs were found for the best pRNFL (average thickness = 0.872) and GCC parameters (average thickness = 0.824; P = 0.19). The coefficient representing disc area in the ROC regression model was not statistically significant for average pRNFL thickness (-0.176) or average GCC thickness (0.088; P ≥ 0.56). AUCs for fixed disc areas (1.5, 2.0, and 2.5 mm(2)) were 0.904, 0.891, and 0.875 for average pRNFL thickness and 0.834, 0.842, and 0.851 for average GCC thickness, respectively. The highest sensitivities - at 80% specificity for average pRNFL (84.5%) and GCC thicknesses (74.5%) - were found with disc sizes fixed at 1.5 mm(2) and 2.5 mm(2). Diagnostic accuracy was similar between pRNFL and GCC thickness parameters. Although not statistically significant, there was a trend for a better diagnostic accuracy of pRNFL thickness measurement in cases of smaller discs. For GCC analysis, an inverse effect was observed.

Carbon dioxide stripping in aquaculture -- part III: model verification

USGS Publications Warehouse

Colt, John; Watten, Barnaby; Pfeiffer, Tim

2012-01-01

Based on conventional mass transfer models developed for oxygen, the use of the non-linear ASCE method, 2-point method, and one parameter linear-regression method were evaluated for carbon dioxide stripping data. For values of KLaCO2 < approximately 1.5/h, the 2-point or ASCE method are a good fit to experimental data, but the fit breaks down at higher values of KLaCO2. How to correct KLaCO2 for gas phase enrichment remains to be determined. The one-parameter linear regression model was used to vary the C*CO2 over the test, but it did not result in a better fit to the experimental data when compared to the ASCE or fixed C*CO2 assumptions.

Methods for selecting fixed-effect models for heterogeneous codon evolution, with comments on their application to gene and genome data.

PubMed

Bao, Le; Gu, Hong; Dunn, Katherine A; Bielawski, Joseph P

2007-02-08

Models of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a gene's functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial. In this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria). Fixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori knowledge for partitioning sites. We recommend: (i) selection of models by using backward elimination rather than AIC or AICc, (ii) use a stringent cut-off, e.g., p = 0.0001, and (iii) conduct sensitivity analysis of results. With thoughtful application, fixed-effect codon models should provide a useful tool for large scale multi-gene analyses.

A Modified LS+AR Model to Improve the Accuracy of the Short-term Polar Motion Prediction

NASA Astrophysics Data System (ADS)

Wang, Z. W.; Wang, Q. X.; Ding, Y. Q.; Zhang, J. J.; Liu, S. S.

2017-03-01

There are two problems of the LS (Least Squares)+AR (AutoRegressive) model in polar motion forecast: the inner residual value of LS fitting is reasonable, but the residual value of LS extrapolation is poor; and the LS fitting residual sequence is non-linear. It is unsuitable to establish an AR model for the residual sequence to be forecasted, based on the residual sequence before forecast epoch. In this paper, we make solution to those two problems with two steps. First, restrictions are added to the two endpoints of LS fitting data to fix them on the LS fitting curve. Therefore, the fitting values next to the two endpoints are very close to the observation values. Secondly, we select the interpolation residual sequence of an inward LS fitting curve, which has a similar variation trend as the LS extrapolation residual sequence, as the modeling object of AR for the residual forecast. Calculation examples show that this solution can effectively improve the short-term polar motion prediction accuracy by the LS+AR model. In addition, the comparison results of the forecast models of RLS (Robustified Least Squares)+AR, RLS+ARIMA (AutoRegressive Integrated Moving Average), and LS+ANN (Artificial Neural Network) confirm the feasibility and effectiveness of the solution for the polar motion forecast. The results, especially for the polar motion forecast in the 1-10 days, show that the forecast accuracy of the proposed model can reach the world level.

Essays in energy economics: The electricity industry

NASA Astrophysics Data System (ADS)

Martinez-Chombo, Eduardo

Electricity demand analysis using cointegration and error-correction models with time varying parameters: The Mexican case. In this essay we show how some flexibility can be allowed in modeling the parameters of the electricity demand function by employing the time varying coefficient (TVC) cointegrating model developed by Park and Hahn (1999). With the income elasticity of electricity demand modeled as a TVC, we perform tests to examine the adequacy of the proposed model against the cointegrating regression with fixed coefficients, as well as against the spuriousness of the regression with TVC. The results reject the specification of the model with fixed coefficients and favor the proposed model. We also show how some flexibility is gained in the specification of the error correction model based on the proposed TVC cointegrating model, by including more lags of the error correction term as predetermined variables. Finally, we present the results of some out-of-sample forecast comparison among competing models. Electricity demand and supply in Mexico. In this essay we present a simplified model of the Mexican electricity transmission network. We use the model to approximate the marginal cost of supplying electricity to consumers in different locations and at different times of the year. We examine how costs and system operations will be affected by proposed investments in generation and transmission capacity given a forecast of growth in regional electricity demands. Decomposing electricity prices with jumps. In this essay we propose a model that decomposes electricity prices into two independent stochastic processes: one that represents the "normal" pattern of electricity prices and the other that captures temporary shocks, or "jumps", with non-lasting effects in the market. Each contains specific mean reverting parameters to estimate. In order to identify such components we specify a state-space model with regime switching. Using Kim's (1994) filtering algorithm we estimate the parameters of the model, the transition probabilities and the unobservable components for the mean adjusted series of New South Wales' electricity prices. Finally, bootstrap simulations were performed to estimate the expected contribution of each of the components in the overall electricity prices.

“Smooth” Semiparametric Regression Analysis for Arbitrarily Censored Time-to-Event Data

PubMed Central

Zhang, Min; Davidian, Marie

2008-01-01

Summary A general framework for regression analysis of time-to-event data subject to arbitrary patterns of censoring is proposed. The approach is relevant when the analyst is willing to assume that distributions governing model components that are ordinarily left unspecified in popular semiparametric regression models, such as the baseline hazard function in the proportional hazards model, have densities satisfying mild “smoothness” conditions. Densities are approximated by a truncated series expansion that, for fixed degree of truncation, results in a “parametric” representation, which makes likelihood-based inference coupled with adaptive choice of the degree of truncation, and hence flexibility of the model, computationally and conceptually straightforward with data subject to any pattern of censoring. The formulation allows popular models, such as the proportional hazards, proportional odds, and accelerated failure time models, to be placed in a common framework; provides a principled basis for choosing among them; and renders useful extensions of the models straightforward. The utility and performance of the methods are demonstrated via simulations and by application to data from time-to-event studies. PMID:17970813

Father absence due to migration and child illness in rural Mexico.

PubMed

Schmeer, Kammi

2009-10-01

Little research to date has assessed the importance of the presence of fathers in the household for protecting child health, particularly in developing country contexts. Although divorce and non-marital childbearing are low in many developing countries, migration is a potentially important source of father absence that has yet to be studied in relation to child health. This study utilizes prospective, longitudinal data from Mexico to assess whether father absence due to migration is associated with increased child illness in poor, rural communities. Rural Mexico provides a setting where child illness is related to more serious health problems, and where migration is an important source of father absence. Both state- and individual-level fixed effects regression analyses are used to estimate the relationship between father absence due to migration and child illness while controlling for unobserved contextual and individual characteristics. The state-level models illustrate that the odds of children being ill are 39% higher for any illness and 51% higher for diarrhea when fathers are absent compared with when fathers are present in the household. The individual-level fixed effects models support these findings, indicating that, in the context of rural Mexico, fathers may be important sources of support for ensuring the healthy development of young children.

Association between economic fluctuations and road mortality in OECD countries.

PubMed

Chen, Gang

2014-08-01

Using longitudinal data from 32 Organization for Economic Co-operation and Development (OECD) countries (1970-2010), this article investigates association between annual variations in road mortality and the economic fluctuations. Two regression models (fixed-effects and random-coefficients) were adopted for estimation. The cross-country data analyses suggested that road mortality is pro-cyclical and that the cyclicality is symmetric. Based on data from 32 OECD countries, an increase of on average 1% in economic growth is associated with a 1.1% increase in road mortality, and vice versa. © The Author 2014. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Genetic variance in micro-environmental sensitivity for milk and milk quality in Walloon Holstein cattle.

PubMed

Vandenplas, J; Bastin, C; Gengler, N; Mulder, H A

2013-09-01

Animals that are robust to environmental changes are desirable in the current dairy industry. Genetic differences in micro-environmental sensitivity can be studied through heterogeneity of residual variance between animals. However, residual variance between animals is usually assumed to be homogeneous in traditional genetic evaluations. The aim of this study was to investigate genetic heterogeneity of residual variance by estimating variance components in residual variance for milk yield, somatic cell score, contents in milk (g/dL) of 2 groups of milk fatty acids (i.e., saturated and unsaturated fatty acids), and the content in milk of one individual fatty acid (i.e., oleic acid, C18:1 cis-9), for first-parity Holstein cows in the Walloon Region of Belgium. A total of 146,027 test-day records from 26,887 cows in 747 herds were available. All cows had at least 3 records and a known sire. These sires had at least 10 cows with records and each herd × test-day had at least 5 cows. The 5 traits were analyzed separately based on fixed lactation curve and random regression test-day models for the mean. Estimation of variance components was performed by running iteratively expectation maximization-REML algorithm by the implementation of double hierarchical generalized linear models. Based on fixed lactation curve test-day mean models, heritability for residual variances ranged between 1.01×10(-3) and 4.17×10(-3) for all traits. The genetic standard deviation in residual variance (i.e., approximately the genetic coefficient of variation of residual variance) ranged between 0.12 and 0.17. Therefore, some genetic variance in micro-environmental sensitivity existed in the Walloon Holstein dairy cattle for the 5 studied traits. The standard deviations due to herd × test-day and permanent environment in residual variance ranged between 0.36 and 0.45 for herd × test-day effect and between 0.55 and 0.97 for permanent environmental effect. Therefore, nongenetic effects also contributed substantially to micro-environmental sensitivity. Addition of random regressions to the mean model did not reduce heterogeneity in residual variance and that genetic heterogeneity of residual variance was not simply an effect of an incomplete mean model. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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.

A primer for biomedical scientists on how to execute model II linear regression analysis.

PubMed

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

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

PubMed

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

2011-10-31

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

Efficacy of a new sealant to prevent white spot lesions during fixed orthodontic treatment : A 12-month, single-center, randomized controlled clinical trial.

PubMed

Hammad, Shaza M; Knösel, Michael

2016-11-01

White spot lesions (WSLs) are an undesirable side effect of fixed orthodontic appliance therapy and are reported to occur in 2-96 % of orthodontic patients. In this study, the efficacy of a new sealant to prevent WSLs during fixed orthodontic treatment was compared to a control group that did not receive sealant. For this 2-arm parallel-group randomized trial, 50 subjects aged 12-18 years (mean age 14.57 ± 2.04 years) were recruited from the orthodontics department at Mansoura University, Egypt. Eligibility criteria were no restorations, no active WSLs or caries, and adequate oral hygiene. Subjects were randomized in a 1:1 ratio to one of the two arms prior to undergoing fixed orthodontic treatment, namely a single application of SeLECT Defense™ sealant during the bracketing appointment or no sealant (control arm). Instructions and dentifrices for local home fluoridation regimen were identical in both groups. Oral hygiene was assessed using the Approximal Plaque Index (API) at specified time intervals. Dental photographs were taken for blinded WSLs assessment; inter- and intra-operator error were also calculated. Categorical data were tested using the χ 2 test, and a logistic regression model was adopted to detect associations between decalcification (WSLs), sealant application, and oral hygiene status. Only excellent or good oral hygiene were independent prognostic factors for preventing severe WSLs (p = 0.035). No significant effect on caries incidence was observed for the sealant. In combination with adequate oral hygiene SeLECT Defense™ helps to reduced the frequency of WSLs. However, the sealat showed no significant effect as sole preventive strategy.

Meta-analysis of the effect of road safety campaigns on accidents.

PubMed

Phillips, Ross Owen; Ulleberg, Pål; Vaa, Truls

2011-05-01

A meta-analysis of 67 studies evaluating the effect of road safety campaigns on accidents is reported. A total of 119 results were extracted from the studies, which were reported in 12 different countries between 1975 and 2007. After allowing for publication bias and heterogeneity of effects, the weighted average effect of road safety campaigns is a 9% reduction in accidents (with 95% confidence that the weighted average is between -12 and -6%). To account for the variability of effects measured across studies, data were collected to characterise aspects of the campaign and evaluation design associated with each effect, and analysed to identify a model of seven campaign factors for testing by meta-regression. The model was tested using both fixed and random effect meta-regression, and dependency among effects was accounted for by aggregation. These analyses suggest positive associations between accident reduction and the use of personal communication or roadside media as part of a campaign delivery strategy. Campaigns with a drink-driving theme were also associated with greater accident reductions, while some of the analyses suggested that accompanying enforcement and short campaign duration (less than one month) are beneficial. Overall the results are consistent with the idea that campaigns can be more effective in the short term if the message is delivered with personal communication in a way that is proximal in space and time to the behaviour targeted by the campaign. Copyright © 2011 Elsevier Ltd. All rights reserved.

Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides.

PubMed

Brandt, Patrick T; Kovandzic, Tomislav V

2015-01-01

Executions in Texas from 1994-2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models--based on pre-tests for unit roots that correct for earlier model misspecifications--one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention.

Income and Child Maltreatment in Unmarried Families: Evidence from the Earned Income Tax Credit.

PubMed

Berger, Lawrence M; Font, Sarah A; Slack, Kristen S; Waldfogel, Jane

2017-12-01

This study estimates the associations of income with both (self-reported) child protective services (CPS) involvement and parenting behaviors that proxy for child abuse and neglect risk among unmarried families. Our primary strategy follows the instrumental variables (IV) approach employed by Dahl and Lochner (2012), which leverages variation between states and over time in the generosity of the total state and federal Earned Income Tax Credit for which a family is eligible to identify exogenous variation in family income. As a robustness check, we also estimate standard OLS regressions (linear probability models), reduced form OLS regressions, and OLS regressions with the inclusion of a control function (each with and without family-specific fixed effects). Our micro-level data are drawn from the Fragile Families and Child Wellbeing Study, a longitudinal birth-cohort of relatively disadvantaged urban children who have been followed from birth to age nine. Results suggest that an exogenous increase in income is associated with reductions in behaviorally-approximated child neglect and CPS involvement, particularly among low-income single-mother families.

The relationship between air pollution, fossil fuel energy consumption, and water resources in the panel of selected Asia-Pacific countries.

PubMed

Rafindadi, Abdulkadir Abdulrashid; Yusof, Zarinah; Zaman, Khalid; Kyophilavong, Phouphet; Akhmat, Ghulam

2014-10-01

The objective of the study is to examine the relationship between air pollution, fossil fuel energy consumption, water resources, and natural resource rents in the panel of selected Asia-Pacific countries, over a period of 1975-2012. The study includes number of variables in the model for robust analysis. The results of cross-sectional analysis show that there is a significant relationship between air pollution, energy consumption, and water productivity in the individual countries of Asia-Pacific. However, the results of each country vary according to the time invariant shocks. For this purpose, the study employed the panel least square technique which includes the panel least square regression, panel fixed effect regression, and panel two-stage least square regression. In general, all the panel tests indicate that there is a significant and positive relationship between air pollution, energy consumption, and water resources in the region. The fossil fuel energy consumption has a major dominating impact on the changes in the air pollution in the region.

Adolescent Work and Alcohol Use Revisited: Variations by Family Structure

ERIC Educational Resources Information Center

Rocheleau, Gregory C.; Swisher, Raymond R.

2012-01-01

Previous research finds adolescent work hours to be associated with increased alcohol use. Most studies, however, fail to account for possible selection effects that lead youth to both work and substance use. Using data from the first two waves of the National Longitudinal Study of Adolescent Health (N = 12,620), a fixed effects regression method…

Still the Favorite? Parents' Differential Treatment of Siblings Entering Young Adulthood.

PubMed

Siennick, Sonja E

2013-08-01

This study examined within-family stability in parents' differential treatment of siblings from adolescence to young adulthood and the effect of differential treatment in young adulthood on grown siblings' relationship quality. The author used longitudinal data on parent - child and sibling relations from the sibling sample of the National Longitudinal Study of Adolescent Health ( N = 1,470 sibling dyads). Within-dyad fixed effects regression models revealed that the adolescent sibling who was closer to parents went on to be the young adult sibling who was closer to and received more material support from parents. Results from an actor - partner interdependence model revealed that differential parental financial assistance of young adult siblings predicted worse sibling relationship quality. These findings demonstrate the lasting importance of affect between parents and offspring earlier in the family life course and the relevance of within-family inequalities for understanding family relations.

Still the Favorite? Parents’ Differential Treatment of Siblings Entering Young Adulthood

PubMed Central

Siennick, Sonja E.

2013-01-01

This study examined within-family stability in parents’ differential treatment of siblings from adolescence to young adulthood and the effect of differential treatment in young adulthood on grown siblings’ relationship quality. The author used longitudinal data on parent – child and sibling relations from the sibling sample of the National Longitudinal Study of Adolescent Health (N = 1,470 sibling dyads). Within-dyad fixed effects regression models revealed that the adolescent sibling who was closer to parents went on to be the young adult sibling who was closer to and received more material support from parents. Results from an actor – partner interdependence model revealed that differential parental financial assistance of young adult siblings predicted worse sibling relationship quality. These findings demonstrate the lasting importance of affect between parents and offspring earlier in the family life course and the relevance of within-family inequalities for understanding family relations. PMID:24244050

A framework for longitudinal data analysis via shape regression

NASA Astrophysics Data System (ADS)

Fishbaugh, James; Durrleman, Stanley; Piven, Joseph; Gerig, Guido

2012-02-01

Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.

Can the provision of a home help service for the elderly population reduce the incidence of fall-related injuries? A quasi-experimental study of the community-level effects on hospital admissions in Swedish municipalities.

PubMed

Bonander, Carl; Gustavsson, Johanna; Nilson, Finn

2016-12-01

Fall-related injuries are a global public health problem, especially in elderly populations. The effect of an intervention aimed at reducing the risk of falls in the homes of community-dwelling elderly persons was evaluated. The intervention mainly involves the performance of complicated tasks and hazards assessment by a trained assessor, and has been adopted gradually over the last decade by 191 of 290 Swedish municipalities. A quasi-experimental design was used where intention-to-treat effect estimates were derived using panel regression analysis and a regression discontinuity (RD) design. The outcome measure was the incidence of fall-related hospitalisations in the treatment population, the age of which varied by municipality (≥65 years, ≥67 years, ≥70 years or ≥75 years). We found no statistically significant reductions in injury incidence in the panel regression (IRR 1.01 (95% CI 0.98 to 1.05)) or RD (IRR 1.00 (95% CI 0.97 to 1.03)) analyses. The results are robust to several different model specifications, including segmented panel regression analysis with linear trend change and community fixed effects parameters. It is unclear whether the absence of an effect is due to a low efficacy of the services provided, or a result of low adherence. Additional studies of the effects on other quality-of-life measures are recommended before conclusions are drawn regarding the cost-effectiveness of the provision of home help service programmes. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Exploring unobserved heterogeneity in bicyclists' red-light running behaviors at different crossing facilities.

PubMed

Guo, Yanyong; Li, Zhibin; Wu, Yao; Xu, Chengcheng

2018-06-01

Bicyclists running the red light at crossing facilities increase the potential of colliding with motor vehicles. Exploring the contributing factors could improve the prediction of running red-light probability and develop countermeasures to reduce such behaviors. However, individuals could have unobserved heterogeneities in running a red light, which make the accurate prediction more challenging. Traditional models assume that factor parameters are fixed and cannot capture the varying impacts on red-light running behaviors. In this study, we employed the full Bayesian random parameters logistic regression approach to account for the unobserved heterogeneous effects. Two types of crossing facilities were considered which were the signalized intersection crosswalks and the road segment crosswalks. Electric and conventional bikes were distinguished in the modeling. Data were collected from 16 crosswalks in urban area of Nanjing, China. Factors such as individual characteristics, road geometric design, environmental features, and traffic variables were examined. Model comparison indicates that the full Bayesian random parameters logistic regression approach is statistically superior to the standard logistic regression model. More red-light runners are predicted at signalized intersection crosswalks than at road segment crosswalks. Factors affecting red-light running behaviors are gender, age, bike type, road width, presence of raised median, separation width, signal type, green ratio, bike and vehicle volume, and average vehicle speed. Factors associated with the unobserved heterogeneity are gender, bike type, signal type, separation width, and bike volume. Copyright © 2018 Elsevier Ltd. All rights reserved.

The effect of hospital mergers on long-term sickness absence among hospital employees: a fixed effects multivariate regression analysis using panel data.

PubMed

Kjekshus, Lars Erik; Bernstrøm, Vilde Hoff; Dahl, Espen; Lorentzen, Thomas

2014-02-03

Hospitals are merging to become more cost-effective. Mergers are often complex and difficult processes with variable outcomes. The aim of this study was to analyze the effect of mergers on long-term sickness absence among hospital employees. Long-term sickness absence was analyzed among hospital employees (N = 107 209) in 57 hospitals involved in 23 mergers in Norway between 2000 and 2009. Variation in long-term sickness absence was explained through a fixed effects multivariate regression analysis using panel data with years-since-merger as the independent variable. We found a significant but modest effect of mergers on long-term sickness absence in the year of the merger, and in years 2, 3 and 4; analyzed by gender there was a significant effect for women, also for these years, but only in year 4 for men. However, men are less represented among the hospital workforce; this could explain the lack of significance. Mergers has a significant effect on employee health that should be taken into consideration when deciding to merge hospitals. This study illustrates the importance of analyzing the effects of mergers over several years and the need for more detailed analyses of merger processes and of the changes that may occur as a result of such mergers.

Imaging Determinants of Clinical Effectiveness of Lumbar Transforaminal Epidural Steroid Injections.

PubMed

Maus, Timothy P; El-Yahchouchi, Christine A; Geske, Jennifer R; Carter, Rickey E; Kaufmann, Timothy J; Wald, John T; Diehn, Felix E

2016-12-01

To examine associations between imaging characteristics of compressive lesions and patient outcomes after lumbar transforaminal epidural steroid injections (TFESIs) stratified by steroid formulation (solution versus suspension). Retrospective observational study, academic radiology practice. A 516-patient sample was selected from 2,634 consecutive patients receiving lumbar TFESI for radicular pain. The advanced imaging study(s) preceding sampled TFESI were reviewed. Compressive lesions were described by a) nature of the lesion [disc herniation, fixed stenosis, synovial cyst, epidural fibrosis, no lesion] b) degree of neural compression [4 part scale], and c) presence of a tandem lesion. Associations between 2-month categorical outcomes (responder rates for pain, functional recovery) and imaging characteristics, stratified by steroid formulation, were examined with chi-squared tests of categorical outcomes and multivariable logistic regression models. Disc herniation patients had more responders for functional recovery than patients with fixed lesions (54% versus 38%, P = 0.01). Patients with fixed lesions receiving steroid solution (dexamethasone) had more responders for pain relief, with a similar trend for functional recovery, than patients receiving suspensions (59% versus 40%, P = 0.01). Outcomes for patients with fixed lesions treated with dexamethasone were not statistically different from those for disc herniation patients. Patients with single compressive lesions had more responders than those with tandem lesions (55% versus 41%, P = 0.03). In the entire sample, outcomes for disc herniations were more favorable than for fixed lesions. However, fixed lesions treated with dexamethasone had outcomes indistinguishable from disc herniations. Single lesions had better outcomes than tandem lesions. © 2016 American Academy of Pain Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

PubMed

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

2017-11-02

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

The Impact of Life Events on Job Satisfaction

ERIC Educational Resources Information Center

Georgellis, Yannis; Lange, Thomas; Tabvuma, Vurain

2012-01-01

Employing fixed effects regression techniques on longitudinal data, we investigate how life events affect employees' job satisfaction. Unlike previous work-life research, exploring mostly contemporaneous correlations, we look for evidence of adaptation in the years following major life events. We find evidence of adaptation following the first…

Evaluation of Denoising Strategies to Address Motion-Correlated Artifacts in Resting-State Functional Magnetic Resonance Imaging Data from the Human Connectome Project

PubMed Central

Kandala, Sridhar; Nolan, Dan; Laumann, Timothy O.; Power, Jonathan D.; Adeyemo, Babatunde; Harms, Michael P.; Petersen, Steven E.; Barch, Deanna M.

2016-01-01

Abstract Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR. PMID:27571276

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.

Distributed Monitoring of the R(sup 2) Statistic for Linear Regression

NASA Technical Reports Server (NTRS)

Bhaduri, Kanishka; Das, Kamalika; Giannella, Chris R.

2011-01-01

The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more dependent target variables. This problem becomes challenging for large scale data in a distributed computing environment when only a subset of instances is available at individual nodes and the local data changes frequently. Data centralization and periodic model recomputation can add high overhead to tasks like anomaly detection in such dynamic settings. Therefore, the goal is to develop techniques for monitoring and updating the model over the union of all nodes data in a communication-efficient fashion. Correctness guarantees on such techniques are also often highly desirable, especially in safety-critical application scenarios. In this paper we develop DReMo a distributed algorithm with very low resource overhead, for monitoring the quality of a regression model in terms of its coefficient of determination (R2 statistic). When the nodes collectively determine that R2 has dropped below a fixed threshold, the linear regression model is recomputed via a network-wide convergecast and the updated model is broadcast back to all nodes. We show empirically, using both synthetic and real data, that our proposed method is highly communication-efficient and scalable, and also provide theoretical guarantees on correctness.

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

PubMed

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

2016-10-15

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

Flexible link functions in nonparametric binary regression with Gaussian process priors.

PubMed

Li, Dan; Wang, Xia; Lin, Lizhen; Dey, Dipak K

2016-09-01

In many scientific fields, it is a common practice to collect a sequence of 0-1 binary responses from a subject across time, space, or a collection of covariates. Researchers are interested in finding out how the expected binary outcome is related to covariates, and aim at better prediction in the future 0-1 outcomes. Gaussian processes have been widely used to model nonlinear systems; in particular to model the latent structure in a binary regression model allowing nonlinear functional relationship between covariates and the expectation of binary outcomes. A critical issue in modeling binary response data is the appropriate choice of link functions. Commonly adopted link functions such as probit or logit links have fixed skewness and lack the flexibility to allow the data to determine the degree of the skewness. To address this limitation, we propose a flexible binary regression model which combines a generalized extreme value link function with a Gaussian process prior on the latent structure. Bayesian computation is employed in model estimation. Posterior consistency of the resulting posterior distribution is demonstrated. The flexibility and gains of the proposed model are illustrated through detailed simulation studies and two real data examples. Empirical results show that the proposed model outperforms a set of alternative models, which only have either a Gaussian process prior on the latent regression function or a Dirichlet prior on the link function. © 2015, The International Biometric Society.

Flexible Link Functions in Nonparametric Binary Regression with Gaussian Process Priors

PubMed Central

Li, Dan; Lin, Lizhen; Dey, Dipak K.

2015-01-01

Summary In many scientific fields, it is a common practice to collect a sequence of 0-1 binary responses from a subject across time, space, or a collection of covariates. Researchers are interested in finding out how the expected binary outcome is related to covariates, and aim at better prediction in the future 0-1 outcomes. Gaussian processes have been widely used to model nonlinear systems; in particular to model the latent structure in a binary regression model allowing nonlinear functional relationship between covariates and the expectation of binary outcomes. A critical issue in modeling binary response data is the appropriate choice of link functions. Commonly adopted link functions such as probit or logit links have fixed skewness and lack the flexibility to allow the data to determine the degree of the skewness. To address this limitation, we propose a flexible binary regression model which combines a generalized extreme value link function with a Gaussian process prior on the latent structure. Bayesian computation is employed in model estimation. Posterior consistency of the resulting posterior distribution is demonstrated. The flexibility and gains of the proposed model are illustrated through detailed simulation studies and two real data examples. Empirical results show that the proposed model outperforms a set of alternative models, which only have either a Gaussian process prior on the latent regression function or a Dirichlet prior on the link function. PMID:26686333

Building a Decision Support System for Inpatient Admission Prediction With the Manchester Triage System and Administrative Check-in Variables.

PubMed

Zlotnik, Alexander; Alfaro, Miguel Cuchí; Pérez, María Carmen Pérez; Gallardo-Antolín, Ascensión; Martínez, Juan Manuel Montero

2016-05-01

The usage of decision support tools in emergency departments, based on predictive models, capable of estimating the probability of admission for patients in the emergency department may give nursing staff the possibility of allocating resources in advance. We present a methodology for developing and building one such system for a large specialized care hospital using a logistic regression and an artificial neural network model using nine routinely collected variables available right at the end of the triage process.A database of 255.668 triaged nonobstetric emergency department presentations from the Ramon y Cajal University Hospital of Madrid, from January 2011 to December 2012, was used to develop and test the models, with 66% of the data used for derivation and 34% for validation, with an ordered nonrandom partition. On the validation dataset areas under the receiver operating characteristic curve were 0.8568 (95% confidence interval, 0.8508-0.8583) for the logistic regression model and 0.8575 (95% confidence interval, 0.8540-0. 8610) for the artificial neural network model. χ Values for Hosmer-Lemeshow fixed "deciles of risk" were 65.32 for the logistic regression model and 17.28 for the artificial neural network model. A nomogram was generated upon the logistic regression model and an automated software decision support system with a Web interface was built based on the artificial neural network model.

Aerodynamic braking of high speed ground transportation vehicles.

NASA Technical Reports Server (NTRS)

Marte, J. E.; Marko, W. J.

1973-01-01

The drag effectiveness of aerodynamic brakes arranged in series on a train-like vehicle was investigated. Fixed- and moving-model testing techniques were used in order to determine the importance of proper vehicle-ground interference simulation. Fixed-model tests were carried out on a sting-mounted model: alone; with a fixed ground plane; and in proximity to an image model. Moving-model tests were conducted in a vertical slide-wire facility with and without a ground plane. Results from investigations of one brake configuration are presented which show the effect of the number of brakes in the set and of spacing between brakes.

Evaluation of Invisalign treatment effectiveness and efficiency compared with conventional fixed appliances using the Peer Assessment Rating index.

PubMed

Gu, Jiafeng; Tang, Jack Shengyu; Skulski, Brennan; Fields, Henry W; Beck, F Michael; Firestone, Allen R; Kim, Do-Gyoon; Deguchi, Toru

2017-02-01

The purpose of this retrospective case-control study was to compare the treatment effectiveness and efficiency of the Invisalign system with conventional fixed appliances in treating orthodontic patients with mild to moderate malocclusion in a graduate orthodontic clinic. Using the peer assessment rating (PAR) index, we evaluated pretreatment and posttreatment records of 48 Invisalign patients and 48 fixed appliances patients. The 2 groups of patients were controlled for general characteristics and initial severity of malocclusion. We analyzed treatment outcome, duration, and improvement between the Invisalign and fixed appliances groups. The average pretreatment PAR scores (United Kingdom weighting) were 20.81 for Invisalign and 22.79 for fixed appliances (P = 1.0000). Posttreatment weighted PAR scores between Invisalign and fixed appliances were not statistically different (P = 0.7420). On average, the Invisalign patients finished 5.7 months faster than did those with fixed appliances (P = 0.0040). The weighted PAR score reduction with treatment was not statistically different between the Invisalign and fixed appliances groups (P = 0.4573). All patients in both groups had more than a 30% reduction in the PAR scores. Logistic regression analysis indicated that the odds of achieving "great improvement" in the Invisalign group were 0.329 times the odds of achieving "great improvement" in the fixed appliances group after controlling for age (P = 0.0150). Our data showed that both Invisalign and fixed appliances were able to improve the malocclusion. Invisalign patients finished treatment faster than did those with fixed appliances. However, it appears that Invisalign may not be as effective as fixed appliances in achieving "great improvement" in a malocclusion. This study might help clinicians to determine appropriate patients for Invisalign treatment. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Effect of clenbuterol on tracheal mucociliary transport in horses undergoing simulated long-distance transportation.

PubMed

Norton, J L; Jackson, K; Chen, J W; Boston, R; Nolen-Walston, R D

2013-01-01

Pneumonia is observed in horses after long-distance transportation in association with confinement of head position leading to reduction in tracheal mucociliary clearance rate (TMCR). Clenbuterol, a beta-2 agonist shown to increase TMCR in the horse, will ameliorate the effects of a fixed elevated head position on large airway contamination and inflammation in a model of long-distance transportation model. Six adult horses. A cross-over designed prospective study. Horses were maintained with a fixed elevated head position for 48 hours to simulate long-distance transport, and treated with clenbuterol (0.8 μg/kg PO q12h) or a placebo starting 12 hours before simulated transportation. TMCR was measured using a charcoal clearance technique. Data were collected at baseline and 48 hours, and included TMCR, tracheal wash cytology and quantitative culture, rectal temperature, CBC, fibrinogen, and serum TNFα, IL-10, and IL-2 levels. There was a 18-21 day washout between study arms, and data were analyzed using regression analysis and Wilcoxon rank-sum tests. Tracheal mucociliary clearance rate was significantly decreased after transportation in both treatment (P = .002) and placebo (P = .03) groups. There was a significant effect of treatment on TMCR, with the treatment group showing half the reduction in TMCR compared with the placebo group (P = .002). Other significant differences between before- and after-transportation samples occurred for serum fibrinogen, peripheral eosinophil count, quantitative culture, tracheal bacteria, and degenerate neutrophils, though no treatment effect was found. Treatment with clenbuterol modestly attenuates the deleterious effects of this long-distance transportation model on tracheal mucociliary clearance. Copyright © 2013 by the American College of Veterinary Internal Medicine.

Is Exposure to Income Inequality a Public Health Concern? Lagged Effects of Income Inequality on Individual and Population Health

PubMed Central

Mellor, Jennifer M; Milyo, Jeffrey

2003-01-01

Objective To examine the health consequences of exposure to income inequality. Data Sources Secondary analysis employing data from several publicly available sources. Measures of individual health status and other individual characteristics are obtained from the March Current Population Survey (CPS). State-level income inequality is measured by the Gini coefficient based on family income, as reported by the U.S. Census Bureau and Al-Samarrie and Miller (1967). State-level mortality rates are from the Vital Statistics of the United States; other state-level characteristics are from U.S. census data as reported in the Statistical Abstract of the United States. Study Design We examine the effects of state-level income inequality lagged from 5 to 29 years on individual health by estimating probit models of poor/fair health status for samples of adults aged 25–74 in the 1995 through 1999 March CPS. We control for several individual characteristics, including educational attainment and household income, as well as regional fixed effects. We use multivariate regression to estimate the effects of income inequality lagged 10 and 20 years on state-level mortality rates for 1990, 1980, 1970, and 1960. Principal Findings Lagged income inequality is not significantly associated with individual health status after controlling for regional fixed effects. Lagged income inequality is not associated with all cause mortality, but associated with reduced mortality from cardiovascular disease and malignant neoplasms, after controlling for state fixed-effects. Conclusions In contrast to previous studies that fail to control for regional variations in health outcomes, we find little support for the contention that exposure to income inequality is detrimental to either individual or population health. PMID:12650385

Occupational exposures to solvents and metals are associated with fixed airflow obstruction.

PubMed

Alif, Sheikh M; Dharmage, Shyamali C; Benke, Geza; Dennekamp, Martine; Burgess, John A; Perret, Jennifer L; Lodge, Caroline J; Morrison, Stephen; Johns, David P; Giles, Graham G; Gurrin, Lyle C; Thomas, Paul S; Hopper, John L; Wood-Baker, Richard; Thompson, Bruce R; Feather, Iain H; Vermeulen, Roel; Kromhout, Hans; Walters, E Haydn; Abramson, Michael J; Matheson, Melanie C

2017-11-01

Objectives This study investigated the associations between occupational exposures to solvents and metals and fixed airflow obstruction (AO) using post-bronchodilator spirometry. Methods We included 1335 participants from the 2002-2008 follow-up of the Tasmanian Longitudinal Health Study. Ever-exposure and cumulative exposure-unit (EU) years were calculated using the ALOHA plus job exposure matrix (JEM). Fixed AO was defined as post-bronchodilator forced expiratory volume in one second (FEV1)/forced vital capacity (FVC) <0.7 and FEV 1 /FVC

Understanding cost of care for patients on renal replacement therapy: looking beyond fixed tariffs.

PubMed

Li, Bernadette; Cairns, John A; Fotheringham, James; Tomson, Charles R; Forsythe, John L; Watson, Christopher; Metcalfe, Wendy; Fogarty, Damian G; Draper, Heather; Oniscu, Gabriel C; Dudley, Christopher; Johnson, Rachel J; Roderick, Paul; Leydon, Geraldine; Bradley, J Andrew; Ravanan, Rommel

2015-10-01

In a number of countries, reimbursement to hospitals providing renal dialysis services is set according to a fixed tariff. While the cost of maintenance dialysis and transplant surgery are amenable to a system of fixed tariffs, patients with established renal failure commonly present with comorbid conditions that can lead to variations in the need for hospitalization beyond the provision of renal replacement therapy. Patient-level cost data for incident renal replacement therapy patients in England were obtained as a result of linkage of the Hospital Episodes Statistics dataset to UK Renal Registry data. Regression models were developed to explore variations in hospital costs in relation to treatment modality, number of years on treatment and factors such as age and comorbidities. The final models were then used to predict annual costs for patients with different sets of characteristics. Excluding the cost of renal replacement therapy itself, inpatient costs generally decreased with number of years on treatment for haemodialysis and transplant patients, whereas costs for patients receiving peritoneal dialysis remained constant. Diabetes was associated with higher mean annual costs for all patients irrespective of treatment modality and hospital setting. Age did not have a consistent effect on costs. Combining predicted hospital costs with the fixed costs of renal replacement therapy showed that the total cost differential for a patient continuing on dialysis rather than receiving a transplant is considerable following the first year of renal replacement therapy, thus reinforcing the longer-term economic advantage of transplantation over dialysis for the health service. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Academic achievement and course taking among language minority youth in U.S. schools: Effects of ESL placement

PubMed Central

Callahan, Rebecca; Wilkinson, Lindsey; Muller, Chandra

2014-01-01

The 1974 Lau decision requires that U.S. public schools ensure a meaningful education for students learning English. English as a Second Language (ESL) placement is an institutional response to the linguistic needs of these students; however, its academic implications remain largely unexplored. Using nationally representative data from the Educational Longitudinal Study (ELS), the effects of ESL placement on college preparatory course enrollment and academic achievement of language minority students are estimated, first with fixed effects regression models and then with multi-level propensity score matching techniques. While numerous school and individual level factors beyond language proficiency predict ESL placement, a significant negative estimated effect of ESL placement on science enrollment and cumulative GPA is consistently found. Perhaps more important, however, no positive effects of ESL placement on the achievement of language minority youth are found when accounting for English proficiency and other potential covariates. PMID:25431506

Academic achievement and course taking among language minority youth in U.S. schools: Effects of ESL placement.

PubMed

Callahan, Rebecca; Wilkinson, Lindsey; Muller, Chandra

2010-03-01

The 1974 Lau decision requires that U.S. public schools ensure a meaningful education for students learning English. English as a Second Language (ESL) placement is an institutional response to the linguistic needs of these students; however, its academic implications remain largely unexplored. Using nationally representative data from the Educational Longitudinal Study (ELS), the effects of ESL placement on college preparatory course enrollment and academic achievement of language minority students are estimated, first with fixed effects regression models and then with multi-level propensity score matching techniques. While numerous school and individual level factors beyond language proficiency predict ESL placement, a significant negative estimated effect of ESL placement on science enrollment and cumulative GPA is consistently found. Perhaps more important, however, no positive effects of ESL placement on the achievement of language minority youth are found when accounting for English proficiency and other potential covariates.

Full Bayes Poisson gamma, Poisson lognormal, and zero inflated random effects models: Comparing the precision of crash frequency estimates.

PubMed

Aguero-Valverde, Jonathan

2013-01-01

In recent years, complex statistical modeling approaches have being proposed to handle the unobserved heterogeneity and the excess of zeros frequently found in crash data, including random effects and zero inflated models. This research compares random effects, zero inflated, and zero inflated random effects models using a full Bayes hierarchical approach. The models are compared not just in terms of goodness-of-fit measures but also in terms of precision of posterior crash frequency estimates since the precision of these estimates is vital for ranking of sites for engineering improvement. Fixed-over-time random effects models are also compared to independent-over-time random effects models. For the crash dataset being analyzed, it was found that once the random effects are included in the zero inflated models, the probability of being in the zero state is drastically reduced, and the zero inflated models degenerate to their non zero inflated counterparts. Also by fixing the random effects over time the fit of the models and the precision of the crash frequency estimates are significantly increased. It was found that the rankings of the fixed-over-time random effects models are very consistent among them. In addition, the results show that by fixing the random effects over time, the standard errors of the crash frequency estimates are significantly reduced for the majority of the segments on the top of the ranking. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Impact of Principal Movement and School Achievement on Principal Salaries

ERIC Educational Resources Information Center

Tran, Henry; Buckman, David G.

2017-01-01

This study examines whether principals' movements and school achievement are associated with their salaries. Predictors of principal salaries were examined using three years of panel data. Results from a fixed-effects regression analysis suggest that principals who moved to school leadership positions in other districts leveraged higher salaries…

Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies

USDA-ARS?s Scientific Manuscript database

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises t...

Income analysis of goat farmers on the farmers group in district of Serdang Bedagai

NASA Astrophysics Data System (ADS)

Manurung, J. N.; Hasnudi; Supriana, T.

2018-02-01

The farmers group are expected to reduce the production cost of goat breeding and improve the income of farmers which impact on the welfare of goat farmers. This research aim to analyze the factors that influence the income of farmers group, in sub-district Dolok Masihul Pegajahan, and Dolok Merawan, Serdang Bedagai. The method used is survey method with 90 respondents. Data was analysed by multiple linear regression. The result showed, simultaneously goat cost, sale price of goat, fixed cost and variable cost had significant effect on income of goat farmers. Partially, goat cost, variable cost and sale price of goat had significant effect on income of goat farmers, while fixed cost had no significant effect.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-06-20

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

Fathers’ Participation in Parenting and Maternal Parenting Stress: Variation by Relationship Status

PubMed Central

Nomaguchi, Kei; Brown, Susan L.; Leyman, Tanya M.

2015-01-01

The growing diversity in mother-father relationship status has led to a debate over the role of fathers in parenting. Little is known, however, about how fathers’ participation in parenting is linked to maternal well-being across different mother-father relationship statuses. Using data from the Fragile Families and Child Wellbeing Study (N = 2,062), fixed-effects as well as random-effects regression models show that overall fathers’ engagement with children and sharing in child-related chores are negatively related to maternal parenting stress. Fathers’ cooperative coparenting is negatively related to maternal parenting stress only in the random-effects model, suggesting that the association is driven by selection factors. There is little variation in these associations by mother-father relationship status, once selection factors are controlled for. These findings extend support for the current cultural emphasis on benefits of fathers’ active participation in parenting for mothers and children even after the mother-father relationship dissolved. PMID:28479649

Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors.

PubMed

Minet, L; Gehr, R; Hatzopoulou, M

2017-11-01

The development of reliable measures of exposure to traffic-related air pollution is crucial for the evaluation of the health effects of transportation. Land-use regression (LUR) techniques have been widely used for the development of exposure surfaces, however these surfaces are often highly sensitive to the data collected. With the rise of inexpensive air pollution sensors paired with GPS devices, we witness the emergence of mobile data collection protocols. For the same urban area, can we achieve a 'universal' model irrespective of the number of locations and sampling visits? Can we trade the temporal representation of fixed-point sampling for a larger spatial extent afforded by mobile monitoring? This study highlights the challenges of short-term mobile sampling campaigns in terms of the resulting exposure surfaces. A mobile monitoring campaign was conducted in 2015 in Montreal; nitrogen dioxide (NO 2 ) levels at 1395 road segments were measured under repeated visits. We developed LUR models based on sub-segments, categorized in terms of the number of visits per road segment. We observe that LUR models were highly sensitive to the number of road segments and to the number of visits per road segment. The associated exposure surfaces were also highly dissimilar. Copyright © 2017 Elsevier Ltd. All rights reserved.

Relationship between recycling rate and air pollution: Waste management in the state of Massachusetts.

PubMed

Giovanis, Eleftherios

2015-06-01

This study examines the relationship between recycling rate of solid waste and air pollution using data from a waste municipality survey in the state of Massachusetts during the period 2009-2012. Two econometric approaches are applied. The first approach is a fixed effects model, while the second is a Stochastic Frontier Analysis (SFA) with fixed effects model. The advantage of the first approach is the ability of controlling for stable time invariant characteristics of the municipalities, thereby eliminating potentially large sources of bias. The second approach is applied in order to estimate the technical efficiency and rank of each municipality accordingly. The regressions control for various demographic, economic and recycling services, such as income per capita, population density, unemployment, trash services, Pay-as-you-throw (PAYT) program and meteorological data. The findings support that a negative relationship between particulate particles in the air 2.5 μm or less in size (PM2.5) and recycling rate is presented. In addition, the pollution is increased with increases on income per capita up to $23,000-$26,000, while after this point income contributes positively on air quality. Finally, based on the efficiency derived by the Stochastic Frontier Analysis (SFA) model, the municipalities which provide both drop off and curbside services for trash, food and yard waste and the PAYT program present better performance regarding the air quality. Copyright © 2015 Elsevier Ltd. All rights reserved.

Does Stepwise Voltage Ramping Protect the Kidney from Injury During Extracorporeal Shockwave Lithotripsy? Results of a Prospective Randomized Trial.

PubMed

Skuginna, Veronika; Nguyen, Daniel P; Seiler, Roland; Kiss, Bernhard; Thalmann, George N; Roth, Beat

2016-02-01

Renal damage is more frequent with new-generation lithotripters. However, animal studies suggest that voltage ramping minimizes the risk of complications following extracorporeal shock wave lithotripsy (SWL). In the clinical setting, the optimal voltage strategy remains unclear. To evaluate whether stepwise voltage ramping can protect the kidney from damage during SWL. A total of 418 patients with solitary or multiple unilateral kidney stones were randomized to receive SWL using a Modulith SLX-F2 lithotripter with either stepwise voltage ramping (n=213) or a fixed maximal voltage (n=205). SWL. The primary outcome was sonographic evidence of renal hematomas. Secondary outcomes included levels of urinary markers of renal damage, stone disintegration, stone-free rate, and rates of secondary interventions within 3 mo of SWL. Descriptive statistics were used to compare clinical outcomes between the two groups. A logistic regression model was generated to assess predictors of hematomas. Significantly fewer hematomas occurred in the ramping group(12/213, 5.6%) than in the fixed group (27/205, 13%; p=0.008). There was some evidence that the fixed group had higher urinary β2-microglobulin levels after SWL compared to the ramping group (p=0.06). Urinary microalbumin levels, stone disintegration, stone-free rate, and rates of secondary interventions did not significantly differ between the groups. The logistic regression model showed a significantly higher risk of renal hematomas in older patients (odds ratio [OR] 1.03, 95% confidence interval [CI] 1.00-1.05; p=0.04). Stepwise voltage ramping was associated with a lower risk of hematomas (OR 0.39, 95% CI 0.19-0.80; p=0.01). The study was limited by the use of ultrasound to detect hematomas. In this prospective randomized study, stepwise voltage ramping during SWL was associated with a lower risk of renal damage compared to a fixed maximal voltage without compromising treatment effectiveness. Lithotripsy is a noninvasive technique for urinary stone disintegration using ultrasonic energy. In this study, two voltage strategies are compared. The results show that a progressive increase in voltage during lithotripsy decreases the risk of renal hematomas while maintaining excellent outcomes. ISRCTN95762080. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Estimation of Spatial Dynamic Nonparametric Durbin Models with Fixed Effects

ERIC Educational Resources Information Center

Qian, Minghui; Hu, Ridong; Chen, Jianwei

2016-01-01

Spatial panel data models have been widely studied and applied in both scientific and social science disciplines, especially in the analysis of spatial influence. In this paper, we consider the spatial dynamic nonparametric Durbin model (SDNDM) with fixed effects, which takes the nonlinear factors into account base on the spatial dynamic panel…

Interaction between the FTO gene, body mass index and depression: meta-analysis of 13701 individuals†

PubMed Central

Rivera, Margarita; Locke, Adam E.; Corre, Tanguy; Czamara, Darina; Wolf, Christiane; Ching-Lopez, Ana; Milaneschi, Yuri; Kloiber, Stefan; Cohen-Woods, Sara; Rucker, James; Aitchison, Katherine J.; Bergmann, Sven; Boomsma, Dorret I.; Craddock, Nick; Gill, Michael; Holsboer, Florian; Hottenga, Jouke-Jan; Korszun, Ania; Kutalik, Zoltan; Lucae, Susanne; Maier, Wolfgang; Mors, Ole; Müller-Myhsok, Bertram; Owen, Michael J.; Penninx, Brenda W. J. H.; Preisig, Martin; Rice, John; Rietschel, Marcella; Tozzi, Federica; Uher, Rudolf; Vollenweider, Peter; Waeber, Gerard; Willemsen, Gonneke; Craig, Ian W.; Farmer, Anne E.; Lewis, Cathryn M.; Breen, Gerome; McGuffin, Peter

2017-01-01

Background Depression and obesity are highly prevalent, and major impacts on public health frequently co-occur. Recently, we reported that having depression moderates the effect of the FTO gene, suggesting its implication in the association between depression and obesity. Aims To confirm these findings by investigating the FTO polymorphism rs9939609 in new cohorts, and subsequently in a meta-analysis. Method The sample consists of 6902 individuals with depression and 6799 controls from three replication cohorts and two original discovery cohorts. Linear regression models were performed to test for association between rs9939609 and body mass index (BMI), and for the interaction between rs9939609 and depression status for an effect on BMI. Fixed and random effects meta-analyses were performed using METASOFT. Results In the replication cohorts, we observed a significant interaction between FTO, BMI and depression with fixed effects meta-analysis (β = 0.12, P = 2.7 × 10−4) and with the Han/Eskin random effects method (P = 1.4 × 10−7) but not with traditional random effects (β = 0.1, P = 0.35). When combined with the discovery cohorts, random effects meta-analysis also supports the interaction (β = 0.12, P = 0.027) being highly significant based on the Han/Eskin model (P = 6.9 × 10−8). On average, carriers of the risk allele who have depression have a 2.2% higher BMI for each risk allele, over and above the main effect of FTO. Conclusions This meta-analysis provides additional support for a significant interaction between FTO, depression and BMI, indicating that depression increases the effect of FTO on BMI. The findings provide a useful starting point in understanding the biological mechanism involved in the association between obesity and depression. PMID:28642257

Relationship between recycling rate and air pollution: Waste management in the state of Massachusetts

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

Giovanis, Eleftherios, E-mail: giovanis95@gmail.com

Highlights: • This study examines the relationship between recycling rate of solid waste and air pollution. • Fixed effects Stochastic Frontier Analysis model with panel data are employed. • The case study is a waste municipality survey in the state of Massachusetts during 2009–2012. • The findings support that a negative relationship between air pollution and recycling. - Abstract: This study examines the relationship between recycling rate of solid waste and air pollution using data from a waste municipality survey in the state of Massachusetts during the period 2009–2012. Two econometric approaches are applied. The first approach is a fixedmore » effects model, while the second is a Stochastic Frontier Analysis (SFA) with fixed effects model. The advantage of the first approach is the ability of controlling for stable time invariant characteristics of the municipalities, thereby eliminating potentially large sources of bias. The second approach is applied in order to estimate the technical efficiency and rank of each municipality accordingly. The regressions control for various demographic, economic and recycling services, such as income per capita, population density, unemployment, trash services, Pay-as-you-throw (PAYT) program and meteorological data. The findings support that a negative relationship between particulate particles in the air 2.5 μm or less in size (PM{sub 2.5}) and recycling rate is presented. In addition, the pollution is increased with increases on income per capita up to $23,000–$26,000, while after this point income contributes positively on air quality. Finally, based on the efficiency derived by the Stochastic Frontier Analysis (SFA) model, the municipalities which provide both drop off and curbside services for trash, food and yard waste and the PAYT program present better performance regarding the air quality.« less

Modeling demand for public transit services in rural areas

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

Attaluri, P.; Seneviratne, P.N.; Javid, M.

1997-05-01

Accurate estimates of demand are critical for planning, designing, and operating public transit systems. Previous research has demonstrated that the expected demand in rural areas is a function of both demographic and transit system variables. Numerous models have been proposed to describe the relationship between the aforementioned variables. However, most of them are site specific and their validity over time and space is not reported or perhaps has not been tested. Moreover, input variables in some cases are extremely difficult to quantify. In this article, the estimation of demand using the generalized linear modeling technique is discussed. Two separate models,more » one for fixed-route and another for demand-responsive services, are presented. These models, calibrated with data from systems in nine different states, are used to demonstrate the appropriateness and validity of generalized linear models compared to the regression models. They explain over 70% of the variation in expected demand for fixed-route services and 60% of the variation in expected demand for demand-responsive services. It was found that the models are spatially transferable and that data for calibration are easily obtainable.« less

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

PubMed

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

2011-08-01

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

An R2 statistic for fixed effects in the linear mixed model.

PubMed

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

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.

ELASTIC NET FOR COX'S PROPORTIONAL HAZARDS MODEL WITH A SOLUTION PATH ALGORITHM.

PubMed

Wu, Yichao

2012-01-01

For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox's proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox's proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems.

Modeling the influence of Chevron alignment sign on young male driver performance: A driving simulator study.

PubMed

Wu, Yiping; Zhao, Xiaohua; Chen, Chen; He, Jiayuan; Rong, Jian; Ma, Jianming

2016-10-01

In China, the Chevron alignment sign on highways is a vertical rectangle with a white arrow and border on a blue background, which differs from its counterpart in other countries. Moreover, little research has been devoted to the effectiveness of China's Chevron signs; there is still no practical method to quantitatively describe the impact of Chevron signs on driver performance in roadway curves. In this paper, a driving simulator experiment collected data on the driving performance of 30 young male drivers as they navigated on 29 different horizontal curves under different conditions (presence of Chevron signs, curve radius and curve direction). To address the heterogeneity issue in the data, three models were estimated and tested: a pooled data linear regression model, a fixed effects model, and a random effects model. According to the Hausman Test and Akaike Information Criterion (AIC), the random effects model offers the best fit. The current study explores the relationship between driver performance (i.e., vehicle speed and lane position) and horizontal curves with respect to the horizontal curvature, presence of Chevron signs, and curve direction. This study lays a foundation for developing procedures and guidelines that would allow more uniform and efficient deployment of Chevron signs on China's highways. Copyright © 2016 Elsevier Ltd. All rights reserved.

How to derive biological information from the value of the normalization constant in allometric equations.

PubMed

Kaitaniemi, Pekka

2008-04-09

Allometric equations are widely used in many branches of biological science. The potential information content of the normalization constant b in allometric equations of the form Y = bX(a) has, however, remained largely neglected. To demonstrate the potential for utilizing this information, I generated a large number of artificial datasets that resembled those that are frequently encountered in biological studies, i.e., relatively small samples including measurement error or uncontrolled variation. The value of X was allowed to vary randomly within the limits describing different data ranges, and a was set to a fixed theoretical value. The constant b was set to a range of values describing the effect of a continuous environmental variable. In addition, a normally distributed random error was added to the values of both X and Y. Two different approaches were then used to model the data. The traditional approach estimated both a and b using a regression model, whereas an alternative approach set the exponent a at its theoretical value and only estimated the value of b. Both approaches produced virtually the same model fit with less than 0.3% difference in the coefficient of determination. Only the alternative approach was able to precisely reproduce the effect of the environmental variable, which was largely lost among noise variation when using the traditional approach. The results show how the value of b can be used as a source of valuable biological information if an appropriate regression model is selected.

Correlation among extinction efficiency and other parameters in an aggregate dust model

NASA Astrophysics Data System (ADS)

Dhar, Tanuj Kumar; Sekhar Das, Himadri

2017-10-01

We study the extinction properties of highly porous Ballistic Cluster-Cluster Aggregate dust aggregates in a wide range of complex refractive indices (1.4≤ n≤ 2.0, 0.001≤ k≤ 1.0) and wavelengths (0.11 {{μ }}{{m}}≤ {{λ }}≤ 3.4 {{μ }} m). An attempt has been made for the first time to investigate the correlation among extinction efficiency ({Q}{ext}), composition of dust aggregates (n,k), wavelength of radiation (λ) and size parameter of the monomers (x). If k is fixed at any value between 0.001 and 1.0, {Q}{ext} increases with increase of n from 1.4 to 2.0. {Q}{ext} and n are correlated via linear regression when the cluster size is small, whereas the correlation is quadratic at moderate and higher sizes of the cluster. This feature is observed at all wavelengths (ultraviolet to optical to infrared). We also find that the variation of {Q}{ext} with n is very small when λ is high. When n is fixed at any value between 1.4 and 2.0, it is observed that {Q}{ext} and k are correlated via a polynomial regression equation (of degree 1, 2, 3 or 4), where the degree of the equation depends on the cluster size, n and λ. The correlation is linear for small size and quadratic/cubic/quartic for moderate and higher sizes. We have also found that {Q}{ext} and x are correlated via a polynomial regression (of degree 3, 4 or 5) for all values of n. The degree of regression is found to be n and k-dependent. The set of relations obtained from our work can be used to model interstellar extinction for dust aggregates in a wide range of wavelengths and complex refractive indices.

The UK Military Experience of Thoracic Injury in the Wars in Iraq and Afghanistan

DTIC Science & Technology

2013-01-01

investigations including computed tomography (CT), laboratory and blood bank. A Role 4 hospital is a fixed capability in the home nation capable of providing full...not an independent predictor of mortality in our model. Goodness of the logistic regression model fit was demonstrated using a Hosmer and Lemeshow test...of good practice and ethical care; thus we believe the hidden mortality is minimal. It is possible that in some circumstances, the desire to do

Comparing the Income Elasticity of Health Spending in Middle-Income and High-Income Countries: The Role of Financial Protection

PubMed Central

Vargas Bustamante, Arturo; Shimoga, Sandhya V.

2018-01-01

Background: As middle-income countries become more affluent, economically sophisticated and productive, health expenditure patterns are likely to change. Other socio-demographic and political changes that accompany rapid economic growth are also likely to influence health spending and financial protection. Methods: This study investigates the relationship between growth on per-capita healthcare expenditure and gross domestic product (GDP) in a group of 27 large middle-income economies and compares findings with those of 24 high-income economies from the Organization for Economic Cooperation and Development (OECD) group. This comparison uses national accounts data from 1995-2014. We hypothesize that the aggregated income elasticity of health expenditure in middle-income countries would be less than one (meaning healthcare is a normal good). An initial exploratory analysis tests between fixed-effects and random-effects model specifications. A fixed-effects model with time-fixed effects is implemented to assess the relationship between the two measures. Unit root, Hausman and serial correlation tests are conducted to determine model fit. Additional explanatory variables are introduced in different model specifications to test the robustness of our regression results. We include the out-of-pocket (OOP) share of health spending in each model to study the potential role of financial protection in our sample of high- and middle-income countries. The first-difference of study variables is implemented to address non-stationarity and cointegration properties. Results: The elasticity of per-capita health expenditure and GDP growth is positive and statistically significant among sampled middle-income countries (51 per unit-growth in GDP) and high-income countries (50 per unit-growth in GDP). In contrast with previous research that has found that income elasticity of health spending in middle-income countries is larger than in high-income countries, our findings show that elasticity estimates can change if different criteria are used to assemble a more homogenous group of middle-income countries. Financial protection differences between middle- and high-income countries, however, are not associated with their respective income elasticity of health spending. Conclusion: The study findings show that in spite of the rapid economic growth experienced by the sampled middleincome countries, the aggregated income elasticity of health expenditure in them is less than one, and equals that of high-income countries. PMID:29524954

Vocalization behavior and response of black rails

USGS Publications Warehouse

Legare, M.L.; Eddleman, W.R.; Buckley, P.A.; Kelly, C.

1999-01-01

We measured the vocal responses and movements of radio-tagged black rails (Laterallus jamaicensis) (n = 43, 26 males, 17 females) to playback of vocalizations at 2 sites in Florida during the breeding seasons of 1992-95. We used regression coefficients from logistic regression equations to model the probability of a response conditional to the birds' sex, nesting status, distance to playback source, and the time of survey. With a probability of 0.811, non-nesting male black rails were most likely to respond to playback, while nesting females were the least likely to respond (probability = 0.189). Linear regression was used to determine daily, monthly, and annual variation in response from weekly playback surveys along a fixed route during the breeding seasons of 1993-95. Significant sources of variation in the linear regression model were month (F = 3.89, df = 3, p = 0.0140), year (F = 9.37, df = 2, p = 0.0003), temperature (F = 5.44, df=1, p = 0.0236), and month*year (F = 2.69, df = 5, p = 0.0311). The model was highly significant (p < 0.0001) and explained 53% of the variation of mean response per survey period (R2 = 0.5353). Response probability data obtained from the radio-tagged black rails and data from the weekly playback survey route were combined to provide a density estimate of 0.25 birds/ha for the St. Johns National Wildlife Refuge. Density estimates for black rails may be obtained from playback surveys, and fixed radius circular plots. Circular plots should be considered as having a radius of 80 m and be located so the plot centers are 150 m apart. Playback tapes should contain one series of Kic-kic-kerr and Growl vocalizations recorded within the same geographic region as the study area. Surveys should be conducted from 0-2 hours after sunrise or 0-2 hours before sunset, during the pre-nesting season, and when wind velocity is < 20 kph. Observers should listen for 3-4 minutes after playing the survey tape and record responses heard during that time. Observers should be trained to identify black rail vocalizations and should have acceptable hearing ability. Given the number of variables that may have large effects on the response behavior of black rails to tape playback, we recommend that future studies using playback surveys should be cautious when presenting estimates of 'absolute' density. Though results did account for variation in response behavior, we believe that additional variation in vocal response between sites, with breeding status, and bird density remains in question. Playback surveys along fixed routes providing a simple index of abundance would be useful to monitor populations over large geographic areas, and over time. Considering the limitations of most agency resources for webless waterbirds, index surveys may be more appropriate. Future telemetry studies of this type on other species and at other sites would be useful to calibrate information obtained from playback surveys whether reporting an index of abundance or density estimate.

State tobacco control expenditures and tax paid cigarette sales

PubMed Central

Tauras, John A.; Xu, Xin; Huang, Jidong; King, Brian; Lavinghouze, S. Rene; Sneegas, Karla S.; Chaloupka, Frank J.

2018-01-01

This research is the first nationally representative study to examine the relationship between actual state-level tobacco control spending in each of the 5 CDC’s Best Practices for Comprehensive Tobacco Control Program categories and cigarette sales. We employed several alternative two-way fixed-effects regression techniques to estimate the determinants of cigarette sales in the United States for the years 2008–2012. State spending on tobacco control was found to have a negative and significant impact on cigarette sales in all models that were estimated. Spending in the areas of cessation interventions, health communication interventions, and state and community interventions were found to have a negative impact on cigarette sales in all models that were estimated, whereas spending in the areas of surveillance and evaluation, and administration and management were found to have negative effects on cigarette sales in only some models. Our models predict that states that spend up to seven times their current levels could still see significant reductions in cigarette sales. The findings from this research could help inform further investments in state tobacco control programs. PMID:29652890

On Models for Binomial Data with Random Numbers of Trials

PubMed Central

Comulada, W. Scott; Weiss, Robert E.

2010-01-01

Summary A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability π of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how π is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study. PMID:17688514

The Competitive Effects of the Louisiana Scholarship Program on Public School Performance. Louisiana Scholarship Program Evaluation Report #4. Technical Report

ERIC Educational Resources Information Center

Egalite, Anna J.

2016-01-01

Given the significant growth rate and geographic expansion of private school choice programs over the past two decades, it is important to examine how traditional public schools respond to the sudden injection of competition for students and resources. This article uses: (1) a school fixed effects approach; and (2) a regression discontinuity…

Systemic Sustainability in RtI Using Intervention-Based Scheduling Methodologies

ERIC Educational Resources Information Center

Dallas, William P.

2017-01-01

This study evaluated a scheduling methodology referred to as intervention-based scheduling to address the problem of practice regarding the fidelity of implementing Response to Intervention (RtI) in an existing school schedule design. Employing panel data, this study used fixed-effects regressions and first differences ordinary least squares (OLS)…

Messing Up Texas?: A Re-Analysis of the Effects of Executions on Homicides

PubMed Central

Brandt, Patrick T.; Kovandzic, Tomislav V.

2015-01-01

Executions in Texas from 1994–2005 do not deter homicides, contrary to the results of Land et al. (2009). We find that using different models—based on pre-tests for unit roots that correct for earlier model misspecifications—one cannot reject the null hypothesis that executions do not lead to a change in homicides in Texas over this period. Using additional control variables, we show that variables such as the number of prisoners in Texas may drive the main drop in homicides over this period. Such conclusions however are highly sensitive to model specification decisions, calling into question the assumptions about fixed parameters and constant structural relationships. This means that using dynamic regressions to account for policy changes that may affect homicides need to be done with significant care and attention. PMID:26398193

Associations of neighborhood disorganization and maternal spanking with children's aggression: A fixed-effects regression analysis.

PubMed

Ma, Julie; Grogan-Kaylor, Andrew; Lee, Shawna J

2018-02-01

This study employed fixed effects regression that controls for selection bias, omitted variables bias, and all time-invariant aspects of parent and child characteristics to examine the simultaneous associations between neighborhood disorganization, maternal spanking, and aggressive behavior in early childhood using data from the Fragile Families and Child Wellbeing Study (FFCWS). Analysis was based on 2,472 children and their mothers who participated in Wave 3 (2001-2003; child age 3) and Wave 4 (2003-2006; child age 5) of the FFCWS. Results indicated that higher rates of neighborhood crime and violence predicted higher levels of child aggression. Maternal spanking in the past year, whether frequent or infrequent, was also associated with increases in aggressive behavior. This study contributes statistically rigorous evidence that exposure to violence in the neighborhood as well as the family context are predictors of child aggression. We conclude with a discussion for the need for multilevel prevention and intervention approaches that target both community and parenting factors. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

The effect of hospital mergers on long-term sickness absence among hospital employees: a fixed effects multivariate regression analysis using panel data

PubMed Central

2014-01-01

Background Hospitals are merging to become more cost-effective. Mergers are often complex and difficult processes with variable outcomes. The aim of this study was to analyze the effect of mergers on long-term sickness absence among hospital employees. Methods Long-term sickness absence was analyzed among hospital employees (N = 107 209) in 57 hospitals involved in 23 mergers in Norway between 2000 and 2009. Variation in long-term sickness absence was explained through a fixed effects multivariate regression analysis using panel data with years-since-merger as the independent variable. Results We found a significant but modest effect of mergers on long-term sickness absence in the year of the merger, and in years 2, 3 and 4; analyzed by gender there was a significant effect for women, also for these years, but only in year 4 for men. However, men are less represented among the hospital workforce; this could explain the lack of significance. Conclusions Mergers has a significant effect on employee health that should be taken into consideration when deciding to merge hospitals. This study illustrates the importance of analyzing the effects of mergers over several years and the need for more detailed analyses of merger processes and of the changes that may occur as a result of such mergers. PMID:24490750

The occurrence of Simpson's paradox if site-level effect was ignored in the TREAT Asia HIV Observational Database.

PubMed

Jiamsakul, Awachana; Kerr, Stephen J; Chandrasekaran, Ezhilarasi; Huelgas, Aizobelle; Taecharoenkul, Sineenart; Teeraananchai, Sirinya; Wan, Gang; Ly, Penh Sun; Kiertiburanakul, Sasisopin; Law, Matthew

2016-08-01

In multisite human immunodeficiency virus (HIV) observational cohorts, clustering of observations often occurs within sites. Ignoring clustering may lead to "Simpson's paradox" (SP) where the trend observed in the aggregated data is reversed when the groups are separated. This study aimed to investigate the SP in an Asian HIV cohort and the effects of site-level adjustment through various Cox regression models. Survival time from combination antiretroviral therapy (cART) initiation was analyzed using four Cox models: (1) no site adjustment; (2) site as a fixed effect; (3) stratification through site; and (4) shared frailty on site. A total of 6,454 patients were included from 23 sites in Asia. SP was evident in the year of cART initiation variable. Model (1) shows the hazard ratio (HR) for years 2010-2014 was higher than the HR for 2006-2009, compared to 2003-2005 (HR = 0.68 vs. 0.61). Models (2)-(4) consistently implied greater improvement in survival for those who initiated in 2010-2014 than 2006-2009 contrasting findings from model (1). The effects of other significant covariates on survival were similar across four models. Ignoring site can lead to SP causing reversal of treatment effects. Greater emphasis should be made to include site in survival models when possible. Copyright © 2016 Elsevier Inc. All rights reserved.

Dispersion models and sampling of cacao mirid bug Sahlbergella singularis (Hemiptera: Miridae) on Theobroma Cacao in southern Cameroon.

PubMed

Bisseleua, D H B; Vidal, Stefan

2011-02-01

The spatio-temporal distribution of Sahlbergella singularis Haglung, a major pest of cacao trees (Theobroma cacao) (Malvaceae), was studied for 2 yr in traditional cacao forest gardens in the humid forest area of southern Cameroon. The first objective was to analyze the dispersion of this insect on cacao trees. The second objective was to develop sampling plans based on fixed levels of precision for estimating S. singularis populations. The following models were used to analyze the data: Taylor's power law, Iwao's patchiness regression, the Nachman model, and the negative binomial distribution. Our results document that Taylor's power law was a better fit for the data than the Iwao and Nachman models. Taylor's b and Iwao's β were both significantly >1, indicating that S. singularis aggregated on specific trees. This result was further supported by the calculated common k of 1.75444. Iwao's α was significantly <0, indicating that the basic distribution component of S. singularis was the individual insect. Comparison of negative binomial (NBD) and Nachman models indicated that the NBD model was appropriate for studying S. singularis distribution. Optimal sample sizes for fixed precision levels of 0.10, 0.15, and 0.25 were estimated with Taylor's regression coefficients. Required sample sizes increased dramatically with increasing levels of precision. This is the first study on S. singularis dispersion in cacao plantations. Sampling plans, presented here, should be a tool for research on population dynamics and pest management decisions of mirid bugs on cacao. © 2011 Entomological Society of America

Resonant frequency analysis of Timoshenko nanowires with surface stress for different boundary conditions

NASA Astrophysics Data System (ADS)

He, Qilu; Lilley, Carmen M.

2012-10-01

The influence of both surface and shear effects on the resonant frequency of nanowires (NWs) was studied by incorporating the Young-Laplace equation with the Timoshenko beam theory. Face-centered-cubic metal NWs were studied. A dimensional analysis of the resonant frequencies for fixed-fixed gold (100) NWs were compared to molecular dynamic simulations. Silver NWs with diameters from 10 nm-500 nm were modeled as a cantilever, simply supported and fixed-fixed system for aspect ratios from 2.5-20 to identify the shear, surface, and size effects on the resonant frequencies. The shear effect was found to have a larger significance than surface effects when the aspect ratios were small (i.e., <5) regardless of size for the diameters modeled. Finally, as the aspect ratio grows, the surface effect becomes significant for the smaller diameter NWs.

Effects of air pollution on infant and children respiratory mortality in four large Latin-American cities.

PubMed

Gouveia, Nelson; Junger, Washington Leite

2018-01-01

Air pollution is an important public health concern especially for children who are particularly susceptible. Latin America has a large children population, is highly urbanized and levels of pollution are substantially high, making the potential health impact of air pollution quite large. We evaluated the effect of air pollution on children respiratory mortality in four large urban centers: Mexico City, Santiago, Chile, and Sao Paulo and Rio de Janeiro in Brazil. Generalized Additive Models in Poisson regression was used to fit daily time-series of mortality due to respiratory diseases in infants and children, and levels of PM 10 and O 3 . Single lag and constrained polynomial distributed lag models were explored. Analyses were carried out per cause for each age group and each city. Fixed- and random-effects meta-analysis was conducted in order to combine the city-specific results in a single summary estimate. These cities host nearly 43 million people and pollution levels were above the WHO guidelines. For PM 10 the percentage increase in risk of death due to respiratory diseases in infants in a fixed effect model was 0.47% (0.09-0.85). For respiratory deaths in children 1-5 years old, the increase in risk was 0.58% (0.08-1.08) while a higher effect was observed for lower respiratory infections (LRI) in children 1-14 years old [1.38% (0.91-1.85)]. For O 3 , the only summarized estimate statistically significant was for LRI in infants. Analysis by season showed effects of O 3 in the warm season for respiratory diseases in infants, while negative effects were observed for respiratory and LRI deaths in children. We provided comparable mortality impact estimates of air pollutants across these cities and age groups. This information is important because many public policies aimed at preventing the adverse effects of pollution on health consider children as the population group that deserves the highest protection. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genetic evaluations for growth heat tolerance in Angus cattle.

PubMed

Bradford, H L; Fragomeni, B O; Bertrand, J K; Lourenco, D A L; Misztal, I

2016-10-01

The objectives were to assess the impact of heat stress and to develop a model for genetic evaluation of growth heat tolerance in Angus cattle. The American Angus Association provided weaning weight (WW) and yearling weight (YW) data, and records from the Upper South region were used because of the hot climatic conditions. Heat stress was characterized by a weaning (yearling) heat load function defined as the mean temperature-humidity index (THI) units greater than 75 (70) for 30 (150) d prior to the weigh date. Therefore, a weaning (yearling) heat load of 5 units corresponded to 80 (75) for the corresponding period prior to the weigh date. For all analyses, 82,669 WW and 69,040 YW were used with 3 ancestral generations in the pedigree. Univariate models were a proxy for the Angus growth evaluation, and reaction norms using 2 B-splines for heat load were fit separately for weaning and yearling heat loads. For both models, random effects included direct genetic, maternal genetic, maternal permanent environment (WW only), and residual. Fixed effects included a linear age covariate, age-of-dam class (WW only), and contemporary group for both models and fixed regressions on the B-splines in the reaction norm. Direct genetic correlations for WW were strong for modest heat load differences but decreased to less than 0.50 for large differences. Reranking of proven sires occurred for only WW direct effects for the reaction norms with extreme heat load differences. Conversely, YW results indicated little effect of heat stress on genetic merit. Therefore, weaning heat tolerance was a better candidate for developing selection tools. Maternal heritabilities were consistent across heat loads, and maternal genetic correlations were greater than 0.90 for nearly all heat load combinations. No evidence existed for a genotype × environment interaction for the maternal component of growth. Overall, some evidence exists for phenotypic plasticity for the direct genetic effects of WW, but traditional national cattle evaluations are likely adequately ranking sires for nonextreme environmental conditions.

The relationship between aircraft noise exposure and day-use visitor survey responses in backcountry areas of national parks.

PubMed

Rapoza, Amanda; Sudderth, Erika; Lewis, Kristin

2015-10-01

To evaluate the relationship between aircraft noise exposure and the quality of national park visitor experience, more than 4600 visitor surveys were collected at seven backcountry sites in four U.S. national parks simultaneously with calibrated sound level measurements. Multilevel logistic regression was used to estimate parameters describing the relationship among visitor responses, aircraft noise dose metrics, and mediator variables. For the regression models, survey responses were converted to three dichotomous variables, representing visitors who did or did not experience slightly or more, moderately or more, or very or more annoyance or interference with natural quiet from aircraft noise. Models with the most predictive power included noise dose metrics of sound exposure level, percent time aircraft were audible, and percentage energy due to helicopters and fixed-wing propeller aircraft. These models also included mediator variables: visitor ratings of the "importance of calmness, peace and tranquility," visitor group composition (adults or both adults and children), first visit to the site, previously taken an air tour, and participation in bird-watching or interpretive talks. The results complement and extend previous research conducted in frontcountry areas and will inform evaluations of air tour noise effects on visitors to national parks and remote wilderness sites.

Robust learning for optimal treatment decision with NP-dimensionality

PubMed Central

Shi, Chengchun; Song, Rui; Lu, Wenbin

2016-01-01

In order to identify important variables that are involved in making optimal treatment decision, Lu, Zhang and Zeng (2013) proposed a penalized least squared regression framework for a fixed number of predictors, which is robust against the misspecification of the conditional mean model. Two problems arise: (i) in a world of explosively big data, effective methods are needed to handle ultra-high dimensional data set, for example, with the dimension of predictors is of the non-polynomial (NP) order of the sample size; (ii) both the propensity score and conditional mean models need to be estimated from data under NP dimensionality. In this paper, we propose a robust procedure for estimating the optimal treatment regime under NP dimensionality. In both steps, penalized regressions are employed with the non-concave penalty function, where the conditional mean model of the response given predictors may be misspecified. The asymptotic properties, such as weak oracle properties, selection consistency and oracle distributions, of the proposed estimators are investigated. In addition, we study the limiting distribution of the estimated value function for the obtained optimal treatment regime. The empirical performance of the proposed estimation method is evaluated by simulations and an application to a depression dataset from the STAR*D study. PMID:28781717

Countervailing effects of income, air pollution, smoking, and obesity on aging and life expectancy: population-based study of U.S. Counties.

PubMed

Allen, Ryan T; Hales, Nicholas M; Baccarelli, Andrea; Jerrett, Michael; Ezzati, Majid; Dockery, Douglas W; Pope, C Arden

2016-08-12

Income, air pollution, obesity, and smoking are primary factors associated with human health and longevity in population-based studies. These four factors may have countervailing impacts on longevity. This analysis investigates longevity trade-offs between air pollution and income, and explores how relative effects of income and air pollution on human longevity are potentially influenced by accounting for smoking and obesity. County-level data from 2,996 U.S. counties were analyzed in a cross-sectional analysis to investigate relationships between longevity and the four factors of interest: air pollution (mean 1999-2008 PM2.5), median income, smoking, and obesity. Two longevity measures were used: life expectancy (LE) and an exceptional aging (EA) index. Linear regression, generalized additive regression models, and bivariate thin-plate smoothing splines were used to estimate the benefits of living in counties with higher incomes or lower PM2.5. Models were estimated with and without controls for smoking, obesity, and other factors. Models which account for smoking and obesity result in substantially smaller estimates of the effects of income and pollution on longevity. Linear regression models without these two variables estimate that a $1,000 increase in median income (1 μg/m(3) decrease in PM2.5) corresponds to a 27.39 (33.68) increase in EA and a 0.14 (0.12) increase in LE, whereas models that control for smoking and obesity estimate only a 12.32 (20.22) increase in EA and a 0.07 (0.05) increase in LE. Nonlinear models and thin-plate smoothing splines also illustrate that, at higher levels of income, the relative benefits of the income-pollution tradeoff changed-the benefit of higher incomes diminished relative to the benefit of lower air pollution exposure. Higher incomes and lower levels of air pollution both correspond with increased human longevity. Adjusting for smoking and obesity reduces estimates of the benefits of higher income and lower air pollution exposure. This adjustment also alters the tradeoff between income and pollution: increases in income become less beneficial relative to a fixed reduction in air pollution-especially at higher levels of income.

The impact of tiered physician networks on patient choices.

PubMed

Sinaiko, Anna D; Rosenthal, Meredith B

2014-08-01

To assess whether patient choice of physician or health plan was affected by physician tier-rankings. Administrative claims and enrollment data on 171,581 nonelderly beneficiaries enrolled in Massachusetts Group Insurance Commission health plans that include a tiered physician network and who had an office visit with a tiered physician. We estimate the impact of tier-rankings on physician market share within a plan of new patients and on the percent of a physician's patients who switch to other physicians with fixed effects regression models. The effect of tiering on consumer plan choice is estimated using logistic regression and a pre-post study design. Physicians in the bottom (least-preferred) tier, particularly certain specialist physicians, had lower market share of new patient visits than physicians with higher tier-rankings. Patients whose physician was in the bottom tier were more likely to switch health plans. There was no effect of tier-ranking on patients switching away from physicians whom they have seen previously. The effect of tiering appears to be among patients who choose new physicians and at the lower end of the distribution of tiered physicians, rather than moving patients to the "best" performers. These findings suggest strong loyalty of patients to physicians more likely to be considered their personal doctor. © Health Research and Educational Trust.

Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies

PubMed Central

Liu, Xiaolei; Huang, Meng; Fan, Bin; Buckler, Edward S.; Zhang, Zhiwu

2016-01-01

False positives in a Genome-Wide Association Study (GWAS) can be effectively controlled by a fixed effect and random effect Mixed Linear Model (MLM) that incorporates population structure and kinship among individuals to adjust association tests on markers; however, the adjustment also compromises true positives. The modified MLM method, Multiple Loci Linear Mixed Model (MLMM), incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, we divided MLMM into two parts: Fixed Effect Model (FEM) and a Random Effect Model (REM) and use them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model over-fitting problem in FEM, the associated markers are estimated in REM by using them to define kinship. The P values of testing markers and the associated markers are unified at each iteration. We named the new method as Fixed and random model Circulating Probability Unification (FarmCPU). Both real and simulated data analyses demonstrated that FarmCPU improves statistical power compared to current methods. Additional benefits include an efficient computing time that is linear to both number of individuals and number of markers. Now, a dataset with half million individuals and half million markers can be analyzed within three days. PMID:26828793

Data Shared Lasso: A Novel Tool to Discover Uplift.

PubMed

Gross, Samuel M; Tibshirani, Robert

2016-09-01

A model is presented for the supervised learning problem where the observations come from a fixed number of pre-specified groups, and the regression coefficients may vary sparsely between groups. The model spans the continuum between individual models for each group and one model for all groups. The resulting algorithm is designed with a high dimensional framework in mind. The approach is applied to a sentiment analysis dataset to show its efficacy and interpretability. One particularly useful application is for finding sub-populations in a randomized trial for which an intervention (treatment) is beneficial, often called the uplift problem. Some new concepts are introduced that are useful for uplift analysis. The value is demonstrated in an application to a real world credit card promotion dataset. In this example, although sending the promotion has a very small average effect, by targeting a particular subgroup with the promotion one can obtain a 15% increase in the proportion of people who purchase the new credit card.

Data Shared Lasso: A Novel Tool to Discover Uplift

PubMed Central

Gross, Samuel M.; Tibshirani, Robert

2017-01-01

A model is presented for the supervised learning problem where the observations come from a fixed number of pre-specified groups, and the regression coefficients may vary sparsely between groups. The model spans the continuum between individual models for each group and one model for all groups. The resulting algorithm is designed with a high dimensional framework in mind. The approach is applied to a sentiment analysis dataset to show its efficacy and interpretability. One particularly useful application is for finding sub-populations in a randomized trial for which an intervention (treatment) is beneficial, often called the uplift problem. Some new concepts are introduced that are useful for uplift analysis. The value is demonstrated in an application to a real world credit card promotion dataset. In this example, although sending the promotion has a very small average effect, by targeting a particular subgroup with the promotion one can obtain a 15% increase in the proportion of people who purchase the new credit card. PMID:29056802

Evaluating differential effects using regression interactions and regression mixture models

PubMed Central

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This paper focuses on understanding regression mixture models, a relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their formulation, and their assumptions are compared using Monte Carlo simulations and real data analysis. The capabilities of regression mixture models are described and specific issues to be addressed when conducting regression mixtures are proposed. The paper aims to clarify the role that regression mixtures can take in the estimation of differential effects and increase awareness of the benefits and potential pitfalls of this approach. Regression mixture models are shown to be a potentially effective exploratory method for finding differential effects when these effects can be defined by a small number of classes of respondents who share a typical relationship between a predictor and an outcome. It is also shown that the comparison between regression mixture models and interactions becomes substantially more complex as the number of classes increases. It is argued that regression interactions are well suited for direct tests of specific hypotheses about differential effects and regression mixtures provide a useful approach for exploring effect heterogeneity given adequate samples and study design. PMID:26556903

Green pastures: Do US real estate prices respond to population health?

PubMed

Nau, Claudia; Bishai, David

2018-01-01

We investigate whether communities with improving population health will subsequently experience rising real estate prices. Home price indices (HPIs) for 371 MSAs from 1990 to 2010 are regressed against life-expectancy five years prior. HPIs come from the Federal Housing Finance Agency. Life expectancy estimates come from the Institute of Health Metrics. Our analysis uses random and fixed effect models with a comprehensive set of controls. Life expectancy predicted increases in the HPI controlling for potential confounders. We found that, this effect varied spatially. Communities that invest their revenue from property taxes in public health infrastructure could benefit from a virtuous cycle of better health leading to higher property values. Communities that do not invest in health could enter vicious cycles and this could widen geospatial health and wealth disparities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of Marker-Based Genomic Estimated Breeding Values and Phenotypic Evaluation for Selection of Bacterial Spot Resistance in Tomato.

PubMed

Liabeuf, Debora; Sim, Sung-Chur; Francis, David M

2018-03-01

Bacterial spot affects tomato crops (Solanum lycopersicum) grown under humid conditions. Major genes and quantitative trait loci (QTL) for resistance have been described, and multiple loci from diverse sources need to be combined to improve disease control. We investigated genomic selection (GS) prediction models for resistance to Xanthomonas euvesicatoria and experimentally evaluated the accuracy of these models. The training population consisted of 109 families combining resistance from four sources and directionally selected from a population of 1,100 individuals. The families were evaluated on a plot basis in replicated inoculated trials and genotyped with single nucleotide polymorphisms (SNP). We compared the prediction ability of models developed with 14 to 387 SNP. Genomic estimated breeding values (GEBV) were derived using Bayesian least absolute shrinkage and selection operator regression (BL) and ridge regression (RR). Evaluations were based on leave-one-out cross validation and on empirical observations in replicated field trials using the next generation of inbred progeny and a hybrid population resulting from selections in the training population. Prediction ability was evaluated based on correlations between GEBV and phenotypes (r g ), percentage of coselection between genomic and phenotypic selection, and relative efficiency of selection (r g /r p ). Results were similar with BL and RR models. Models using only markers previously identified as significantly associated with resistance but weighted based on GEBV and mixed models with markers associated with resistance treated as fixed effects and markers distributed in the genome treated as random effects offered greater accuracy and a high percentage of coselection. The accuracy of these models to predict the performance of progeny and hybrids exceeded the accuracy of phenotypic selection.

HOW POPULATION STRUCTURE SHAPES NEIGHBORHOOD SEGREGATION*

PubMed Central

Bruch, Elizabeth E.

2014-01-01

This study investigates how choices about social affiliation based on one attribute can exacerbate or attenuate segregation on another correlated attribute. The specific application is the role of racial and economic factors in generating patterns of racial residential segregation. I identify three population parameters—between-group inequality, within-group inequality, and relative group size—that determine how income inequality between race groups affects racial segregation. I use data from the Panel Study of Income Dynamics to estimate models of individual-level residential mobility, and incorporate these estimates into agent-based models. I then simulate segregation dynamics under alternative assumptions about: (1) the relative size of minority groups; and (2) the degree of correlation between race and income among individuals. I find that income inequality can have offsetting effects at the high and low ends of the income distribution. I demonstrate the empirical relevance of the simulation results using fixed-effects, metro-level regressions applied to 1980-2000 U.S. Census data. PMID:25009360

Nursing Home Staffing Requirements and Input Substitution: Effects on Housekeeping, Food Service, and Activities Staff

PubMed Central

Bowblis, John R; Hyer, Kathryn

2013-01-01

Objective To study the effect of minimum nurse staffing requirements on the subsequent employment of nursing home support staff. Data Sources Nursing home data from the Online Survey Certification and Reporting (OSCAR) System merged with state nurse staffing requirements. Study Design Facility-level housekeeping, food service, and activities staff levels are regressed on nurse staffing requirements and other controls using fixed effect panel regression. Data Extraction Method OSCAR surveys from 1999 to 2004. Principal Findings Increases in state direct care and licensed nurse staffing requirements are associated with decreases in the staffing levels of all types of support staff. Conclusions Increased nursing home nurse staffing requirements lead to input substitution in the form of reduced support staffing levels. PMID:23445455

Nursing home staffing requirements and input substitution: effects on housekeeping, food service, and activities staff.

PubMed

Bowblis, John R; Hyer, Kathryn

2013-08-01

To study the effect of minimum nurse staffing requirements on the subsequent employment of nursing home support staff. Nursing home data from the Online Survey Certification and Reporting (OSCAR) System merged with state nurse staffing requirements. Facility-level housekeeping, food service, and activities staff levels are regressed on nurse staffing requirements and other controls using fixed effect panel regression. OSCAR surveys from 1999 to 2004. Increases in state direct care and licensed nurse staffing requirements are associated with decreases in the staffing levels of all types of support staff. Increased nursing home nurse staffing requirements lead to input substitution in the form of reduced support staffing levels. © Health Research and Educational Trust.

Children's Home Environments: Understanding the Role of Family Structure Changes

ERIC Educational Resources Information Center

Kowaleski-Jones, Lori; Dunifon, Rachel

2004-01-01

Using data from the 1996 National Longitudinal Survey of Youth (NLSY79) merged mother-child sample, we investigate the impact of two family events, parental divorce and the birth of a sibling, on the cognitive stimulation and emotional support provided to children in the home. We use fixed-effect regression techniques to control for unmeasured…

Testing the Efficacy of a Scholarship Program for Single Parent, Post-Freshmen, Full Time Undergraduates

ERIC Educational Resources Information Center

Carpenter, Dick M., II; Kaka, Sarah J.; Tygret, Jennifer A.; Cathcart, Katy

2018-01-01

This study examines the efficacy of a scholarship program designed to assist single parent, post-freshmen, full time undergraduate students and predictors of success among a sample of said students, where success is defined as progress toward completion, academic achievement, and degree completion. Results of fixed effects regression and…

Eat, Drink, Man, Woman: Gender, Income Share and Household Expenditure in South Africa

ERIC Educational Resources Information Center

Gummerson, Elizabeth; Schneider, Daniel

2013-01-01

This study examines how gendered household bargaining occurs in non-nuclear family households. We employ two South African data sets and use linear regression and household fixed effects to investigate the relationship between women's income shares and household expenditures. In married couple households, when women garner larger shares of income,…

Strategy, structure, and patient quality outcomes in ambulatory surgery centers (1997-2004).

PubMed

Chukmaitov, Askar; Devers, Kelly J; Harless, David W; Menachemi, Nir; Brooks, Robert G

2011-04-01

The purpose of this study was to examine potential associations among ambulatory surgery centers' (ASCs) organizational strategy, structure, and quality performance. The authors obtained several large-scale, all-payer claims data sets for the 1997 to 2004 period. The authors operationalized quality performance as unplanned hospitalizations at 30 days after outpatient arthroscopy and colonoscopy procedures. The authors draw on related organizational theory, behavior, and health services research literatures to develop their conceptual framework and hypotheses and fitted fixed and random effects Poisson regression models with the count of unplanned hospitalizations. Consistent with the key hypotheses formulated, the findings suggest that higher levels of specialization and the volume of procedures may be associated with a decrease in unplanned hospitalizations at ASCs.

Considerations when loading spinal finite element models with predicted muscle forces from inverse static analyses.

PubMed

Zhu, Rui; Zander, Thomas; Dreischarf, Marcel; Duda, Georg N; Rohlmann, Antonius; Schmidt, Hendrik

2013-04-26

Mostly simplified loads were used in biomechanical finite element (FE) studies of the spine because of a lack of data on muscular physiological loading. Inverse static (IS) models allow the prediction of muscle forces for predefined postures. A combination of both mechanical approaches - FE and IS - appears to allow a more realistic modeling. However, it is unknown what deviations are to be expected when muscle forces calculated for models with rigid vertebrae and fixed centers of rotation, as generally found in IS models, are applied to a FE model with elastic vertebrae and discs. The aim of this study was to determine the effects of these disagreements. Muscle forces were estimated for 20° flexion and 10° extension in an IS model and transferred to a FE model. The effects of the elasticity of bony structures (rigid vs. elastic) and the definition of the center of rotation (fixed vs. non-fixed) were quantified using the deviation of actual intervertebral rotation (IVR) of the FE model and the targeted IVR from the IS model. For extension, the elasticity of the vertebrae had only a minor effect on IVRs, whereas a non-fixed center of rotation increased the IVR deviation on average by 0.5° per segment. For flexion, a combination of the two parameters increased IVR deviation on average by 1° per segment. When loading FE models with predicted muscle forces from IS analyses, the main limitations in the IS model - rigidity of the segments and the fixed centers of rotation - must be considered. Copyright © 2013 Elsevier Ltd. All rights reserved.

Organ dose conversion coefficients for tube current modulated CT protocols for an adult population

NASA Astrophysics Data System (ADS)

Fu, Wanyi; Tian, Xiaoyu; Sahbaee, Pooyan; Zhang, Yakun; Segars, William Paul; Samei, Ehsan

2016-03-01

In computed tomography (CT), patient-specific organ dose can be estimated using pre-calculated organ dose conversion coefficients (organ dose normalized by CTDIvol, h factor) database, taking into account patient size and scan coverage. The conversion coefficients have been previously estimated for routine body protocol classes, grouped by scan coverage, across an adult population for fixed tube current modulated CT. The coefficients, however, do not include the widely utilized tube current (mA) modulation scheme, which significantly impacts organ dose. This study aims to extend the h factors and the corresponding dose length product (DLP) to create effective dose conversion coefficients (k factor) database incorporating various tube current modulation strengths. Fifty-eight extended cardiac-torso (XCAT) phantoms were included in this study representing population anatomy variation in clinical practice. Four mA profiles, representing weak to strong mA dependency on body attenuation, were generated for each phantom and protocol class. A validated Monte Carlo program was used to simulate the organ dose. The organ dose and effective dose was further normalized by CTDIvol and DLP to derive the h factors and k factors, respectively. The h factors and k factors were summarized in an exponential regression model as a function of body size. Such a population-based mathematical model can provide a comprehensive organ dose estimation given body size and CTDIvol. The model was integrated into an iPhone app XCATdose version 2, enhancing the 1st version based upon fixed tube current modulation. With the organ dose calculator, physicists, physicians, and patients can conveniently estimate organ dose.

The Role of Prostatitis in Prostate Cancer: Meta-Analysis

PubMed Central

Yunxia, Zhang; Zhu, Hong; Liu, Junjiang; Pumill, Chris

2013-01-01

Objective Use systematic review methods to quantify the association between prostatitis and prostate cancer, under both fixed and random effects model. Evidence Acquisition Case control studies of prostate cancer with information on prostatitis history. All studies published between 1990-2012, were collected to calculate a pooled odds ratio. Selection criteria: the selection criteria are as follows: human case control studies; published from May 1990 to July 2012; containing number of prostatitis, and prostate cancer cases. Evidence Synthesis In total, 20 case control studies were included. A significant association between prostatitis and prostate cancer was found, under both fixed effect model (pooled OR=1.50, 95%CI: 1.39-1.62), and random effects model (OR=1.64, 95%CI: 1.36-1.98). Personal interview based case control studies showed a high level of association (fixed effect model: pooled OR=1.59, 95%CI: 1.47-1.73, random effects model: pooled OR= 1.87, 95%CI: 1.52-2.29), compared with clinical based studies (fixed effect model: pooled OR=1.05, 95%CI: 0.86-1.28, random effects model: pooled OR= 0.98, 95%CI: 0.67-1.45). Additionally, pooled ORs, were calculated for each decade. In a fixed effect model: 1990’s: OR=1.58, 95% CI: 1.35-1.84; 2000’s: OR=1.59, 95% CI: 1.40-1.79; 2010’s: OR=1.37, 95% CI: 1.22-1.56. In a random effects model: 1990’s: OR=1.98, 95% CI: 1.08-3.62; 2000’s: OR=1.64, 95% CI: 1.23-2.19; 2010’s: OR=1.34, 95% CI: 1.03-1.73. Finally a meta-analysis stratified by each country was conducted. In fixed effect models, U.S: pooled OR =1.45, 95%CI: 1.34-1.57; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90. In random effects model, U.S: pooled OR=1.50, 95%CI: 1.25-1.80; China: pooled OR =4.67, 95%CI: 3.08-7.07; Cuba: pooled OR =1.43, 95%CI: 1.00-2.04; Italy: pooled OR =0.61, 95%CI: 0.13-2.90.CONCLUSIONS: the present meta-analysis provides the statistical evidence that the association between prostatitis and prostate cancer is significant. PMID:24391995

Olfactory-visual congruence effects stable across ages: yellow is warmer when it is pleasantly lemony.

PubMed

Guerdoux, Estelle; Trouillet, Raphaël; Brouillet, Denis

2014-07-01

This study aimed to examine the age-related differences in the olfactory-visual cross-correspondences and the extent to which they are moderated by the odors pleasantness. Sixty participants aged from 20- to 75- years (young, middle-aged and older adults) performed a priming task to explore the influence of six olfactory primes (lemon, orange, rose, thyme, mint and fish) on the categorization (cool vs. warm) of six subsequent color targets (yellow, orange, pink, malachite green, grass-green, and blue-gray). We tested mixed effects models. Response times were regressed on covariates models using both fixed effects (Groups of age, olfactory Pleasantness and multimodal Condition) and cross-random effects (Subject, Color and Odor). The random effects coding for Odor (p < .001) and Color (p = .001) were significant. There was a significant interaction effect ( p= .004) between Condition × Pleasantness, but not with Groups of age. The compatibility effect (i.e., when odors and colors were congruent, the targets processing were facilitated) was as much enhanced as the olfactory primes were pleasant. Cross-correspondences between olfaction and vision may be robust in aging. They should be considered alongside spatiotemporal but also emotional congruency.

Analysis of energy expenditure in diet-induced obese rats

PubMed Central

Assaad, Houssein; Yao, Kang; Tekwe, Carmen D.; Feng, Shuo; Bazer, Fuller W.; Zhou, Lan; Carroll, Raymond J.; Meininger, Cynthia J.; Wu, Guoyao

2014-01-01

Development of obesity in animals is affected by energy intake, dietary composition, and metabolism. Useful models for studying this metabolic problem are Sprague-Dawley rats fed low-fat (LF) or high-fat (HF) diets beginning at 28 days of age. Through experimental design, their dietary intakes of energy, protein, vitamins, and minerals per kg body weight (BW) do not differ in order to eliminate confounding factors in data interpretation. The 24-h energy expenditure of rats is measured using indirect calorimetry. A regression model is constructed to accurately predict BW gain based on diet, initial BW gain, and the principal component scores of respiratory quotient and heat production. Time-course data on metabolism (including energy expenditure) are analyzed using a mixed effect model that fits both fixed and random effects. Cluster analysis is employed to classify rats as normal-weight or obese. HF-fed rats are heavier than LF-fed rats, but rates of their heat production per kg non-fat mass do not differ. We conclude that metabolic conversion of dietary lipids into body fat primarily contributes to obesity in HF-fed rats. PMID:24896330

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Correlation of Vitamin D status and orthodontic-induced external apical root resorption.

PubMed

Tehranchi, Azita; Sadighnia, Azin; Younessian, Farnaz; Abdi, Amir H; Shirvani, Armin

2017-01-01

Adequate Vitamin D is essential for dental and skeletal health in children and adult. The purpose of this study was to assess the correlation of serum Vitamin D level with external-induced apical root resorption (EARR) following fixed orthodontic treatment. In this cross-sectional study, the prevalence of Vitamin D deficiency (defined by25-hydroxyvitamin-D) was determined in 34 patients (23.5% male; age range 12-23 years; mean age 16.63 ± 2.84) treated with fixed orthodontic treatment. Root resorption of four maxillary incisors was measured using before and after periapical radiographs (136 measured teeth) by means of a design-to-purpose software to optimize data collection. Teeth with a maximum percentage of root resorption (%EARR) were indicated as representative root resorption for each patient. A multiple linear regression model and Pearson correlation coefficient were used to assess the association of Vitamin D status and observed EARR. P < 0.05 was considered statistically significant. The Pearson coefficient between these two variables was determined about 0.15 ( P = 0.38). Regression analysis revealed that Vitamin D status of the patients demonstrated no significant statistical correlation with EARR, after adjustment of confounding variables using linear regression model ( P > 0.05). This study suggests that Vitamin D level is not among the clinical variables that are potential contributors for EARR. The prevalence of Vitamin D deficiency does not differ in patients with higher EARR. These data suggest the possibility that Vitamin D insufficiency may not contribute to the development of more apical root resorption although this remains to be confirmed by further longitudinal cohort studies.

Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models

ERIC Educational Resources Information Center

Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung

2015-01-01

Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…

Global Evidence on the Association between Cigarette Graphic Warning Labels and Cigarette Smoking Prevalence and Consumption

PubMed Central

Ngo, Anh; Cheng, Kai-Wen; Huang, Jidong; Chaloupka, Frank J.

2018-01-01

Background: In 2011, the courts ruled in favor of tobacco companies in preventing the implementation of graphic warning labels (GWLs) in the US, stating that FDA had not established the effectiveness of GWLs in reducing smoking. Methods: Data came from various sources: the WHO MPOWER package (GWLs, MPOWER policy measures, cigarette prices), Euromonitor International (smoking prevalence, cigarette consumption), and the World Bank database (countries’ demographic characteristics). The datasets were aggregated and linked using country and year identifiers. Fractional logit regressions and OLS regressions were applied to examine the associations between GWLs and smoking prevalence and cigarette consumption, controlling for MPOWER policy scores, cigarette prices, GDP per capita, unemployment, population aged 15–64 (%), aged 65 and over (%), year indicators, and country fixed effects. Results: GWLs were associated with a 0.9–3 percentage point decrease in adult smoking prevalence and were significantly associated with a reduction of 230–287 sticks in per capita cigarette consumption, compared to countries without GWLs. However, the association between GWLs and cigarette consumption became statistically insignificant once country indicators were included in the models. Conclusions: The implementation of GWLs may be associated with reduced cigarette smoking. PMID:29495581

Global Evidence on the Association between Cigarette Graphic Warning Labels and Cigarette Smoking Prevalence and Consumption.

PubMed

Ngo, Anh; Cheng, Kai-Wen; Shang, Ce; Huang, Jidong; Chaloupka, Frank J

2018-02-28

Background : In 2011, the courts ruled in favor of tobacco companies in preventing the implementation of graphic warning labels (GWLs) in the US, stating that FDA had not established the effectiveness of GWLs in reducing smoking. Methods : Data came from various sources: the WHO MPOWER package (GWLs, MPOWER policy measures, cigarette prices), Euromonitor International (smoking prevalence, cigarette consumption), and the World Bank database (countries' demographic characteristics). The datasets were aggregated and linked using country and year identifiers. Fractional logit regressions and OLS regressions were applied to examine the associations between GWLs and smoking prevalence and cigarette consumption, controlling for MPOWER policy scores, cigarette prices, GDP per capita, unemployment, population aged 15-64 (%), aged 65 and over (%), year indicators, and country fixed effects. Results : GWLs were associated with a 0.9-3 percentage point decrease in adult smoking prevalence and were significantly associated with a reduction of 230-287 sticks in per capita cigarette consumption, compared to countries without GWLs. However, the association between GWLs and cigarette consumption became statistically insignificant once country indicators were included in the models. Conclusions : The implementation of GWLs may be associated with reduced cigarette smoking.

Evaluating lake phytoplanton response to human disturbance and climate change using satellite imagery

NASA Astrophysics Data System (ADS)

Novitski, Linda Nicole

Accurate and cost-effective assessment of water quality is necessary for proper management and restoration of inland water bodies susceptible to algal bloom conditions. Landsat and MODIS satellite images were used to create chlorophyll and Secchi depth predictive models for algal assessment of Great Lakes and other lakes of the United States. Boosted regression tree (BRT) models using satellite imagery are both easy to use and can have high predictive performance. BRT models inferred chlorophyll and Secchi depth more accurately than linear regression models for all study locations. Inferred chlorophyll of inner Saginaw Bay was subsequently used in ecological models to help understand the ecological drivers of algal blooms in this ecosystem. For small lakes (non-Great Lakes), the best national Landsat model for ln-transformed chlorophyll was the BRT model and had a cross-validation R 2 of 0.44 and a 0.76 ln-transformed mug/L RMSE. The best national Landsat model for Secchi depth was also a BRT model that had an adjusted R 2 of 0.52 and a 0.80 m RMSE. We assessed the applicability of the national chlorophyll model for ecological analysis by comparing the total phosphorus- chlorophyll relationship with chlorophyll determined from sampling or remote sensing, which showed the total phosphorus- chlorophyll relationship had an adjusted R2 = 0.58 and 1.02 ln-transformed microg/L RMSE with sampled chlorophyll versus an adjusted R2 = 0.56 and 1.04 ln-transformed mug/L RMSE with chlorophyll determined by the boosted regression tree remote sensing model. For Great Lakes models, the MODIS BRT model predicted chlorophyll most accurately of the three BRT models and compared well to other models in the literature. BRT models for Landsat ETM+ and TM more accurately predicted chlorophyll than the MSS model and all Landsat models had favorable results when compared to the literature. BRT chlorophyll predictive models are useful in helping to understand historical, long-term chlorophyll trends and to inform us of how climate change may alter ecosystems in the future. In inner Saginaw Bay, annual average and upper quartile Landsat-derived chlorophyll decreased from 7.44 to 6.62 and 8.38 to 7.38 mug/L between 1973-1982, and annual upper quartile of 8-day phosphorus loads increased from 5.29 to 6.79 kg between 1973-2012. Simple linear and multiple regression models and Wilcoxon rank test results for MODIS and Landsat-derived chlorophyll indicate that distance from the Saginaw River mouth influences chlorophyll concentration in Saginaw Bay; Landsat-derived surface water temperature and phosphorus loads to a lesser extent. Mixed-effect models for MODIS and Landsat-derived chlorophyll were related to chlorophyll better than simple linear or multiple regressions, with random effects of pixel and sample date contributing substantially to predictive power (NSE=0.35-70), though phosphorus loads, distance to Saginaw River mouth, and water were significant fixed effects in most models. Water quality changes in Saginaw Bay between 1972-2012 were influenced by phosphorus loading and distance to the Saginaw River's mouth. Landsat and MODIS imagery are complementary platforms because of the long history of Landsat operation and the finer spectral resolution and image frequency of MODIS. Remote sensing water quality assessment tools can be valuable for limnological study, ecological assessment, and water resource management.

Physiologic Growth Hormone-Replacement Therapy and Craniopharyngioma Recurrence in Pediatric Patients: A Meta-Analysis.

PubMed

Alotaibi, Nawaf M; Noormohamed, Nadia; Cote, David J; Alharthi, Salman; Doucette, Joanne; Zaidi, Hasan A; Mekary, Rania A; Smith, Timothy R

2018-01-01

A systematic review and meta-analysis were conducted to examine the effect of growth hormone-replacement therapy (GHRT) on the recurrence of craniopharyngioma in children. PubMed, Embase, and Cochrane databases were searched through April 2017 for studies that evaluated the effect of GHRT on the recurrence of pediatric craniopharyngioma. Pooled effect estimates were calculated with fixed- and random-effects models. Ten studies (n = 3487 patients) met all inclusion criteria, including 2 retrospective cohorts and 8 case series. Overall, 3436 pediatric patients were treated with GHRT after surgery and 51 were not. Using the fixed effect model, we found that the overall craniopharyngioma recurrence rate was lower among children who were treated by GHRT (10.9%; 95% confidence interval 9.80%-12.1%; I 2  = 89.1%; P for heterogeneity <0.01; n = 10 groups) compared with those who were not (35.2%; 95% confidence interval 23.1%-49.6%; I 2  = 61.7%; P for heterogeneity = 0.11; n = 3); the P value comparing the 2 groups was <0.01. Among patients who were treated with GHRT, subgroup analysis revealed that there was a greater prevalence of craniopharyngioma recurrence among studies conducted outside the United States (P < 0.01), single-center studies (P < 0.01), lower impact factor studies (P = 0.03), or studies with a lower quality rating (P = 0.01). Using the random-effects model, we found that the results were not materially different except for when stratifying by GHRT, impact factor, or study quality; this led to nonsignificant differences. Both Begg's rank correlation test (P = 0.7) and Egger's linear regression test (P = 0.06) indicated no publication bias. This meta-analysis demonstrated a lower recurrence rate of craniopharyngioma among children treated with GHRT than those who were not. Copyright © 2017 Elsevier Inc. All rights reserved.

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

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

The effect of internal possession laws on underage drinking among high school students: a 12-state analysis.

PubMed

Disney, Lynn D; LaVallee, Robin A; Yi, Hsiao-Ye

2013-06-01

We assessed the effect of internal possession (IP) laws, which allow law enforcement to charge underage drinkers with alcohol possession if they have ingested alcohol, on underage drinking behaviors. We examined Youth Risk Behavior Survey (YRBS) data from 12 states with IP laws and with YRBS data before and after each law's implementation. We used logistic regression models with fixed effects for state to assess the effects of IP laws on drinking and binge drinking among high school students. Implementation of IP laws is associated with reductions in the odds of past-month drinking. This reduction was bigger among male than among female adolescents (27% vs 15%) and only significant among younger students aged 14 and 15 years (15% and 11%, respectively). Male adolescents also reported a significant reduction (24%) in the odds of past-month binge drinking under IP laws. These findings suggest that IP laws are effective in reducing underage drinking, particularly among younger adolescents.

The Effect of Internal Possession Laws on Underage Drinking Among High School Students: A 12-State Analysis

PubMed Central

Disney, Lynn D.; Yi, Hsiao-ye

2013-01-01

Objectives. We assessed the effect of internal possession (IP) laws, which allow law enforcement to charge underage drinkers with alcohol possession if they have ingested alcohol, on underage drinking behaviors. Methods. We examined Youth Risk Behavior Survey (YRBS) data from 12 states with IP laws and with YRBS data before and after each law’s implementation. We used logistic regression models with fixed effects for state to assess the effects of IP laws on drinking and binge drinking among high school students. Results. Implementation of IP laws is associated with reductions in the odds of past-month drinking. This reduction was bigger among male than among female adolescents (27% vs 15%) and only significant among younger students aged 14 and 15 years (15% and 11%, respectively). Male adolescents also reported a significant reduction (24%) in the odds of past-month binge drinking under IP laws. Conclusions. These findings suggest that IP laws are effective in reducing underage drinking, particularly among younger adolescents. PMID:23597385

A population-based spatio-temporal analysis of Clostridium difficile infection in Queensland, Australia over a 10-year period.

PubMed

Furuya-Kanamori, Luis; Robson, Jenny; Soares Magalhães, Ricardo J; Yakob, Laith; McKenzie, Samantha J; Paterson, David L; Riley, Thomas V; Clements, Archie C A

2014-11-01

To identify the spatio-temporal patterns and environmental factors associated with Clostridium difficile infection (CDI) in Queensland, Australia. Data from patients tested for CDI were collected from 392 postcodes across Queensland between May 2003 and December 2012. A binomial logistic regression model, with CDI status as the outcome, was built in a Bayesian framework, incorporating fixed effects for sex, age, source of the sample (healthcare facility or community), elevation, rainfall, land surface temperature, seasons of the year, time in months and spatially unstructured random effects at the postcode level. C. difficile was identified in 13.1% of the samples, the proportion significantly increased over the study period from 5.9% in 2003 to 18.8% in 2012. CDI peaked in summer (14.6%) and was at its lowest in autumn (10.1%). Other factors significantly associated with CDI included female sex (OR: 1.08; 95%CI: 1.01-1.14), community source samples (OR: 1.12; 95%CI: 1.05-1.20), and higher rainfall (OR: 1.09; 95%CI: 1.02-1.17). There was no significant spatial variation in CDI after accounting for the fixed effects in the model. There was an increasing annual trend in CDI in Queensland from 2003 to 2012. Peaks of CDI were found in summer (December-February), which is at odds with the current epidemiological pattern described for northern hemisphere countries. Epidemiologically plausible explanations for this disparity require further investigation. Copyright © 2014 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Random Effects: Variance Is the Spice of Life.

PubMed

Jupiter, Daniel C

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

Employment status and mental health among persons with and without a disability: evidence from an Australian cohort study.

PubMed

Milner, A; LaMontagne, A D; Aitken, Z; Bentley, R; Kavanagh, A M

2014-11-01

Unemployment and economic inactivity are associated with worse mental health in the general population, but there is limited understanding of whether these relationships are different for those persons with mental or physical disabilities. The aim of this study was to assess whether there were differences in mental health by labour force status among persons with and without disabilities. Over eight annual waves of the Household, Income and Labour Dynamics in Australia (HILDA) survey, a total of 2379 people with disabilities and 11 417 people without disabilities were identified. Mental health using the Mental Component Summary (MCS) from the Short Form 36 was modelled as a function of labour force status using fixed-effects regression models to control for time invariant confounding. Differences between those with and without disabilities were assessed by including an interaction term in regression models. After finding evidence of effect modification, regression models were stratified by disability status. After adjustment, unemployment and economic inactivity were associated with a -1.85 (95% CI -2.96 to -0.73, p=0.001) and -2.66 (95% CI -3.46 to -1.86, p<0.001) reduction in scores of the MCS among those with a disability. For those without a disability, there were smaller declines associated with unemployment (-0.57, 95% CI -1.02 to -0.12, p=0.013) and economic inactivity (-0.34, 95% CI -0.64 to 0.05, p=0.022). These results suggest a greater reduction in mental health for those persons with disabilities who were unemployed or economically inactive than those who were employed. This highlights the value of employment for people with disabilities. 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.

Sickness absence and psychosocial job quality: an analysis from a longitudinal survey of working Australians, 2005-2012.

PubMed

Milner, Allison; Butterworth, Peter; Bentley, Rebecca; Kavanagh, Anne M; LaMontagne, Anthony D

2015-05-15

Sickness absence is associated with adverse health, organizational, and societal outcomes. Using data from a longitudinal cohort study of working Australians (the Household, Income and Labour Dynamics in Australia (HILDA) Survey), we examined the relationship between changes in individuals' overall psychosocial job quality and variation in sickness absence. The outcome variables were paid sickness absence (yes/no) and number of days of paid sickness absence in the past year (2005-2012). The main exposure variable was psychosocial job quality, measured using a psychosocial job quality index (levels of job control, demands and complexity, insecurity, and perceptions of unfair pay). Analysis was conducted using longitudinal fixed-effects logistic regression models and negative binomial regression models. There was a dose-response relationship between the number of psychosocial job stressors reported by an individual and the odds of paid sickness absence (1 adversity: odds ratio (OR) = 1.26, 95% confidence interval (CI): 1.09, 1.45 (P = 0.002); 2 adversities: OR = 1.28, 95% CI: 1.09, 1.51 (P = 0.002); ≥3 adversities: OR = 1.58, 95% CI: 1.29, 1.94 (P < 0.001)). The negative binomial regression models also indicated that respondents reported a greater number of days of sickness absence in response to worsening psychosocial job quality. These results suggest that workplace interventions aiming to improve the quality of work could help reduce sickness absence. © The Author 2015. 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.

Construction of robust dynamic genome-scale metabolic model structures of Saccharomyces cerevisiae through iterative re-parameterization.

PubMed

Sánchez, Benjamín J; Pérez-Correa, José R; Agosin, Eduardo

2014-09-01

Dynamic flux balance analysis (dFBA) has been widely employed in metabolic engineering to predict the effect of genetic modifications and environmental conditions in the cell׳s metabolism during dynamic cultures. However, the importance of the model parameters used in these methodologies has not been properly addressed. Here, we present a novel and simple procedure to identify dFBA parameters that are relevant for model calibration. The procedure uses metaheuristic optimization and pre/post-regression diagnostics, fixing iteratively the model parameters that do not have a significant role. We evaluated this protocol in a Saccharomyces cerevisiae dFBA framework calibrated for aerobic fed-batch and anaerobic batch cultivations. The model structures achieved have only significant, sensitive and uncorrelated parameters and are able to calibrate different experimental data. We show that consumption, suboptimal growth and production rates are more useful for calibrating dynamic S. cerevisiae metabolic models than Boolean gene expression rules, biomass requirements and ATP maintenance. Copyright © 2014 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

The Longitudinal Association Between Early Childhood Obesity and Fathers' Involvement in Caregiving and Decision-Making.

PubMed

Wong, Michelle S; Jones-Smith, Jessica C; Colantuoni, Elizabeth; Thorpe, Roland J; Bleich, Sara N; Chan, Kitty S

2017-10-01

Fathers have increased their involvement in child caregiving; however, their changing role in childhood obesity is understudied. This study assessed the longitudinal association between changes in obesity among children aged 2 to 4 years and changes in fathers' involvement with raising children. Longitudinal data from the Early Childhood Longitudinal Study-Birth Cohort were used to conduct child fixed-effects linear and logistic regression analyses to assess the association between changes in childhood obesity-related outcomes (sugar-sweetened beverage consumption, screen time, BMI z score, overweight/obesity, obesity) and fathers' involvement with raising children (caregiving and influencing child-related decisions). Fixed-effects models control for all time-invariant characteristics. Analyses were controlled for time-varying confounders, including child age, maternal and paternal employment, and family poverty status. Children whose fathers increased their frequency of taking children outside and involvement with physical childcare experienced a decrease in their odds of obesity from age 2 to age 4. Obesity-related outcomes were not associated with fathers' decision-making influence. Increases in fathers' involvement with some aspects of caregiving may be associated with lower odds of childhood obesity. Encouraging fathers to increase their involvement with raising children and including fathers in childhood obesity prevention efforts may help reduce obesity risk among young children. © 2017 The Obesity Society.

Hospital participation in Meaningful Use and racial disparities in readmissions.

PubMed

Unruh, Mark Aaron; Jung, Hye Young; Kaushal, Rainu; Vest, Joshua R

2018-01-01

To measure the impact of hospital participation in Meaningful Use (MU) on disparities in 30-day readmissions associated with race. A retrospective cohort study that compared the likelihood of 30-day readmission for Medicare beneficiaries discharged from hospitals participating in Stage 1 of MU with the likelihood of readmission for beneficiaries concurrently discharged from hospitals that were not participating in the initiative. Inpatient claims for 2,414,205 Medicare beneficiaries from Florida, New York, and Washington State were used as the primary data source. The study period (2009-2013) included at least 2 years of baseline data prior to each hospital initiating participation in MU. Estimates were derived with linear regression models that included hospital and time fixed effects. By including both hospital and time fixed effects, estimates were based on discharges from the same hospital in the same time period. MU participation among hospitals was not associated with a statistically significant change in readmissions for the broader Medicare population (percentage points [PP], 0.6; 95% CI, -0.2 to 1.4), but hospitals' participation in the initiative was associated with a lower likelihood of readmission for African American beneficiaries (PP, -0.9; 95% CI, -1.5 to -0.4). Hospital participation in MU reduced disparities in 30-day readmissions for African American Medicare beneficiaries.

Effects of response-independent stimuli on fixed-interval and fixed-ratio performance of rats: a model for stressful disruption of cyclical eating patterns.

PubMed

Reed, Phil

2011-03-01

Binge eating is often associated with stress-induced disruption of typical eating patterns. Three experiments were performed with the aim of developing a potential model for this effect by investigating the effect of presenting response-independent stimuli on rats' lever-pressing for food reinforcement during both fixed-interval (FI) and fixed-ratio (FR) schedules of reinforcement. In Experiment 1, a response-independent brief tone (500-ms, 105-dB, broadband, noisy signal, ranging up to 16 kHz, with spectral peaks at 3 and 500 Hz) disrupted the performance on an FI 60-s schedule. Responding with the response-independent tone was more vigorous than in the absence of the tone. This effect was replicated in Experiment 2 using a within-subject design, but no such effect was noted when a light was employed as a disrupter. In Experiment 3, a 500-ms tone, but not a light, had a similar effect on rats' performance on FR schedules. This tone-induced effect may represent a release from response-inhibition produced by an aversive event. The implications of these results for modeling binge eating are discussed.

ELASTIC NET FOR COX’S PROPORTIONAL HAZARDS MODEL WITH A SOLUTION PATH ALGORITHM

PubMed Central

Wu, Yichao

2012-01-01

For least squares regression, Efron et al. (2004) proposed an efficient solution path algorithm, the least angle regression (LAR). They showed that a slight modification of the LAR leads to the whole LASSO solution path. Both the LAR and LASSO solution paths are piecewise linear. Recently Wu (2011) extended the LAR to generalized linear models and the quasi-likelihood method. In this work we extend the LAR further to handle Cox’s proportional hazards model. The goal is to develop a solution path algorithm for the elastic net penalty (Zou and Hastie (2005)) in Cox’s proportional hazards model. This goal is achieved in two steps. First we extend the LAR to optimizing the log partial likelihood plus a fixed small ridge term. Then we define a path modification, which leads to the solution path of the elastic net regularized log partial likelihood. Our solution path is exact and piecewise determined by ordinary differential equation systems. PMID:23226932

The national stream quality accounting network: A flux-basedapproach to monitoring the water quality of large rivers

USGS Publications Warehouse

Hooper, R.P.; Aulenbach, Brent T.; Kelly, V.J.

2001-01-01

Estimating the annual mass flux at a network of fixed stations is one approach to characterizing water quality of large rivers. The interpretive context provided by annual flux includes identifying source and sink areas for constituents and estimating the loadings to receiving waters, such as reservoirs or the ocean. Since 1995, the US Geological Survey's National Stream Quality Accounting Network (NASQAN) has employed this approach at a network of 39 stations in four of the largest river basins of the USA: The Mississippi, the Columbia, the Colorado and the Rio Grande. In this paper, the design of NASQAN is described and its effectiveness at characterizing the water quality of these rivers is evaluated using data from the first 3 years of operation. A broad range of constituents was measured by NASQAN, including trace organic and inorganic chemicals, major ions, sediment and nutrients. Where possible, a regression model relating concentration to discharge and season was used to interpolate between chemical observations for flux estimation. For water-quality network design, the most important finding from NASQAN was the importance of having a specific objective (that is, estimating annual mass flux) and, from that, an explicitly stated data analysis strategy, namely the use of regression models to interpolate between observations. The use of such models aided in the design of sampling strategy and provided a context for data review. The regression models essentially form null hypotheses for concentration variation that can be evaluated by the observed data. The feedback between network operation and data collection established by the hypothesis tests places the water-quality network on a firm scientific footing.

Worklife expectancies of fixed-term Finnish employees in 1997-2006.

PubMed

Nurminen, Markku

2008-04-01

Fixed-term employment is prevalent in the Finnish labor force. This form of employment contract is marked by fragmentary work periods, demands for flexibility in workhours, and concern for multiple insecurities. A nonpermanent employee may also incur adverse health consequences. Yet there exist no exact statistics on the duration of fixed-term employment. This paper estimated the future duration of the time that a Finn is expected to be engaged in irregular work. Multistate regression modeling and stochastic analysis were applied to aggregated data from surveys conducted among the labor force by Statistics Finland in 1997-2006. In 2006, a Finnish male was expected to work a total of 3.8 years in fixed-term employment, combined over consecutive or separate time spans; this time amounts to 8% of his remaining work career from entry into the work force until final retirement. For a woman the expectancy was greater, 6.5 years or 13%. For the age interval 20-29 years, the total was 16% for men and 23% for women. The type and duration of employment is influenced by security factors and economic cycles, both of which affect men and women differently. Over the past decade, fixed-term employment increased consistently in the female labor contingent, and it was more pronounced during economic slowdowns. This labor market development calls for standards for flexibility and guarantees for security in the fragmented future worklives of fixed-term employees.

Beneficial Combination of Lacosamide with Retigabine in Experimental Animals: An Isobolographic Analysis.

PubMed

Luszczki, Jarogniew J; Zagaja, Mirosław; Miziak, Barbara; Kondrat-Wrobel, Maria W; Zaluska, Katarzyna; Wroblewska-Luczka, Paula; Adamczuk, Piotr; Czuczwar, Stanislaw J; Florek-Luszczki, Magdalena

2018-01-01

To isobolographically determine the types of interactions that occur between retigabine and lacosamide (LCM; two third-generation antiepileptic drugs) with respect to their anticonvulsant activity and acute adverse effects (sedation) in the maximal electroshock-induced seizures (MES) and chimney test (motor performance) in adult male Swiss mice. Type I isobolographic analysis for nonparallel dose-response effects for the combination of retigabine with LCM (at the fixed-ratio of 1:1) in both the MES and chimney test in mice was performed. Brain concentrations of retigabine and LCM were measured by high-pressure liquid chromatography (HPLC) to characterize any pharmacokinetic interactions occurring when combining these drugs. Linear regression analysis revealed that retigabine had its dose-response effect line nonparallel to that of LCM in both the MES and chimney tests. The type I isobolographic analysis illustrated that retigabine combined with LCM (fixed-ratio of 1:1) exerted an additive interaction in the mouse MES model and sub-additivity (antagonism) in the chimney test. With HPLC, retigabine and LCM did not mutually change their total brain concentrations, thereby confirming the pharmacodynamic nature of the interaction. LCM combined with retigabine possesses a beneficial preclinical profile (benefit index ranged from 2.07 to 2.50) and this 2-drug combination is worth recommending as treatment plan to patients with pharmacoresistant epilepsy. © 2017 S. Karger AG, Basel.

Modeling and simulation of deformation of hydrogels responding to electric stimulus.

PubMed

Li, Hua; Luo, Rongmo; Lam, K Y

2007-01-01

A model for simulation of pH-sensitive hydrogels is refined in this paper to extend its application to electric-sensitive hydrogels, termed the refined multi-effect-coupling electric-stimulus (rMECe) model. By reformulation of the fixed-charge density and consideration of finite deformation, the rMECe model is able to predict the responsive deformations of the hydrogels when they are immersed in a bath solution subject to externally applied electric field. The rMECe model consists of nonlinear partial differential governing equations with chemo-electro-mechanical coupling effects and the fixed-charge density with electric-field effect. By comparison between simulation and experiment extracted from literature, the model is verified to be accurate and stable. The rMECe model performs quantitatively for deformation analysis of the electric-sensitive hydrogels. The influences of several physical parameters, including the externally applied electric voltage, initial fixed-charge density, hydrogel strip thickness, ionic strength and valence of surrounding solution, are discussed in detail on the displacement and average curvature of the hydrogels.

Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models

DOE PAGES

Andrews, Timothy; Gregory, Jonathan M.; Webb, Mark J.; ...

2012-05-15

We quantify forcing and feedbacks across available CMIP5 coupled atmosphere-ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon dioxide concentration. This is the first application of the linear forcing-feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1–4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top-of-atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects overmore » the ocean and is consistent with independent estimates of forcing using fixed sea-surface temperature methods. Moreover, we suggest that future research should focus more on understanding transient climate change, including any time-scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing.« less

A prospective 10-year study of metal ceramic single crowns and fixed dental prosthesis retainers in private practice settings.

PubMed

Reitemeier, Bernd; Hänsel, Kristina; Kastner, Christian; Weber, Anke; Walter, Michael H

2013-03-01

Metal ceramic restorations are widely used in prosthodontics, but long-term data on their clinical performance in private practice settings based on prospective trials are sparse. This clinical trial was designed to provide realistic long-term survival rates for different outcomes related to tooth loss, crown loss, and metal ceramic defect. Ninety-five participants were provided with 190 noble metal ceramic single crowns and 138 participants with 276 fixed dental prosthesis retainer crowns on vital posterior teeth. Follow-up examinations were scheduled 2 weeks after insertion, annually up to 8 years, and after 10 years. Kaplan-Meier survival analyses, Mantel-Cox logrank tests, and Cox regression analyses were conducted. Because of variations in the time of the last examinations, the maximum observation period was 12.1 years. For the primary outcome 'loss of crown or tooth', the Kaplan-Meier survival rate was 94.3% ±1.8% (standard error) at 8.0 years (last outcome event) for single crowns and 94.4% ±1.5% at 11.0 years for fixed dental prosthesis retainer crowns. The difference between the survival functions was not significant (P>.05). For the secondary outcome 'metal ceramic defect', the survival rate was 88.8% ±3.2% at 11.0 years for single crowns and 81.7% ±3.5% at 11.0 years for fixed dental prosthesis retainer crowns. In Cox regression models, the only significant covariates for the outcome event 'metal ceramic defect' were bruxism in the medical history (single crowns) and signs and symptoms of bruxism (fixed dental prosthesis retainer crowns) with hazard ratios of 3.065 (95% CI 1.063 - 8.832) and 2.554 (95% CI 1.307 - 4.992). Metal ceramic crowns provided in private practice settings show good longevity. Bruxism appears to indicate a risk for metal ceramic defects. Copyright © 2013 The Editorial Council of the Journal of Prosthetic Dentistry. Published by Mosby, Inc. All rights reserved.

Economic effect of restaurant smoking restrictions on restaurant business in Massachusetts, 1992 to 1998.

PubMed

Bartosch, William J; Pope, G C

2002-06-01

To determine if restaurant business declines or improves after the implementation of restrictive restaurant smoking policies. Analysis used a pre/post-quasi-experimental design that compared town meals tax receipts before and after the imposition of highly restrictive restaurant smoking policies in adopting versus non-adopting communities. The effect of restaurant smoking policies was estimated using a fixed effects regression model, entering a panel of 84 months of data for the 239 towns in the study. A separate model estimated the effect of restaurant smoking policies on establishments that served alcohol. Change in the trend in meals tax revenue (adjusted for population) following the implementation of highly restrictive restaurant smoking policies. The local adoption of restrictive restaurant smoking policies did not lead to a measurable deviation from the strong positive trend in revenue between 1992 and 1998 that restaurants in Massachusetts experienced. Controlling for other less restrictive restaurant smoking policies did not change this finding. Similar results were found for only those establishments that served alcoholic beverages. Highly restrictive restaurant smoking policies do not have a significant effect on a community's level of meal receipts, indicating that claims of community wide restaurant business decline under such policies are unwarranted.

The Effects of Diagnostic Definitions in Claims Data on Healthcare Cost Estimates: Evidence from a Large-Scale Panel Data Analysis of Diabetes Care in Japan.

PubMed

Fukuda, Haruhisa; Ikeda, Shunya; Shiroiwa, Takeru; Fukuda, Takashi

2016-10-01

Inaccurate estimates of diabetes-related healthcare costs can undermine the efficiency of resource allocation for diabetes care. The quantification of these costs using claims data may be affected by the method for defining diagnoses. The aims were to use panel data analysis to estimate diabetes-related healthcare costs and to comparatively evaluate the effects of diagnostic definitions on cost estimates. Monthly panel data analysis of Japanese claims data. The study included a maximum of 141,673 patients with type 2 diabetes who received treatment between 2005 and 2013. Additional healthcare costs associated with diabetes and diabetes-related complications were estimated for various diagnostic definition methods using fixed-effects panel data regression models. The average follow-up period per patient ranged from 49.4 to 52.3 months. The number of patients identified as having type 2 diabetes varied widely among the diagnostic definition methods, ranging from 14,743 patients to 141,673 patients. The fixed-effects models showed that the additional costs per patient per month associated with diabetes ranged from US$180 [95 % confidence interval (CI) 178-181] to US$223 (95 % CI 221-224). When the diagnostic definition excluded rule-out diagnoses, the diabetes-related complications associated with higher additional healthcare costs were ischemic heart disease with surgery (US$13,595; 95 % CI 13,568-13,622), neuropathy/extremity disease with surgery (US$4594; 95 % CI 3979-5208), and diabetic nephropathy with dialysis (US$3689; 95 % CI 3667-3711). Diabetes-related healthcare costs are sensitive to diagnostic definition methods. Determining appropriate diagnostic definitions can further advance healthcare cost research for diabetes and its applications in healthcare policies.

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.

Model Specifications for Estimating Labor Market Returns to Associate Degrees: How Robust Are Fixed Effects Estimates? A CAPSEE Working Paper

ERIC Educational Resources Information Center

Belfield, Clive; Bailey, Thomas

2017-01-01

Recently, studies have adopted fixed effects modeling to identify the returns to college. This method has the advantage over ordinary least squares estimates in that unobservable, individual-level characteristics that may bias the estimated returns are differenced out. But the method requires extensive longitudinal data and involves complex…

Using Multisite Experiments to Study Cross-Site Variation in Treatment Effects: A Hybrid Approach with Fixed Intercepts and A Random Treatment Coefficient

ERIC Educational Resources Information Center

Bloom, Howard S.; Raudenbush, Stephen W.; Weiss, Michael J.; Porter, Kristin

2017-01-01

The present article considers a fundamental question in evaluation research: "By how much do program effects vary across sites?" The article first presents a theoretical model of cross-site impact variation and a related estimation model with a random treatment coefficient and fixed site-specific intercepts. This approach eliminates…

Association between gingivitis and anterior gingival enlargement in subjects undergoing fixed orthodontic treatment.

PubMed

Zanatta, Fabricio Batistin; Ardenghi, Thiago Machado; Antoniazzi, Raquel Pippi; Pinto, Tatiana Militz Perrone; Rösing, Cassiano Kuchenbecker

2014-01-01

The aim of this study was to investigate the association among gingival enlargement (GE), periodontal conditions and socio-demographic characteristics in subjects undergoing fixed orthodontic treatment. A sample of 330 patients undergoing fixed orthodontic treatment for at least 6 months were examined by a single calibrated examiner for plaque and gingival indexes, probing pocket depth, clinical attachment loss and gingival enlargement. Socio-economic background, orthodontic treatment duration and use of dental floss were assessed by oral interviews. Associations were assessed by means of unadjusted and adjusted Poisson's regression models. The presence of gingival bleeding (RR 1.01; 95% CI 1.00-1.01) and excess resin around brackets (RR 1.02; 95% CI 1.02-1.03) were associated with an increase in GE. No associations were found between socio-demographic characteristics and GE. Proximal anterior gingival bleeding and excess resin around brackets are associated with higher levels of anterior gingival enlargement in subjects under orthodontic treatment.

Association between gingivitis and anterior gingival enlargement in subjects undergoing fixed orthodontic treatment

PubMed Central

Zanatta, Fabricio Batistin; Ardenghi, Thiago Machado; Antoniazzi, Raquel Pippi; Pinto, Tatiana Militz Perrone; Rösing, Cassiano Kuchenbecker

2014-01-01

Objective The aim of this study was to investigate the association among gingival enlargement (GE), periodontal conditions and socio-demographic characteristics in subjects undergoing fixed orthodontic treatment. Methods A sample of 330 patients undergoing fixed orthodontic treatment for at least 6 months were examined by a single calibrated examiner for plaque and gingival indexes, probing pocket depth, clinical attachment loss and gingival enlargement. Socio-economic background, orthodontic treatment duration and use of dental floss were assessed by oral interviews. Associations were assessed by means of unadjusted and adjusted Poisson's regression models. Results The presence of gingival bleeding (RR 1.01; 95% CI 1.00-1.01) and excess resin around brackets (RR 1.02; 95% CI 1.02-1.03) were associated with an increase in GE. No associations were found between socio-demographic characteristics and GE. Conclusion Proximal anterior gingival bleeding and excess resin around brackets are associated with higher levels of anterior gingival enlargement in subjects under orthodontic treatment. PMID:25162567

Predator-prey models with component Allee effect for predator reproduction.

PubMed

Terry, Alan J

2015-12-01

We present four predator-prey models with component Allee effect for predator reproduction. Using numerical simulation results for our models, we describe how the customary definitions of component and demographic Allee effects, which work well for single species models, can be extended to predators in predator-prey models by assuming that the prey population is held fixed. We also find that when the prey population is not held fixed, then these customary definitions may lead to conceptual problems. After this discussion of definitions, we explore our four models, analytically and numerically. Each of our models has a fixed point that represents predator extinction, which is always locally stable. We prove that the predator will always die out either if the initial predator population is sufficiently small or if the initial prey population is sufficiently small. Through numerical simulations, we explore co-existence fixed points. In addition, we demonstrate, by simulation, the existence of a stable limit cycle in one of our models. Finally, we derive analytical conditions for a co-existence trapping region in three of our models, and show that the fourth model cannot possess a particular kind of co-existence trapping region. We punctuate our results with comments on their real-world implications; in particular, we mention the possibility of prey resurgence from mortality events, and the possibility of failure in a biological pest control program.

Developing spatial inequalities in carbon appropriation: a sociological analysis of changing local emissions across the United States.

PubMed

Elliott, James R; Clement, Matthew Thomas

2015-05-01

This study examines an overlooked dynamic in sociological research on greenhouse gas emissions: how local areas appropriate the global carbon cycle for use and exchange purposes as they develop. Drawing on theories of place and space, we hypothesize that development differentially drives and spatially decouples use- and exchange-oriented emissions at the local level. To test our hypotheses, we integrate longitudinal, county-level data on residential and industrial emissions from the Vulcan Project with demographic, economic and environmental data from the U.S. Census Bureau and National Land Change Database. Results from spatial regression models with two-way fixed-effects indicate that alongside innovations and efficiencies capable of reducing environmentally harmful effects of development comes a spatial disarticulation between carbon-intensive production and consumption within as well as across societies. Implications for existing theory, methods and policy are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

When patients govern: federal grant funding and uncompensated care at federally qualified health centers.

PubMed

Wright, Brad; Ricketts, Thomas C

2013-05-01

To determine if the proportion of consumers on federally qualified health center (FQHC) governing boards is associated with their use of federal grant funds to provide uncompensated care. Using FQHC data from the Uniform Data System, county-level data from the Area Resource File and governing board data from FQHC grant applications, the uncompensated care an FQHC provides relative to the amount of its federal funding is modeled as a function of board and executive committee composition using fixed-effects regression with FQHC and county-level controls. Consumer governance does not predict how much uncompensated care an FQHC provides relative to the size of its federal grant. Rather, the proportion of an FQHC's patient-mix that is uninsured drives uncompensated care provision. Aside from a small executive committee effect, consumer governance does not influence FQHCs' provision of uncompensated care. More work is needed to understand the role of consumer governance.

The Marriage Wealth Premium Revisited: Gender Disparities and Within-Individual Changes in Personal Wealth in Germany.

PubMed

Lersch, Philipp M

2017-06-01

This study examines the association between marriage and economic wealth of women and men. Going beyond previous research that focused on household wealth, I examine personal wealth, which allows identifying gender disparities in the association between marriage and wealth. Using unique data from the German Socio-Economic Panel Study (2002, 2007, and 2012), I apply random-effects and fixed-effects regression models to test my expectations. I find that both women and men experience substantial marriage wealth premiums not only in household wealth but also in personal wealth. However, I do not find consistent evidence for gender disparities in these general marriage premiums. Additional analyses indicate, however, that women's marriage premiums are substantially lower than men's premiums in older cohorts and when only nonhousing wealth is considered. Overall, this study provides new evidence that women and men gain unequally in their wealth attainment through marriage.

Globalization and suicide: an ecological study across five regions of the world.

PubMed

Milner, Allison; McClure, Rod; De Leo, Diego

2012-01-01

The impact of globalization on health is recognized to be influenced by country and regional-level factors. This study aimed to investigate the possible relationship between globalization and suicide in five world regions. An index measure of globalization was developed at the country level over 1980 to 2006. The association between the index and sex specific suicide rates was tested using a fixed-effect regression model. Over time, the globalization index seemed to be associated with increased suicide rates in Asia and the Eastern European/Baltic region. In contrast, it was associated with decreased rates in Scandinavia. There was no significant relationship between globalization and suicide in Southern and Western Europe. The effects of globalization could be determined by specific regional (i.e., cultural and societal) factors. Identification of these mediators might provide opportunities to protect countries from the adverse impacts of globalization.

The influence of (public) health expenditure on longevity.

PubMed

Aísa, Rosa; Clemente, Jesús; Pueyo, Fernando

2014-10-01

We report new evidence on the contribution of health expenditure to increasing life expectancy in OECD countries, differentiating the effects of public and private health expenditures. A theoretical model is presented and estimated though a cross-country fixed effects multiple regression analysis for a sample of OECD countries over the period 1980-2000. Although the effect of aggregate health expenditure is not conclusive, public health expenditure plays a significant role in enhancing longevity. However, its influence diminishes as the size of the public health sector on GDP expands, reaching a maximum around the 8 %. With the influence of public health expenditure being positive, the ambiguous effect of the aggregate expenditure suggests that the weight of public and private health sectors matters, the second having a lower impact on longevity. This might explain the poor evolution of the life expectancy in countries with a high amount of private resources devoted to health. In such cases, an extension of public services could give rise to a better outcome from the overall health investment.

Unemployment transitions and self-rated health in Europe: A longitudinal analysis of EU-SILC from 2008 to 2011.

PubMed

Tøge, Anne Grete; Blekesaune, Morten

2015-10-01

The Great Recession of 2008 has led to elevated unemployment in Europe and thereby revitalised the question of causal health effects of unemployment. This article applies fixed effects regression models to longitudinal panel data drawn from the European Union Statistics on Income and Living Conditions for 28 European countries from 2008 to 2011, in order to investigate changes in self-rated health around the event of becoming unemployed. The results show that the correlation between unemployment and health is partly due to a decrease in self-rated health as people enter unemployment. Such health changes vary by country of domicile, and by individual age; older workers have a steeper decline than younger workers. Health changes after the unemployment spell reveal no indication of adverse health effects of unemployment duration. Overall, this study indicates some adverse health effects of unemployment in Europe--predominantly among older workers. Copyright © 2015 Elsevier Ltd. All rights reserved.

Efficacy and safety of nebivolol and valsartan as fixed-dose combination in hypertension: a randomised, multicentre study.

PubMed

Giles, Thomas D; Weber, Michael A; Basile, Jan; Gradman, Alan H; Bharucha, David B; Chen, Wei; Pattathil, Manoj

2014-05-31

The fixed-dose combination of any two antihypertensive drugs from different drug classes is typically more effective in reducing blood pressure than a dose increase of component monotherapy. We assessed the efficacy and safety of a fixed-dose combination of a vasodilating β blocker (nebivolol) and an angiotensin II receptor blocker (valsartan) in adults with hypertension. We did an 8-week, phase 3, multicentre, randomised, double-blind, placebo-controlled, parallel-group trial at 401 US sites. Participants (age ≥18 years) with hypertension but with blood pressure less than 180/110 mm Hg were randomly assigned (2:2:2:2:2:2:2:1) by a 24-h interactive web response system in blocks of 15 to 4 weeks of double-blind treatment with nebivolol and valsartan fixed-dose combination (5 and 80 mg/day, 5 and 160 mg/day, or 10 and 160 mg/day), nebivolol (5 mg/day or 20 mg/day), valsartan (80 mg/day or 160 mg/day), or placebo. Doses were doubled in weeks 5-8; results are reported according to the final dose. Participants and research staff were masked to treatment allocation. The primary and key secondary endpoints were changes from baseline to week 8 in diastolic and systolic blood pressure, respectively. The primary statistical comparison was between the highest fixed-dose combination dose and the highest monotherapy doses; lower doses were then compared if this comparison was positive (Hochberg method for multiple testing). Efficacy analyses were by intention to treat. Safety assessments included monitoring of adverse events. Continuous efficacy parameters were analysed using an ANCOVA model; binary outcomes were analysed using a logistic regression model. This study is registered with ClinicalTrials.gov, NCT01508026. Between Jan 6, 2012, and March 15, 2013, 4161 patients were randomly assigned (277 to placebo and 554-555 to each active comparator group), 4118 of whom were included in the primary analysis. At week 8, the fixed-dose combination 20 and 320 mg/day group had significantly greater reductions in diastolic blood pressure from baseline than both nebivolol 40 mg/day (least-squares mean difference -1·2 mm Hg, 95% CI -2·3 to -0·1; p=0·030) and valsartan 320 mg/day (-4·4 mm Hg, -5·4 to -3·3; p<0·0001); all other comparisons were also significant, favouring the fixed-dose combinations (all p<0·0001). All systolic blood pressure comparisons were also significant (all p<0·01). At least one treatment-emergent adverse event was experienced by 30-36% of participants in each group. Nebivolol and valsartan fixed-dose combination is an effective and well-tolerated treatment option for patients with hypertension. Forest Research Institute. Copyright © 2014 Elsevier Ltd. All rights reserved.

Short communication: Principal components and factor analytic models for test-day milk yield in Brazilian Holstein cattle.

PubMed

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

2012-04-01

A total of 46,089 individual monthly test-day (TD) milk yields (10 test-days), from 7,331 complete first lactations of Holstein cattle were analyzed. A standard multivariate analysis (MV), reduced rank analyses fitting the first 2, 3, and 4 genetic principal components (PC2, PC3, PC4), and analyses that fitted a factor analytic structure considering 2, 3, and 4 factors (FAS2, FAS3, FAS4), were carried out. The models included the random animal genetic effect and fixed effects of the contemporary groups (herd-year-month of test-day), age of cow (linear and quadratic effects), and days in milk (linear effect). The residual covariance matrix was assumed to have full rank. Moreover, 2 random regression models were applied. Variance components were estimated by restricted maximum likelihood method. The heritability estimates ranged from 0.11 to 0.24. The genetic correlation estimates between TD obtained with the PC2 model were higher than those obtained with the MV model, especially on adjacent test-days at the end of lactation close to unity. The results indicate that for the data considered in this study, only 2 principal components are required to summarize the bulk of genetic variation among the 10 traits. Copyright © 2012 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Walkability and cardiometabolic risk factors: Cross-sectional and longitudinal associations from the Multi-Ethnic Study of Atherosclerosis.

PubMed

Braun, Lindsay M; Rodríguez, Daniel A; Evenson, Kelly R; Hirsch, Jana A; Moore, Kari A; Diez Roux, Ana V

2016-05-01

We used data from 3227 older adults in the Multi-Ethnic Study of Atherosclerosis (2004-2012) to explore cross-sectional and longitudinal associations between walkability and cardiometabolic risk factors. In cross-sectional analyses, linear regression was used to estimate associations of Street Smart Walk Score® with glucose, triglycerides, HDL and LDL cholesterol, systolic and diastolic blood pressure, and waist circumference, while logistic regression was used to estimate associations with odds of metabolic syndrome. Econometric fixed effects models were used to estimate longitudinal associations of changes in walkability with changes in each risk factor among participants who moved residential locations between 2004 and 2012 (n=583). Most cross-sectional and longitudinal associations were small and statistically non-significant. We found limited evidence that higher walkability was cross-sectionally associated with lower blood pressure but that increases in walkability were associated with increases in triglycerides and blood pressure over time. Further research over longer time periods is needed to understand the potential for built environment interventions to improve cardiometabolic health. Copyright © 2016 Elsevier Ltd. All rights reserved.

Extensively Parameterized Mutation-Selection Models Reliably Capture Site-Specific Selective Constraint.

PubMed

Spielman, Stephanie J; Wilke, Claus O

2016-11-01

The mutation-selection model of coding sequence evolution has received renewed attention for its use in estimating site-specific amino acid propensities and selection coefficient distributions. Two computationally tractable mutation-selection inference frameworks have been introduced: One framework employs a fixed-effects, highly parameterized maximum likelihood approach, whereas the other employs a random-effects Bayesian Dirichlet Process approach. While both implementations follow the same model, they appear to make distinct predictions about the distribution of selection coefficients. The fixed-effects framework estimates a large proportion of highly deleterious substitutions, whereas the random-effects framework estimates that all substitutions are either nearly neutral or weakly deleterious. It remains unknown, however, how accurately each method infers evolutionary constraints at individual sites. Indeed, selection coefficient distributions pool all site-specific inferences, thereby obscuring a precise assessment of site-specific estimates. Therefore, in this study, we use a simulation-based strategy to determine how accurately each approach recapitulates the selective constraint at individual sites. We find that the fixed-effects approach, despite its extensive parameterization, consistently and accurately estimates site-specific evolutionary constraint. By contrast, the random-effects Bayesian approach systematically underestimates the strength of natural selection, particularly for slowly evolving sites. We also find that, despite the strong differences between their inferred selection coefficient distributions, the fixed- and random-effects approaches yield surprisingly similar inferences of site-specific selective constraint. We conclude that the fixed-effects mutation-selection framework provides the more reliable software platform for model application and future development. © The Author 2016. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The role of personality in health care use: Results of a population-based longitudinal study in Germany.

PubMed

Hajek, André; Bock, Jens-Oliver; König, Hans-Helmut

2017-01-01

To determine the role of personality in health care use longitudinally. Data were derived from the German Socio-Economic Panel (GSOEP), a nationally representative, longitudinal cohort study of German households starting in 1984. Concentrating on the role of personality, we used data from the years 2005, 2009 and 2013. Personality was measured by using the GSOEP Big Five Inventory (BFI-S). Number of physician visits in the last 3 months and hospital stays in the last year were used as measures of health care use. Adjusting for predisposing factors, enabling resources, and need factors, fixed effects regressions revealed that physician visits increased with increasing neuroticism, whereas extraversion, openness to experience, agreeableness and conscientiousness did not affect physician visits in a significant way. The effect of self-rated health on physician visits was significantly moderated by neuroticism. Moreover, fixed effects regressions revealed that the probability of hospitalization in the past year increased with increasing extraversion, whereas the other personality factors did not affect this outcome measure significantly. Our findings suggest that changes in neuroticism are associated with changes in physician visits and that changes in extraversion are associated with the probability of hospitalization. Since recent studies have shown that treatments can modify personality traits, developing interventional strategies should take into account personality factors. For example, efforts to intervene in changing neuroticism might have beneficial effects for the healthcare system.

Simulation of multi-stage nonlinear bone remodeling induced by fixed partial dentures of different configurations: a comparative clinical and numerical study.

PubMed

Liao, Zhipeng; Yoda, Nobuhiro; Chen, Junning; Zheng, Keke; Sasaki, Keiichi; Swain, Michael V; Li, Qing

2017-04-01

This paper aimed to develop a clinically validated bone remodeling algorithm by integrating bone's dynamic properties in a multi-stage fashion based on a four-year clinical follow-up of implant treatment. The configurational effects of fixed partial dentures (FPDs) were explored using a multi-stage remodeling rule. Three-dimensional real-time occlusal loads during maximum voluntary clenching were measured with a piezoelectric force transducer and were incorporated into a computerized tomography-based finite element mandibular model. Virtual X-ray images were generated based on simulation and statistically correlated with clinical data using linear regressions. The strain energy density-driven remodeling parameters were regulated over the time frame considered. A linear single-stage bone remodeling algorithm, with a single set of constant remodeling parameters, was found to poorly fit with clinical data through linear regression (low [Formula: see text] and R), whereas a time-dependent multi-stage algorithm better simulated the remodeling process (high [Formula: see text] and R) against the clinical results. The three-implant-supported and distally cantilevered FPDs presented noticeable and continuous bone apposition, mainly adjacent to the cervical and apical regions. The bridged and mesially cantilevered FPDs showed bone resorption or no visible bone formation in some areas. Time-dependent variation of bone remodeling parameters is recommended to better correlate remodeling simulation with clinical follow-up. The position of FPD pontics plays a critical role in mechanobiological functionality and bone remodeling. Caution should be exercised when selecting the cantilever FPD due to the risk of overloading bone resorption.

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

PubMed

Robbins, Blaine

2013-01-01

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

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

Steigies, C. T.; Barjatya, A.

Langmuir probes are standard instruments for plasma density measurements on many sounding rockets. These probes can be operated in swept-bias as well as in fixed-bias modes. In swept-bias Langmuir probes, contamination effects are frequently visible as a hysteresis between consecutive up and down voltage ramps. This hysteresis, if not corrected, leads to poorly determined plasma densities and temperatures. With a properly chosen sweep function, the contamination parameters can be determined from the measurements and correct plasma parameters can then be determined. In this paper, we study the contamination effects on fixed-bias Langmuir probes, where no hysteresis type effect is seenmore » in the data. Even though the contamination is not evident from the measurements, it does affect the plasma density fluctuation spectrum as measured by the fixed-bias Langmuir probe. We model the contamination as a simple resistor-capacitor circuit between the probe surface and the plasma. We find that measurements of small scale plasma fluctuations (meter to sub-meter scale) along a rocket trajectory are not affected, but the measured amplitude of large scale plasma density variation (tens of meters or larger) is attenuated. From the model calculations, we determine amplitude and cross-over frequency of the contamination effect on fixed-bias probes for different contamination parameters. The model results also show that a fixed bias probe operating in the ion-saturation region is affected less by contamination as compared to a fixed bias probe operating in the electron saturation region.« less

Analyzing the contribution of climate change to long-term variations in sediment nitrogen sources for reservoirs/lakes.

PubMed

Xia, Xinghui; Wu, Qiong; Zhu, Baotong; Zhao, Pujun; Zhang, Shangwei; Yang, Lingyan

2015-08-01

We applied a mixing model based on stable isotopic δ(13)C, δ(15)N, and C:N ratios to estimate the contributions of multiple sources to sediment nitrogen. We also developed a conceptual model describing and analyzing the impacts of climate change on nitrogen enrichment. These two models were conducted in Miyun Reservoir to analyze the contribution of climate change to the variations in sediment nitrogen sources based on two (210)Pb and (137)Cs dated sediment cores. The results showed that during the past 50years, average contributions of soil and fertilizer, submerged macrophytes, N2-fixing phytoplankton, and non-N2-fixing phytoplankton were 40.7%, 40.3%, 11.8%, and 7.2%, respectively. In addition, total nitrogen (TN) contents in sediment showed significant increasing trends from 1960 to 2010, and sediment nitrogen of both submerged macrophytes and phytoplankton sources exhibited significant increasing trends during the past 50years. In contrast, soil and fertilizer sources showed a significant decreasing trend from 1990 to 2010. According to the changing trend of N2-fixing phytoplankton, changes of temperature and sunshine duration accounted for at least 43% of the trend in the sediment nitrogen enrichment over the past 50years. Regression analysis of the climatic factors on nitrogen sources showed that the contributions of precipitation, temperature, and sunshine duration to the variations in sediment nitrogen sources ranged from 18.5% to 60.3%. The study demonstrates that the mixing model provides a robust method for calculating the contribution of multiple nitrogen sources in sediment, and this study also suggests that N2-fixing phytoplankton could be regarded as an important response factor for assessing the impacts of climate change on nitrogen enrichment. Copyright © 2015 Elsevier B.V. All rights reserved.

Bayesian LASSO, scale space and decision making in association genetics.

PubMed

Pasanen, Leena; Holmström, Lasse; Sillanpää, Mikko J

2015-01-01

LASSO is a penalized regression method that facilitates model fitting in situations where there are as many, or even more explanatory variables than observations, and only a few variables are relevant in explaining the data. We focus on the Bayesian version of LASSO and consider four problems that need special attention: (i) controlling false positives, (ii) multiple comparisons, (iii) collinearity among explanatory variables, and (iv) the choice of the tuning parameter that controls the amount of shrinkage and the sparsity of the estimates. The particular application considered is association genetics, where LASSO regression can be used to find links between chromosome locations and phenotypic traits in a biological organism. However, the proposed techniques are relevant also in other contexts where LASSO is used for variable selection. We separate the true associations from false positives using the posterior distribution of the effects (regression coefficients) provided by Bayesian LASSO. We propose to solve the multiple comparisons problem by using simultaneous inference based on the joint posterior distribution of the effects. Bayesian LASSO also tends to distribute an effect among collinear variables, making detection of an association difficult. We propose to solve this problem by considering not only individual effects but also their functionals (i.e. sums and differences). Finally, whereas in Bayesian LASSO the tuning parameter is often regarded as a random variable, we adopt a scale space view and consider a whole range of fixed tuning parameters, instead. The effect estimates and the associated inference are considered for all tuning parameters in the selected range and the results are visualized with color maps that provide useful insights into data and the association problem considered. The methods are illustrated using two sets of artificial data and one real data set, all representing typical settings in association genetics.

Interdependency of the maximum range of flexion-extension of hand metacarpophalangeal joints.

PubMed

Gracia-Ibáñez, V; Vergara, M; Sancho-Bru, J-L

2016-12-01

Mobility of the fingers metacarpophalangeal (MCP) joints depends on the posture of the adjacent ones. Current Biomechanical hand models consider fixed ranges of movement at joints, regardless of the posture, thus allowing for non-realistic postures, generating wrong results in reach studies and forward dynamic analyses. This study provides data for more realistic hand models. The maximum voluntary extension (MVE) and flexion (MVF) of different combinations of MCP joints were measured covering their range of motion. Dependency of the MVF and MVE on the posture of the adjacent MCP joints was confirmed and mathematical models obtained through regression analyses (RMSE 7.7°).

The economic impact of Mexico City's smoke-free law.

PubMed

López, Carlos Manuel Guerrero; Ruiz, Jorge Alberto Jiménez; Shigematsu, Luz Myriam Reynales; Waters, Hugh R

2011-07-01

To evaluate the economic impact of Mexico City's 2008 smoke-free law--The Non-Smokers' Health Protection Law on restaurants, bars and nightclubs. We used the Monthly Services Survey of businesses from January 2005 to April 2009--with revenues, employment and payments to employees as the principal outcomes. The results are estimated using a differences-in-differences regression model with fixed effects. The states of Jalisco, Nuevo León and México, where the law was not in effect, serve as a counterfactual comparison group. In restaurants, after accounting for observable factors and the fixed effects, there was a 24.8% increase in restaurants' revenue associated with the smoke-free law. This difference is not statistically significant but shows that, on average, restaurants did not suffer economically as a result of the law. Total wages increased by 28.2% and employment increased by 16.2%. In nightclubs, bars and taverns there was a decrease of 1.5% in revenues and an increase of 0.1% and 3.0%, respectively, in wages and employment. None of these effects are statistically significant in multivariate analysis. There is no statistically significant evidence that the Mexico City smoke-free law had a negative impact on restaurants' income, employees' wages and levels of employment. On the contrary, the results show a positive, though statistically non-significant, impact of the law on most of these outcomes. Mexico City's experience suggests that smoke-free laws in Mexico and elsewhere will not hurt economic productivity in the restaurant and bar industries.

Data-driven discovery of partial differential equations.

PubMed

Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2017-04-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

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.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

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

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Preoperative intra-aortic counterpulsation in high-risk patients undergoing cardiac surgery: a meta-analysis of randomized controlled trials†.

PubMed

Pilarczyk, Kevin; Boening, Andreas; Jakob, Heinz; Langebartels, Georg; Markewitz, Andreas; Haake, Nils; Heringlake, Matthias; Trummer, Georg

2016-01-01

In contrast to the results of previous studies, recent randomized controlled trials (RCTs) failed to show a benefit of prophylactic aortic counterpulsation in high-risk patients undergoing cardiac surgery. The present analysis aims to redefine the effects of this treatment modality in the light of this new evidence. MEDLINE, EMBASE, CENTRAL/CCTR, Google Scholar and reference lists of relevant articles were searched for full-text articles of RCTs in English or German. Assessments for eligibility, relevance, study validity and data extraction were performed by two reviewers independently using prespecified criteria. The primary outcome was hospital mortality. A total of nine eligible RCTs with 1171 patients were identified: 577 patients were treated preoperatively with intra-aortic balloon pump (IABP) and 594 patients served as controls. The pooled odds ratio (OR) for hospital mortality (22 hospital deaths in the intervention arm, 54 in the control group) was 0.381 (95% CI 0.230-0.629; P < 0.001). The pooled analyses of five RCTs including only patients undergoing isolated on-pump coronary artery bypass grafting (n[IABP] = 348, n[control] = 347) also showed a statistically significant improvement in mortality for preoperative IABP implantation (fixed-effects model: OR 0.267, 95% CI 0.129-0.552, P < 0.001). The pooled OR for hospital mortality from two randomized off-pump trials was 0.556 (fixed-effects model, 95% CI 0.207-1.493, P = 0.226). Preoperative aortic counterpulsation was associated with a significant reduction in low cardiac output syndrome (LCOS) in the total population (fixed-effects model: OR 0.330, 95% CI 0.214-0.508, P < 0.001) as well as in the subgroup of CAGB patients (fixed-effects model: OR 0.113, 95% CI 0.056-0.226, P < 0.001), whereas there was no benefit in the off-pump population (fixed-effects model: OR 0.555, 95% CI 0.209-1.474, P = 0.238). Preoperative IABP implantation was associated with a reduction of intensive care unit (ICU) stay in all investigated populations with a greater effect in the total population [fixed-effects model: standard mean difference (SMD) -0.931 ± 0.198, P < 0.001] as well as in the subgroup of CAGB patients (fixed-effects model: SMD -1.240 ± 0.156, P < 0.001), compared with the off-pump group (fixed-effects model: SMD -0.723 ± 0.128, P < 0.001). Despite contradictory results from recent trials, the present study confirms the findings of previous meta-analyses that prophylactic aortic counterpulsation reduces hospital mortality, incidence of LCOS and ICU requirement in high-risk patients undergoing on-pump cardiac surgery. However, owing to small sample sizes and the lack of a clear-cut definition of high-risk patients, an adequately powered, prospective RCT is necessary to find a definite answer to the question, if certain groups of patients undergoing cardiac surgery benefit from a prophylactic IABP insertion. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Height conditions salary expectations: Evidence from large-scale data in China

NASA Astrophysics Data System (ADS)

Yang, Xiao; Gao, Jian; Liu, Jin-Hu; Zhou, Tao

2018-07-01

Height premium has been revealed by extensive literature, however, evidence from China based on large-scale data remains still lacking. In this paper, we study how height conditions salary expectations by exploring a dataset covering over 140,000 Chinese job seekers. By using graphical and regression models, we find evidence in support of height premium that tall people expect a significantly higher salary in career development. In particular, regression results suggest stronger effects of height premium on female than on male, however, the gender differences decrease as the education level increases and become insignificant after holding all control variables fixed. Further, results from graphical models suggest three promising ways in helping short people: (i) to accumulate more working experiences, since one year seniority can respectively make up about 3 cm and 7 cm shortness for female and male; (ii) to increase the level of education, since one higher academic degree may eliminate all disadvantages that brought by shortness; (iii) to target jobs in regions with a higher level of development. Our work provides a cross-culture supportive evidence of height premium and contributes two novel features to the literature: the compensation story in helping short people, and the focus on salary expectations in isolation from discrimination channels.

Correlation of Vitamin D status and orthodontic-induced external apical root resorption

PubMed Central

Tehranchi, Azita; Sadighnia, Azin; Younessian, Farnaz; Abdi, Amir H.; Shirvani, Armin

2017-01-01

Background: Adequate Vitamin D is essential for dental and skeletal health in children and adult. The purpose of this study was to assess the correlation of serum Vitamin D level with external-induced apical root resorption (EARR) following fixed orthodontic treatment. Materials and Methods: In this cross-sectional study, the prevalence of Vitamin D deficiency (defined by25-hydroxyvitamin-D) was determined in 34 patients (23.5% male; age range 12–23 years; mean age 16.63 ± 2.84) treated with fixed orthodontic treatment. Root resorption of four maxillary incisors was measured using before and after periapical radiographs (136 measured teeth) by means of a design-to-purpose software to optimize data collection. Teeth with a maximum percentage of root resorption (%EARR) were indicated as representative root resorption for each patient. A multiple linear regression model and Pearson correlation coefficient were used to assess the association of Vitamin D status and observed EARR. P < 0.05 was considered statistically significant. Results: The Pearson coefficient between these two variables was determined about 0.15 (P = 0.38). Regression analysis revealed that Vitamin D status of the patients demonstrated no significant statistical correlation with EARR, after adjustment of confounding variables using linear regression model (P > 0.05). Conclusion: This study suggests that Vitamin D level is not among the clinical variables that are potential contributors for EARR. The prevalence of Vitamin D deficiency does not differ in patients with higher EARR. These data suggest the possibility that Vitamin D insufficiency may not contribute to the development of more apical root resorption although this remains to be confirmed by further longitudinal cohort studies. PMID:29238379

Evaluation of the 2013 Southeast Asian Haze on Solar Generation Performance

PubMed Central

Maghami, Mohammadreza; Hizam, Hashim; Gomes, Chandima; Hajighorbani, Shahrooz; Rezaei, Nima

2015-01-01

Pollution in Southeast Asia is a major public energy problem and the cause of energy losses. A significant problem with respect to this type of pollution is that it decreases energy yield. In this study, two types of photovoltaic (PV) solar arrays were used to evaluate the effect of air pollution. The performance of two types of solar arrays were analysed in this research, namely, two units of a 1 kWp tracking flat photovoltaic (TFP) and two units of a 1 kWp fixed flat photovoltaic arrays (FFP). Data analysis was conducted on 2,190 samples at 30 min intervals from 01st June 2013, when both arrays were washed, until 30th June 2013. The performance was evaluated by using environmental data (irradiation, temperature, dust thickness, and air pollution index), power output, and energy yield. Multiple regression models were predicted in view of the environmental data and PV array output. Results showed that the fixed flat system was more affected by air pollution than the tracking flat plate. The contribution of this work is that it considers two types of photovoltaic arrays under the Southeast Asian pollution 2013. PMID:26275303

Compositional Effects on Nickel-Base Superalloy Single Crystal Microstructures

NASA Technical Reports Server (NTRS)

MacKay, Rebecca A.; Gabb, Timothy P.; Garg,Anita; Rogers, Richard B.; Nathal, Michael V.

2012-01-01

Fourteen nickel-base superalloy single crystals containing 0 to 5 wt% chromium (Cr), 0 to 11 wt% cobalt (Co), 6 to 12 wt% molybdenum (Mo), 0 to 4 wt% rhenium (Re), and fixed amounts of aluminum (Al) and tantalum (Ta) were examined to determine the effect of bulk composition on basic microstructural parameters, including gamma' solvus, gamma' volume fraction, volume fraction of topologically close-packed (TCP) phases, phase chemistries, and gamma - gamma'. lattice mismatch. Regression models were developed to describe the influence of bulk alloy composition on the microstructural parameters and were compared to predictions by a commercially available software tool that used computational thermodynamics. Co produced the largest change in gamma' solvus over the wide compositional range used in this study, and Mo produced the largest effect on the gamma lattice parameter and the gamma - gamma' lattice mismatch over its compositional range, although Re had a very potent influence on all microstructural parameters investigated. Changing the Cr, Co, Mo, and Re contents in the bulk alloy had a significant impact on their concentrations in the gamma matrix and, to a smaller extent, in the gamma' phase. The gamma phase chemistries exhibited strong temperature dependencies that were influenced by the gamma and gamma' volume fractions. A computational thermodynamic modeling tool significantly underpredicted gamma' solvus temperatures and grossly overpredicted the amount of TCP phase at 982 C. Furthermore, the predictions by the software tool for the gamma - gamma' lattice mismatch were typically of the wrong sign and magnitude, but predictions could be improved if TCP formation was suspended within the software program. However, the statistical regression models provided excellent estimations of the microstructural parameters based on bulk alloy composition, thereby demonstrating their usefulness.

Deconvolution single shot multibox detector for supermarket commodity detection and classification

NASA Astrophysics Data System (ADS)

Li, Dejian; Li, Jian; Nie, Binling; Sun, Shouqian

2017-07-01

This paper proposes an image detection model to detect and classify supermarkets shelves' commodity. Based on the principle of the features directly affects the accuracy of the final classification, feature maps are performed to combine high level features with bottom level features. Then set some fixed anchors on those feature maps, finally the label and the position of commodity is generated by doing a box regression and classification. In this work, we proposed a model named Deconvolutiuon Single Shot MultiBox Detector, we evaluated the model using 300 images photographed from real supermarket shelves. Followed the same protocol in other recent methods, the results showed that our model outperformed other baseline methods.

Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates.

PubMed

Lord, Dominique; Park, Peter Young-Jin

2008-07-01

Traditionally, transportation safety analysts have used the empirical Bayes (EB) method to improve the estimate of the long-term mean of individual sites; to correct for the regression-to-the-mean (RTM) bias in before-after studies; and to identify hotspots or high risk locations. The EB method combines two different sources of information: (1) the expected number of crashes estimated via crash prediction models, and (2) the observed number of crashes at individual sites. Crash prediction models have traditionally been estimated using a negative binomial (NB) (or Poisson-gamma) modeling framework due to the over-dispersion commonly found in crash data. A weight factor is used to assign the relative influence of each source of information on the EB estimate. This factor is estimated using the mean and variance functions of the NB model. With recent trends that illustrated the dispersion parameter to be dependent upon the covariates of NB models, especially for traffic flow-only models, as well as varying as a function of different time-periods, there is a need to determine how these models may affect EB estimates. The objectives of this study are to examine how commonly used functional forms as well as fixed and time-varying dispersion parameters affect the EB estimates. To accomplish the study objectives, several traffic flow-only crash prediction models were estimated using a sample of rural three-legged intersections located in California. Two types of aggregated and time-specific models were produced: (1) the traditional NB model with a fixed dispersion parameter and (2) the generalized NB model (GNB) with a time-varying dispersion parameter, which is also dependent upon the covariates of the model. Several statistical methods were used to compare the fitting performance of the various functional forms. The results of the study show that the selection of the functional form of NB models has an important effect on EB estimates both in terms of estimated values, weight factors, and dispersion parameters. Time-specific models with a varying dispersion parameter provide better statistical performance in terms of goodness-of-fit (GOF) than aggregated multi-year models. Furthermore, the identification of hazardous sites, using the EB method, can be significantly affected when a GNB model with a time-varying dispersion parameter is used. Thus, erroneously selecting a functional form may lead to select the wrong sites for treatment. The study concludes that transportation safety analysts should not automatically use an existing functional form for modeling motor vehicle crashes without conducting rigorous analyses to estimate the most appropriate functional form linking crashes with traffic flow.

Regression modeling of particle size distributions in urban storm water: advancements through improved sample collection methods

USGS Publications Warehouse

Fienen, Michael N.; Selbig, William R.

2012-01-01

A new sample collection system was developed to improve the representation of sediment entrained in urban storm water by integrating water quality samples from the entire water column. The depth-integrated sampler arm (DISA) was able to mitigate sediment stratification bias in storm water, thereby improving the characterization of suspended-sediment concentration and particle size distribution at three independent study locations. Use of the DISA decreased variability, which improved statistical regression to predict particle size distribution using surrogate environmental parameters, such as precipitation depth and intensity. The performance of this statistical modeling technique was compared to results using traditional fixed-point sampling methods and was found to perform better. When environmental parameters can be used to predict particle size distributions, environmental managers have more options when characterizing concentrations, loads, and particle size distributions in urban runoff.

Mind-Body Interventions for Irritable Bowel Syndrome Patients in the Chinese Population: a Systematic Review and Meta-Analysis.

PubMed

Wang, Weidong; Wang, Fang; Fan, Feng; Sedas, Ana Cristina; Wang, Jian

2017-04-01

The aim of this study is to identify and assess evidence related to the efficacy of mind-body interventions on irritable bowel syndrome (IBS) in the Chinese population. Drawn from Chinese databases, nine RCTs and three Q-E studies were included in the systematic review. The methodological quality of RCTs was evaluated based on the following criteria: adequate sequence generation, allocation concealment, blinding, completeness of outcome data, selective reporting, and other potential biases. For continuous variables, the effect size (ES) was determined by calculating the standardized mean difference between groups. For dichotomous variables, the ES was determined by calculating the risk ratio (RR) between groups. Given the heterogeneity between the trials and the small number of studies included, both random effects and fixed effects models were used. The inverse variance method was used for pooling. Statistical analyses were performed using Review Manager version 5.0. The total number of papers identified was 710: 462 from English language databases and 248 from Chinese language databases. Twelve studies met our eligibility criteria. Among the studies selected, three were Q-E studies the rest RCTs. Two studies described the randomization process. None of the studies reported allocation concealment nor blinding. Seven studies reported no dropouts. One of the studies mentioned the total amount of dropouts; though the reason for dropping out was not referenced. The other four studies did not clearly report dropouts. With the exception of three studies, there was inadequate information to determine biased reporting for the majority; the level of risk for bias in these studies is unclear. Finally, six meta-analyses were performed. One was conducted with four randomized controlled trials (RCTs) that used cure rate as outcome measures to evaluate gastrointestinal (GI) symptoms, which suggested that mind-body interventions were effective in improving GI symptoms (random effects model: RR = 1.08; 95 % CI 1.01 to 1.17; fixed effects model: RR = 1.07; 95 % CI 1.01 to 1.12). The remaining five were conducted in three RCTs, which suggested that mind-body interventions were effective in improving several aspects of quality of life, including interference with activity (random effects and fixed effects models: SMD = 0.64; 95 % CI 0.41 to 0.86), body image (random effects model: SMD = 0.36; 95 % CI 0.06 to 0.67; fixed effects model: SMD = 0.33; 95 % CI 0.11 to 0.55), health worry (random effects and fixed effects models: SMD = 0.67; 95 % CI 0.44 to 0.90), food avoidance (random effects and fixed effects models: SMD = 0.45; 95 % CI 0.23 to 0.68), and social reaction (random effects model: SMD = 0.79; 95 % CI 0.47 to 1.12; fixed effects model: SMD = 0.78; 95 % CI 0.55 to 1.01), as measured by Irritable Bowel Syndrome Quality of Life Questionnaire ( IBS-QOL). Mind-body interventions may have the potential to improve GI symptoms in Chinese patients with IBS. The improvement of GI symptoms was also accompanied with the improvement of various outcomes, including depression, anxiety, and quality of life, just to mention a few. However, the published studies generally had significant methodological limitations. Future clinical trials with rigorous research design are needed in this field. More studies focusing on the mind-body interventions originated in China, such as tai chi and qi gong should be encouraged.

Characterization of metal adsorption kinetic properties in batch and fixed-bed reactors.

PubMed

Chen, J Paul; Wang, Lin

2004-01-01

Copper adsorption kinetic properties in batch and fixed-bed reactors were studied in this paper. The isothermal adsorption experiments showed that the copper adsorption capacity of a granular activated carbon (Filtrasorb 200) increased when ionic strength was higher. The presence of EDTA diminished the adsorption. An intraparticle diffusion model and a fixed-bed model were successfully used to describe the batch kinetic and fixed-bed operation behaviors. The kinetics became faster when the solution pH was not controlled, implying that the surface precipitation caused some metal uptake. The external mass transfer coefficient, the diffusivity and the dispersion coefficient were obtained from the modeling. It was found that both external mass transfer and dispersion coefficients increased when the flow rate was higher. Finally effects of kinetic parameters on simulation of fixed-bed operation were conducted.

Frequency of satisfaction and dissatisfaction with practice among rural-based, group-employed physicians and non-physician practitioners.

PubMed

Waddimba, Anthony C; Scribani, Melissa; Krupa, Nicole; May, John J; Jenkins, Paul

2016-10-22

Widespread dissatisfaction among United States (U.S.) clinicians could endanger ongoing reforms. Practitioners in rural/underserved areas withstand stressors that are unique to or accentuated in those settings. Medical professionals employed by integrating delivery systems are often distressed by the cacophony of organizational change(s) that such consolidation portends. We investigated the factors associated with dis/satisfaction with rural practice among doctors/non-physician practitioners employed by an integrated healthcare delivery network serving 9 counties of upstate New York, during a time of organizational transition. We linked administrative data about practice units with cross-sectional data from a self-administered multi-dimensional questionnaire that contained practitioner demographics plus valid scales assessing autonomy/relatedness needs, risk aversion, tolerance for uncertainty/ambiguity, meaningfulness of patient care, and workload. We targeted medical professionals on the institutional payroll for inclusion. We excluded those who retired, resigned or were fired during the study launch, plus members of the advisory board and research team. Fixed-effects beta regressions were performed to test univariate associations between each factor and the percent of time a provider was dis/satisfied. Factors that manifested significant fixed effects were entered into multivariate, inflated beta regression models of the proportion of time that practitioners were dis/satisfied, incorporating clustering by practice unit as a random effect. Of the 473 eligible participants. 308 (65.1 %) completed the questionnaire. 59.1 % of respondents were doctoral-level; 40.9 % mid-level practitioners. Practitioners with heavier workloads and/or greater uncertainty intolerance were less likely to enjoy top-quintile satisfaction; those deriving greater meaning from practice were more likely. Higher meaningfulness and gratified relational needs increased one's likelihood of being in the lowest quintile of dissatisfaction; heavier workload and greater intolerance of uncertainty reduced that likelihood. Practitioner demographics and most practice unit characteristics did not manifest any independent effect. Mutable factors, such as workload, work meaningfulness, relational needs, uncertainty/ambiguity tolerance, and risk-taking attitudes displayed the strongest association with practitioner satisfaction/dissatisfaction, independent of demographics and practice unit characteristics. Organizational efforts should be dedicated to a redesign of group-employment models, including more equitable division of clinical labor, building supportive peer networks, and uncertainty/risk tolerance coaching, to improve the quality of work life among rural practitioners.

High birth weight and perinatal mortality among siblings: A register based study in Norway, 1967-2011.

PubMed

Kristensen, Petter; Keyes, Katherine M; Susser, Ezra; Corbett, Karina; Mehlum, Ingrid Sivesind; Irgens, Lorentz M

2017-01-01

Perinatal mortality according to birth weight has an inverse J-pattern. Our aim was to estimate the influence of familial factors on this pattern, applying a cohort sibling design. We focused on excess mortality among macrosomic infants (>2 SD above the mean) and hypothesized that the birth weight-mortality association could be explained by confounding shared family factors. We also estimated how the participant's deviation from mean sibling birth weight influenced the association. We included 1 925 929 singletons, born term or post-term to mothers with more than one delivery 1967-2011 registered in the Medical Birth Registry of Norway. We examined z-score birth weight and perinatal mortality in random-effects and sibling fixed-effects logistic regression models including measured confounders (e.g. maternal diabetes) as well as unmeasured shared family confounders (through fixed effects models). Birth weight-specific mortality showed an inverse J-pattern, being lowest (2.0 per 1000) at reference weight (z-score +1 to +2) and increasing for higher weights. Mortality in the highest weight category was 15-fold higher than reference. This pattern changed little in multivariable models. Deviance from mean sibling birth weight modified the mortality pattern across the birth weight spectrum: small and medium-sized infants had increased mortality when being smaller than their siblings, and large-sized infants had an increased risk when outweighing their siblings. Maternal diabetes and birth weight acted in a synergistic fashion with mortality among macrosomic infants in diabetic pregnancies in excess of what would be expected for additive effects. The inverse J-pattern between birth weight and mortality is not explained by measured confounders or unmeasured shared family factors. Infants are at particularly high mortality risk when their birth weight deviates substantially from their siblings. Sensitivity analysis suggests that characteristics related to maternal diabetes could be important in explaining the increased mortality among macrosomic infants.

Generalized Accelerated Failure Time Spatial Frailty Model for Arbitrarily Censored Data

PubMed Central

Zhou, Haiming; Hanson, Timothy; Zhang, Jiajia

2017-01-01

Flexible incorporation of both geographical patterning and risk effects in cancer survival models is becoming increasingly important, due in part to the recent availability of large cancer registries. Most spatial survival models stochastically order survival curves from different subpopulations. However, it is common for survival curves from two subpopulations to cross in epidemiological cancer studies and thus interpretable standard survival models can not be used without some modification. Common fixes are the inclusion of time-varying regression effects in the proportional hazards model or fully non-parametric modeling, either of which destroys any easy interpretability from the fitted model. To address this issue, we develop a generalized accelerated failure time model which allows stratification on continuous or categorical covariates, as well as providing per-variable tests for whether stratification is necessary via novel approximate Bayes factors. The model is interpretable in terms of how median survival changes and is able to capture crossing survival curves in the presence of spatial correlation. A detailed Markov chain Monte Carlo algorithm is presented for posterior inference and a freely available function frailtyGAFT is provided to fit the model in the R package spBayesSurv. We apply our approach to a subset of the prostate cancer data gathered for Louisiana by the Surveillance, Epidemiology, and End Results program of the National Cancer Institute. PMID:26993982

Fungible Correlation Matrices: A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for Monte Carlo Research.

PubMed

Waller, Niels G

2016-01-01

For a fixed set of standardized regression coefficients and a fixed coefficient of determination (R-squared), an infinite number of predictor correlation matrices will satisfy the implied quadratic form. I call such matrices fungible correlation matrices. In this article, I describe an algorithm for generating positive definite (PD), positive semidefinite (PSD), or indefinite (ID) fungible correlation matrices that have a random or fixed smallest eigenvalue. The underlying equations of this algorithm are reviewed from both algebraic and geometric perspectives. Two simulation studies illustrate that fungible correlation matrices can be profitably used in Monte Carlo research. The first study uses PD fungible correlation matrices to compare penalized regression algorithms. The second study uses ID fungible correlation matrices to compare matrix-smoothing algorithms. R code for generating fungible correlation matrices is presented in the supplemental materials.

The U.S. health production function: evidence from 2001 to 2009.

PubMed

Tseng, Hui-Kuan; Olsen, Reed

2016-03-01

This study estimates the impact of the 2007 financial crisis upon U.S. health as measured by age adjusted death rates. OLS regression results suggest that the average death rate was lower in the post-crisis period than the pre-crisis period. The majority of the average decline in the death rate was a result of the time period and not a result of changes in the values of the underlying explanatory variables. We continue to find this result even adding state fixed effects. Contrary to other research, we find that the unemployment rate has no statistically significant impact on death rates either for the U.S. as a whole or for any states individually. Rather, the impact of the financial crisis is felt via year fixed effects that increased over time during the post-crisis period.

Fluoride release and recharge behavior of a nano-filled resin-modified glass ionomer compared with that of other fluoride releasing materials.

PubMed

Mitra, Sumita B; Oxman, Joe D; Falsafi, Afshin; Ton, Tiffany T

2011-12-01

To compare the long-term fluoride release kinetics of a novel nano-filled two-paste resin-modified glass-ionomer (RMGI), Ketac Nano (KN) with that of two powder-liquid resin-modified glass-ionomers, Fuji II LC (FLC) and Vitremer (VT) and one conventional glass-ionomer, Fuji IX (FIX). Fluoride release was measured in vitro using ion-selective electrodes. Kinetic analysis was done using regression analysis and compared with existing models for GIs and compomers. In a separate experiment the samples of KN and two conventional glass-ionomers, FIX and Ketac Molar (KM) were subjected to a treatment with external fluoride source (Oral-B Neutra-Foam) after 3 months of fluoride release and the recharge behavior studied for an additional 7-day period. The cumulative amount of fluoride released from KN, VT and FLC and the release profiles were statistically similar but greater than that for FIX at P < 0.05. All four materials, including KN, showed a burst of fluoride ions at shorter times (t) and an overall rate dependence on t1/2 typical for glass-ionomers. The coating of KN with its primer and of DY with its adhesive did not significantly alter the fluoride release behavior of the respective materials. The overall rate for KN was significantly higher than for the compomer DY. DY showed a linear rate of release vs. t and no burst effect as expected for compomers. The nanoionomer KN showed fluoride recharge behavior similar to the conventional glass ionomers FIX and KM. Thus, it was concluded that the new RMGI KN exhibits fluoride ion release behavior similar to typical conventional and RMGIs and that the primer does not impede the release of fluoride.

Air tankers in Southern California Fires...effectiveness in delivering retardants rated

Treesearch

Theodore G. Storey; Leon W. Cooley

1967-01-01

Eleven air attack experts were asked to rate 12 models of fixed-wing tankers and light helitankers for effectiveness ill delivering chemical fire retardants under 21 typical situations. They rated fixed-wing tankers as more effective in strong wind crosswinds, and downwind approaches, but helitankers as more effective in narrow canyons and on steep slopes. Certain...

Deep supervised dictionary learning for no-reference image quality assessment

NASA Astrophysics Data System (ADS)

Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

2018-03-01

We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

Government, politics and health policy: A quantitative analysis of 30 European countries.

PubMed

Mackenbach, Johan P; McKee, Martin

2015-10-01

Public health policies are often dependent on political decision-making, but little is known of the impact of different forms of government on countries' health policies. In this exploratory study we studied the association between a wide range of process and outcome indicators of health policy and four groups of political factors (levels of democracy, e.g. voice and accountability; political representation, e.g. voter turnout; distribution of power, e.g. constraints on the executive; and quality of government, e.g. absence of corruption) in contemporary Europe. Data on 15 aspects of government and 18 indicators of health policy as well as on potential confounders were extracted from harmonized international data sources, covering 30 European countries and the years 1990-2010. In a first step, multivariate regression analysis was used to relate cumulative measures of government to indicators of health policy, and in a second step panel regression with country fixed effects was used to relate changes in selected measures of government to changes in indicators of health policy. In multivariate regression analyses, measures of quality of democracy and quality of government had many positive associations with process and outcome indicators of health policy, while measures of distribution of power and political representation had few and inconsistent associations. Associations for quality of democracy were robust against more extensive control for confounding variables, including tests in panel regressions with country fixed effects, but associations for quality of government were not. In this period in Europe, the predominant political influence on health policy has been the rise of levels of democracy in countries in the Central & Eastern part of the region. In contrast to other areas of public policy, health policy does not appear to be strongly influenced by institutional features of democracy determining the distribution of power, nor by aspects of political representation. The effect of quality of government on health policy warrants more study. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A Practical Guide to Conducting a Systematic Review and Meta-analysis of Health State Utility Values.

PubMed

Petrou, Stavros; Kwon, Joseph; Madan, Jason

2018-05-10

Economic analysts are increasingly likely to rely on systematic reviews and meta-analyses of health state utility values to inform the parameter inputs of decision-analytic modelling-based economic evaluations. Beyond the context of economic evaluation, evidence from systematic reviews and meta-analyses of health state utility values can be used to inform broader health policy decisions. This paper provides practical guidance on how to conduct a systematic review and meta-analysis of health state utility values. The paper outlines a number of stages in conducting a systematic review, including identifying the appropriate evidence, study selection, data extraction and presentation, and quality and relevance assessment. The paper outlines three broad approaches that can be used to synthesise multiple estimates of health utilities for a given health state or condition, namely fixed-effect meta-analysis, random-effects meta-analysis and mixed-effects meta-regression. Each approach is illustrated by a synthesis of utility values for a hypothetical decision problem, and software code is provided. The paper highlights a number of methodological issues pertinent to the conduct of meta-analysis or meta-regression. These include the importance of limiting synthesis to 'comparable' utility estimates, for example those derived using common utility measurement approaches and sources of valuation; the effects of reliance on limited or poorly reported published data from primary utility assessment studies; the use of aggregate outcomes within analyses; approaches to generating measures of uncertainty; handling of median utility values; challenges surrounding the disentanglement of utility estimates collected serially within the context of prospective observational studies or prospective randomised trials; challenges surrounding the disentanglement of intervention effects; and approaches to measuring model validity. Areas of methodological debate and avenues for future research are highlighted.

Is scale-invariance in gauge-Yukawa systems compatible with the graviton?

NASA Astrophysics Data System (ADS)

Christiansen, Nicolai; Eichhorn, Astrid; Held, Aaron

2017-10-01

We explore whether perturbative interacting fixed points in matter systems can persist under the impact of quantum gravity. We first focus on semisimple gauge theories and show that the leading order gravity contribution evaluated within the functional Renormalization Group framework preserves the perturbative fixed-point structure in these models discovered in [J. K. Esbensen, T. A. Ryttov, and F. Sannino, Phys. Rev. D 93, 045009 (2016)., 10.1103/PhysRevD.93.045009]. We highlight that the quantum-gravity contribution alters the scaling dimension of the gauge coupling, such that the system exhibits an effective dimensional reduction. We secondly explore the effect of metric fluctuations on asymptotically safe gauge-Yukawa systems which feature an asymptotically safe fixed point [D. F. Litim and F. Sannino, J. High Energy Phys. 12 (2014) 178., 10.1007/JHEP12(2014)178]. The same effective dimensional reduction that takes effect in pure gauge theories also impacts gauge-Yukawa systems. There, it appears to lead to a split of the degenerate free fixed point into an interacting infrared attractive fixed point and a partially ultraviolet attractive free fixed point. The quantum-gravity induced infrared fixed point moves towards the asymptotically safe fixed point of the matter system, and annihilates it at a critical value of the gravity coupling. Even after that fixed-point annihilation, graviton effects leave behind new partially interacting fixed points for the matter sector.

Divergent estimation error in portfolio optimization and in linear regression

NASA Astrophysics Data System (ADS)

Kondor, I.; Varga-Haszonits, I.

2008-08-01

The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.

Effect of ionized serum calcium on outcomes in acute kidney injury needing renal replacement therapy: Secondary analysis of the Acute Renal Failure Trial Network Study

PubMed Central

Afshinnia, Farsad; Belanger, Karen; Palevsky, Paul M.; Young, Eric W.

2014-01-01

Background Hypocalcemia is very common in critically ill patients. While the effect of ionized calcium (iCa) on outcome is not well understood, manipulation of iCa in critically ill patients is a common practice. We analyzed all-cause mortality and several secondary outcomes in patients with acute kidney injury (AKI) by categories of serum iCa among participants in the Acute Renal Failure Trial Network (ATN) Study. Methods This is a post hoc secondary analysis of the ATN Study which was not preplanned in the original trial. Risk of mortality and renal recovery by categories of iCa were compared using multiple fixed and adjusted time-varying Cox regression models. Multiple linear regression models were used to explore the impact of baseline iCa on days free from ICU and hospital. Results A total of 685 patients were included in the analysis. Mean age was 60 (SD=15) years. There were 502 male patients (73.3%). Sixty-day all-cause mortality was 57.0%, 54.8%, and 54.4%, in patients with an iCa <1, 1–1.14, and ≥1.15 mmol/L, respectively (P=0.87). Mean of days free from ICU or hospital in all patients and the 28-day renal recovery in survivors to day 28 were not significantly different by categories of iCa. The hazard for death in a fully adjusted time-varying Cox regression survival model was 1.7 (95% CI: 1.3–2.4) comparing iCa <1 to iCa ≥1.15 mmol/L. No outcome was different for levels of iCa >1 mmol/L. Conclusion Severe hypocalcemia with iCa <1 mmol/L independently predicted mortality in patients with AKI needing renal replacement therapy. PMID:23992422

Nonmaternal Care’s Association With Mother’s Parenting Sensitivity: A Case of Self-Selection Bias?

PubMed Central

Nomaguchi, Kei M.; DeMaris, Alfred

2013-01-01

Although attachment theory posits that the use of nonmaternal care undermines quality of mothers’ parenting, empirical evidence for this link is inconclusive. Using data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (N = 1,233), the authors examined the associations between nonmaternal care characteristics and maternal sensitivity during the first 3 years of children’s lives, with special attention to selection effects and moderation by resource levels. Findings from fixed-effects regression models suggested that, on average, there is little relationship between nonmaternal care characteristics and maternal sensitivity, once selection factors are held constant. Some evidence of moderation effects was found, however. Excellent-quality care is related to more sensitivity for mothers with lower family income. Poor-quality care is related to lower sensitivity for single mothers, but not partnered mothers. In sum, nonmaternal care characteristics do not seem to have as much influence on mothers’ parenting as attachment theory claims. PMID:23772093

Motivational and mindfulness intervention for young adult female marijuana users

PubMed Central

de Dios, Marcel A.; Herman, Debra S.; Britton, Willoughby B.; Hagerty, Claire E.; Anderson, Bradley J.; Stein, Michael D.

2011-01-01

This pilot study tested the efficacy of a brief intervention using motivational interviewing (MI) plus mindfulness meditation (MM) to reduce marijuana use among young adult female. Thirty-four female marijuana users between the ages of 18–29 were randomized to either the intervention group (n = 22), consisting of 2 sessions of MI-MM or an assessment-only control group (n = 12). Participants’ marijuana use was assessed at baseline, 1, 2, and 3 months post-treatment. Fixed-effects regression modeling was used to analyze treatment effects. Participants randomized to the intervention group were found to use marijuana on 6.15 (z = −2.42, p=.015), 7.81 (z = −2.78, p=.005), and 6.83 (z = −2.23, p=.026) fewer days at months 1, 2, and 3, respectively, than controls. Findings from this pilot study provide preliminary evidence for the feasibility and effectiveness of a brief MI-MM for young adult female marijuana users. PMID:21940136

What we have learned about minimized extracorporeal circulation versus conventional extracorporeal circulation: an updated meta-analysis.

PubMed

Sun, Yanhua; Gong, Bing; Yuan, Xin; Zheng, Zhe; Wang, Guyan; Chen, Guo; Zhou, Chenghui; Wang, Wei; Ji, Bingyang

2015-08-01

The benefits of minimized extracorporeal circulation (MECC) compared with conventional extracorporeal circulation (CECC) are still in debate. PubMed, EMBASE and the Cochrane Library were searched until November 10, 2014. After quality assessment, we chose a fixed-effects model when the trials showed low heterogeneity, otherwise a random-effects model was used. We performed univariate meta-regression and sensitivity analysis to search for the potential sources of heterogeneity. Cumulative meta-analysis was performed to access the evolution of outcome over time. 41 RCTs enrolling 3744 patients were included after independent article review by 2 authors. MECC significantly reduced atrial fibrillation (RR, 0.76; 95% CI, 0.66 to 0.89; P < 0.001; I2 = 0%), and myocardial infarction (RR, 0.43; 95% CI, 0.26 to 0.71; P = 0.001; I2 = 0%). In addition, the results regarding chest tube drainage, transfusion rate, blood loss, red blood cell transfusion volume, and platelet count favored MECC as well. MECC diminished morbidity of cardiovascular complications postoperatively, conserved blood cells, and reduced allogeneic blood transfusion.

The effectiveness of tape playbacks in estimating Black Rail densities

USGS Publications Warehouse

Legare, M.; Eddleman, W.R.; Buckley, P.A.; Kelly, C.

1999-01-01

Tape playback is often the only efficient technique to survey for secretive birds. We measured the vocal responses and movements of radio-tagged black rails (Laterallus jamaicensis; 26 M, 17 F) to playback of vocalizations at 2 sites in Florida during the breeding seasons of 1992-95. We used coefficients from logistic regression equations to model probability of a response conditional to the birds' sex. nesting status, distance to playback source, and time of survey. With a probability of 0.811, nonnesting male black rails were ))lost likely to respond to playback, while nesting females were the least likely to respond (probability = 0.189). We used linear regression to determine daily, monthly and annual variation in response from weekly playback surveys along a fixed route during the breeding seasons of 1993-95. Significant sources of variation in the regression model were month (F3.48 = 3.89, P = 0.014), year (F2.48 = 9.37, P < 0.001), temperature (F1.48 = 5.44, P = 0.024), and month X year (F5.48 = 2.69, P = 0.031). The model was highly significant (P < 0.001) and explained 54% of the variation of mean response per survey period (r2 = 0.54). We combined response probability data from radiotagged black rails with playback survey route data to provide a density estimate of 0.25 birds/ha for the St. Johns National Wildlife Refuge. The relation between the number of black rails heard during playback surveys to the actual number present was influenced by a number of variables. We recommend caution when making density estimates from tape playback surveys

TH-E-BRF-06: Kinetic Modeling of Tumor Response to Fractionated Radiotherapy

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

Zhong, H; Gordon, J; Chetty, I

2014-06-15

Purpose: Accurate calibration of radiobiological parameters is crucial to predicting radiation treatment response. Modeling differences may have a significant impact on calibrated parameters. In this study, we have integrated two existing models with kinetic differential equations to formulate a new tumor regression model for calibrating radiobiological parameters for individual patients. Methods: A system of differential equations that characterizes the birth-and-death process of tumor cells in radiation treatment was analytically solved. The solution of this system was used to construct an iterative model (Z-model). The model consists of three parameters: tumor doubling time Td, half-life of dying cells Tr and cellmore » survival fraction SFD under dose D. The Jacobian determinant of this model was proposed as a constraint to optimize the three parameters for six head and neck cancer patients. The derived parameters were compared with those generated from the two existing models, Chvetsov model (C-model) and Lim model (L-model). The C-model and L-model were optimized with the parameter Td fixed. Results: With the Jacobian-constrained Z-model, the mean of the optimized cell survival fractions is 0.43±0.08, and the half-life of dying cells averaged over the six patients is 17.5±3.2 days. The parameters Tr and SFD optimized with the Z-model differ by 1.2% and 20.3% from those optimized with the Td-fixed C-model, and by 32.1% and 112.3% from those optimized with the Td-fixed L-model, respectively. Conclusion: The Z-model was analytically constructed from the cellpopulation differential equations to describe changes in the number of different tumor cells during the course of fractionated radiation treatment. The Jacobian constraints were proposed to optimize the three radiobiological parameters. The developed modeling and optimization methods may help develop high-quality treatment regimens for individual patients.« less

Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends.

PubMed

Ionides, Edward L; Wang, Zhen; Tapia Granados, José A

2013-10-03

Many investigations have used panel methods to study the relationships between fluctuations in economic activity and mortality. A broad consensus has emerged on the overall procyclical nature of mortality: perhaps counter-intuitively, mortality typically rises above its trend during expansions. This consensus has been tarnished by inconsistent reports on the specific age groups and mortality causes involved. We show that these inconsistencies result, in part, from the trend specifications used in previous panel models. Standard econometric panel analysis involves fitting regression models using ordinary least squares, employing standard errors which are robust to temporal autocorrelation. The model specifications include a fixed effect, and possibly a linear trend, for each time series in the panel. We propose alternative methodology based on nonlinear detrending. Applying our methodology on data for the 50 US states from 1980 to 2006, we obtain more precise and consistent results than previous studies. We find procyclical mortality in all age groups. We find clear procyclical mortality due to respiratory disease and traffic injuries. Predominantly procyclical cardiovascular disease mortality and countercyclical suicide are subject to substantial state-to-state variation. Neither cancer nor homicide have significant macroeconomic association.

Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends

PubMed Central

Ionides, Edward L.; Wang, Zhen; Tapia Granados, José A.

2013-01-01

Many investigations have used panel methods to study the relationships between fluctuations in economic activity and mortality. A broad consensus has emerged on the overall procyclical nature of mortality: perhaps counter-intuitively, mortality typically rises above its trend during expansions. This consensus has been tarnished by inconsistent reports on the specific age groups and mortality causes involved. We show that these inconsistencies result, in part, from the trend specifications used in previous panel models. Standard econometric panel analysis involves fitting regression models using ordinary least squares, employing standard errors which are robust to temporal autocorrelation. The model specifications include a fixed effect, and possibly a linear trend, for each time series in the panel. We propose alternative methodology based on nonlinear detrending. Applying our methodology on data for the 50 US states from 1980 to 2006, we obtain more precise and consistent results than previous studies. We find procyclical mortality in all age groups. We find clear procyclical mortality due to respiratory disease and traffic injuries. Predominantly procyclical cardiovascular disease mortality and countercyclical suicide are subject to substantial state-to-state variation. Neither cancer nor homicide have significant macroeconomic association. PMID:24587843

Effective Surfactants Blend Concentration Determination for O/W Emulsion Stabilization by Two Nonionic Surfactants by Simple Linear Regression.

PubMed

Hassan, A K

2015-01-01

In this work, O/W emulsion sets were prepared by using different concentrations of two nonionic surfactants. The two surfactants, tween 80(HLB=15.0) and span 80(HLB=4.3) were used in a fixed proportions equal to 0.55:0.45 respectively. HLB value of the surfactants blends were fixed at 10.185. The surfactants blend concentration is starting from 3% up to 19%. For each O/W emulsion set the conductivity was measured at room temperature (25±2°), 40, 50, 60, 70 and 80°. Applying the simple linear regression least squares method statistical analysis to the temperature-conductivity obtained data determines the effective surfactants blend concentration required for preparing the most stable O/W emulsion. These results were confirmed by applying the physical stability centrifugation testing and the phase inversion temperature range measurements. The results indicated that, the relation which represents the most stable O/W emulsion has the strongest direct linear relationship between temperature and conductivity. This relationship is linear up to 80°. This work proves that, the most stable O/W emulsion is determined via the determination of the maximum R² value by applying of the simple linear regression least squares method to the temperature-conductivity obtained data up to 80°, in addition to, the true maximum slope is represented by the equation which has the maximum R² value. Because the conditions would be changed in a more complex formulation, the method of the determination of the effective surfactants blend concentration was verified by applying it for more complex formulations of 2% O/W miconazole nitrate cream and the results indicate its reproducibility.

Psychosocial job quality and mental health among young workers: a fixed-effects regression analysis using 13 waves of annual data.

PubMed

Milner, Allison; Krnjack, Lauren; LaMontagne, Anthony D

2017-01-01

Objectives Entry into employment may be a time when a young person's well-being and mental health is challenged. Specifically, we examined the difference in mental health when a young person was "not in the labor force" (NILF) (ie, non-working activity such as participating in education) compared to being in a job with varying levels of psychosocial quality. Method The data source for this study was the Household Income and Labor Dynamics in Australia (HILDA) study, and the sample included 10 534 young people (aged ≤30 years). We used longitudinal fixed-effects regression to investigate within-person changes in mental health comparing circumstances where individuals were NILF to when they were employed in jobs of varying psychosocial quality. Results Compared to when individuals were not in the labor force, results suggest a statistically significant decline in mental health when young people were employed in jobs with poor psychosocial working conditions and an improvement in mental health when they were employed in jobs with optimal psychosocial working conditions. Our results were robust to various sensitivity tests, including adjustment for life events and the lagged effects of mental health and job stressors. Conclusions If causal, the results suggest that improving the psychosocial quality of work for younger workers will protect and promote their wellbeing, and may reduce the likelihood of mental health problems later on.

Longitudinal predictive ability of mapping models: examining post-intervention EQ-5D utilities derived from baseline MHAQ data in rheumatoid arthritis patients.

PubMed

Kontodimopoulos, Nick; Bozios, Panagiotis; Yfantopoulos, John; Niakas, Dimitris

2013-04-01

The purpose of this methodological study was to to provide insight into the under-addressed issue of the longitudinal predictive ability of mapping models. Post-intervention predicted and reported utilities were compared, and the effect of disease severity on the observed differences was examined. A cohort of 120 rheumatoid arthritis (RA) patients (60.0% female, mean age 59.0) embarking on therapy with biological agents completed the Modified Health Assessment Questionnaire (MHAQ) and the EQ-5D at baseline, and at 3, 6 and 12 months post-intervention. OLS regression produced a mapping equation to estimate post-intervention EQ-5D utilities from baseline MHAQ data. Predicted and reported utilities were compared with t test, and the prediction error was modeled, using fixed effects, in terms of covariates such as age, gender, time, disease duration, treatment, RF, DAS28 score, predicted and reported EQ-5D. The OLS model (RMSE = 0.207, R(2) = 45.2%) consistently underestimated future utilities, with a mean prediction error of 6.5%. Mean absolute differences between reported and predicted EQ-5D utilities at 3, 6 and 12 months exceeded the typically reported MID of the EQ-5D (0.03). According to the fixed-effects model, time, lower predicted EQ-5D and higher DAS28 scores had a significant impact on prediction errors, which appeared increasingly negative for lower reported EQ-5D scores, i.e., predicted utilities tended to be lower than reported ones in more severe health states. This study builds upon existing research having demonstrated the potential usefulness of mapping disease-specific instruments onto utility measures. The specific issue of longitudinal validity is addressed, as mapping models derived from baseline patients need to be validated on post-therapy samples. The underestimation of post-treatment utilities in the present study, at least in more severe patients, warrants further research before it is prudent to conduct cost-utility analyses in the context of RA by means of the MHAQ alone.

Meta-analysis and meta-regression analysis of outcomes of carotid endarterectomy and stenting in the elderly.

PubMed

Antoniou, George A; Georgiadis, George S; Georgakarakos, Efstratios I; Antoniou, Stavros A; Bessias, Nikos; Smyth, John Vincent; Murray, David; Lazarides, Miltos K

2013-12-01

Uncertainty exists about the influence of advanced age on the outcomes of carotid revascularization. To undertake a comprehensive review of the literature and conduct an analysis of the outcomes of carotid interventions in the elderly. A systematic literature review was conducted to identify articles comparing early outcomes of carotid endarterectomy (CEA) or carotid stenting (CAS) in elderly and young patients. Combined overall effect sizes were calculated using fixed or random effects models. Meta-regression models were formed to explore potential heterogeneity as a result of changes in practice over time. RESULTS Our analysis comprised 44 studies reporting data on 512,685 CEA and 75,201 CAS procedures. Carotid stenting was associated with increased incidence of stroke in elderly patients compared with their young counterparts (odds ratio [OR], 1.56; 95% CI, 1.40-1.75), whereas CEA had equivalent cerebrovascular outcomes in old and young age groups (OR, 0.94; 95% CI, 0.88-0.99). Carotid stenting had similar peri-interventional mortality risks in old and young patients (OR, 0.86; 95% CI, 0.72-1.03), whereas CEA was associated with heightened mortality in elderly patients (OR, 1.62; 95% CI, 1.47-1.77). The incidence of myocardial infarction was increased in patients of advanced age in both CEA and CAS (OR, 1.64; 95% CI, 1.57-1.72 and OR, 1.30; 95% CI, 1.16-1.45, respectively). Meta-regression analyses revealed a significant effect of publication date on peri-interventional stroke (P = .003) and mortality (P < .001) in CAS. Age should be considered when planning a carotid intervention. Carotid stenting has an increased risk of adverse cerebrovascular events in elderly patients but mortality equivalent to younger patients. Carotid endarterectomy is associated with similar neurologic outcomes in elderly and young patients, at the expense of increased mortality.

Intimate Partner Violence and Depression Symptom Severity among South African Women during Pregnancy and Postpartum: Population-Based Prospective Cohort Study

PubMed Central

Tsai, Alexander C.; Tomlinson, Mark; Comulada, W. Scott; Rotheram-Borus, Mary Jane

2016-01-01

Background Violence against women by intimate partners remains unacceptably common worldwide. The evidence base for the assumed psychological impacts of intimate partner violence (IPV) is derived primarily from studies conducted in high-income countries. A recently published systematic review identified 13 studies linking IPV to incident depression, none of which were conducted in sub-Saharan Africa. To address this gap in the literature, we analyzed longitudinal data collected during the course of a 3-y cluster-randomized trial with the aim of estimating the association between IPV and depression symptom severity. Methods and Findings We conducted a secondary analysis of population-based, longitudinal data collected from 1,238 pregnant women during a 3-y cluster-randomized trial of a home visiting intervention in Cape Town, South Africa. Surveys were conducted at baseline, 6 mo, 18 mo, and 36 mo (85% retention). The primary explanatory variable of interest was exposure to four types of physical IPV in the past year. Depression symptom severity was measured using the Xhosa version of the ten-item Edinburgh Postnatal Depression Scale. In a pooled cross-sectional multivariable regression model adjusting for potentially confounding time-fixed and time-varying covariates, lagged IPV intensity had a statistically significant association with depression symptom severity (regression coefficient b = 1.04; 95% CI, 0.61–1.47), with estimates from a quantile regression model showing greater adverse impacts at the upper end of the conditional depression distribution. Fitting a fixed effects regression model accounting for all time-invariant confounding (e.g., history of childhood sexual abuse) yielded similar findings (b = 1.54; 95% CI, 1.13–1.96). The magnitudes of the coefficients indicated that a one–standard-deviation increase in IPV intensity was associated with a 12.3% relative increase in depression symptom severity over the same time period. The most important limitations of our study include exposure assessment that lacked measurement of sexual violence, which could have caused us to underestimate the severity of exposure; the extended latency period in the lagged analysis, which could have caused us to underestimate the strength of the association; and outcome assessment that was limited to the use of a screening instrument for depression symptom severity. Conclusions In this secondary analysis of data from a population-based, 3-y cluster-randomized controlled trial, IPV had a statistically significant association with depression symptom severity. The estimated associations were relatively large in magnitude, consistent with findings from high-income countries, and robust to potential confounding by time-invariant factors. Intensive health sector responses to reduce IPV and improve women’s mental health should be explored. PMID:26784110

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

PubMed Central

Robbins, Blaine

2013-01-01

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

Effect of mobile phone use on metal ion release from fixed orthodontic appliances.

PubMed

Saghiri, Mohammad Ali; Orangi, Jafar; Asatourian, Armen; Mehriar, Peiman; Sheibani, Nader

2015-06-01

The aim of this study was to evaluate the effect of exposure to radiofrequency electromagnetic fields emitted by mobile phones on the level of nickel in saliva. Fifty healthy patients with fixed orthodontic appliances were asked not to use their cell phones for a week, and their saliva samples were taken at the end of the week (control group). The patients recorded their time of mobile phone usage during the next week and returned for a second saliva collection (experimental group). Samples at both times were taken between 8:00 and 10:00 pm, and the nickel levels were measured. Two-tailed paired-samples t test, linear regression, independent t test, and 1-way analysis of variance were used for data analysis. The 2-tailed paired-samples t test showed significant differences between the levels of nickel in the control and experimental groups (t [49] = 9.967; P <0.001). The linear regression test showed a significant relationship between mobile phone usage time and the nickel release (F [1, 48] = 60.263; P <0.001; R(2) = 0.577). Mobile phone usage has a time-dependent influence on the concentration of nickel in the saliva of patients with orthodontic appliances. Copyright © 2015 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Time series sightability modeling of animal populations.

PubMed

ArchMiller, Althea A; Dorazio, Robert M; St Clair, Katherine; Fieberg, John R

2018-01-01

Logistic regression models-or "sightability models"-fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

Estimating radiative feedbacks from stochastic fluctuations in surface temperature and energy imbalance

NASA Astrophysics Data System (ADS)

Proistosescu, C.; Donohoe, A.; Armour, K.; Roe, G.; Stuecker, M. F.; Bitz, C. M.

2017-12-01

Joint observations of global surface temperature and energy imbalance provide for a unique opportunity to empirically constrain radiative feedbacks. However, the satellite record of Earth's radiative imbalance is relatively short and dominated by stochastic fluctuations. Estimates of radiative feedbacks obtained by regressing energy imbalance against surface temperature depend strongly on sampling choices and on assumptions about whether the stochastic fluctuations are primarily forced by atmospheric or oceanic variability (e.g. Murphy and Forster 2010, Dessler 2011, Spencer and Braswell 2011, Forster 2016). We develop a framework around a stochastic energy balance model that allows us to parse the different contributions of atmospheric and oceanic forcing based on their differing impacts on the covariance structure - or lagged regression - of temperature and radiative imbalance. We validate the framework in a hierarchy of general circulation models: the impact of atmospheric forcing is examined in unforced control simulations of fixed sea-surface temperature and slab ocean model versions; the impact of oceanic forcing is examined in coupled simulations with prescribed ENSO variability. With the impact of atmospheric and oceanic forcing constrained, we are able to predict the relationship between temperature and radiative imbalance in a fully coupled control simulation, finding that both forcing sources are needed to explain the structure of the lagged-regression. We further model the dependence of feedback estimates on sampling interval by considering the effects of a finite equilibration time for the atmosphere, and issues of smoothing and aliasing. Finally, we develop a method to fit the stochastic model to the short timeseries of temperature and radiative imbalance by performing a Bayesian inference based on a modified version of the spectral Whittle likelihood. We are thus able to place realistic joint uncertainty estimates on both stochastic forcing and radiative feedbacks derived from observational records. We find that these records are, as of yet, too short to be useful in constraining radiative feedbacks, and we provide estimates of how the uncertainty narrows as a function of record length.

Factors explaining priority setting at community mental health centres: a quantitative analysis of referral assessments.

PubMed

Grepperud, Sverre; Holman, Per Arne; Wangen, Knut Reidar

2014-12-14

Clinicians at Norwegian community mental health centres assess referrals from general practitioners and classify them into three priority groups (high priority, low priority, and refusal) according to need where need is defined by three prioritization criteria (severity, effect, and cost-effectiveness). In this study, we seek to operationalize the three criteria and analyze to what extent they have an effect on clinical-level priority setting after controlling for clinician characteristics and organisational factors. Twenty anonymous referrals were rated by 42 admission team members employed at 14 community mental health centres in the South-East Health Region of Norway. Intra-class correlation coefficients were calculated and logistic regressions were performed. Variation in clinicians' assessments of the three criteria was highest for effect and cost-effectiveness. An ordered logistic regression model showed that all three criteria for prioritization, three clinician characteristics (education, being a manager or not, and "guideline awareness"), and the centres themselves (fixed effects), explained priority decisions. The relative importance of the explanatory factors, however, depended on the priority decision studied. For the classification of all admitted patients into high- and low-priority groups, all clinician characteristics became insignificant. For the classification of patients, into those admitted and non-admitted, one criterion (effect) and "being a manager or not" became insignificant, while profession ("being a psychiatrist") became significant. Our findings suggest that variation in priority decisions can be reduced by: (i) reducing the disagreement in clinicians' assessments of cost-effectiveness and effect, and (ii) restricting priority decisions to clinicians with a similar background (education, being a manager or not, and "guideline awareness").

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids

PubMed Central

Ali, S. M.; Mehmood, C. A; Khan, B.; Jawad, M.; Farid, U; Jadoon, J. K.; Ali, M.; Tareen, N. K.; Usman, S.; Majid, M.; Anwar, S. M.

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion. PMID:27314229

Stochastic and Statistical Analysis of Utility Revenues and Weather Data Analysis for Consumer Demand Estimation in Smart Grids.

PubMed

Ali, S M; Mehmood, C A; Khan, B; Jawad, M; Farid, U; Jadoon, J K; Ali, M; Tareen, N K; Usman, S; Majid, M; Anwar, S M

2016-01-01

In smart grid paradigm, the consumer demands are random and time-dependent, owning towards stochastic probabilities. The stochastically varying consumer demands have put the policy makers and supplying agencies in a demanding position for optimal generation management. The utility revenue functions are highly dependent on the consumer deterministic stochastic demand models. The sudden drifts in weather parameters effects the living standards of the consumers that in turn influence the power demands. Considering above, we analyzed stochastically and statistically the effect of random consumer demands on the fixed and variable revenues of the electrical utilities. Our work presented the Multi-Variate Gaussian Distribution Function (MVGDF) probabilistic model of the utility revenues with time-dependent consumer random demands. Moreover, the Gaussian probabilities outcome of the utility revenues is based on the varying consumer n demands data-pattern. Furthermore, Standard Monte Carlo (SMC) simulations are performed that validated the factor of accuracy in the aforesaid probabilistic demand-revenue model. We critically analyzed the effect of weather data parameters on consumer demands using correlation and multi-linear regression schemes. The statistical analysis of consumer demands provided a relationship between dependent (demand) and independent variables (weather data) for utility load management, generation control, and network expansion.

Effectiveness of Vildagliptin in Clinical Practice: Pooled Analysis of Three Korean Observational Studies (the VICTORY Study).

PubMed

Suh, Sunghwan; Song, Sun Ok; Kim, Jae Hyeon; Cho, Hyungjin; Lee, Woo Je; Lee, Byung-Wan

2017-01-01

The present observational study aimed to evaluate the clinical effectiveness of vildagliptin with metformin in Korean patients with type 2 diabetes mellitus (T2DM). Data were pooled from the vildagliptin postmarketing survey (PMS), the vildagliptin/metformin fixed drug combination (DC) PMS, and a retrospective observational study of vildagliptin/metformin (fixed DC or free DC). The effectiveness endpoint was the proportion of patients who achieved a glycemic target (HbA1c) of ≤7.0% at 24 weeks. In total, 4303 patients were included in the analysis; of these, 2087 patients were eligible. The mean patient age was 56.99 ± 11.25 years. Overall, 58.94% patients achieved an HbA1c target of ≤7.0% at 24 weeks. The glycemic target achievement rate was significantly greater in patients with baseline HbA1c < 7.5% versus ≥7.5% (84.64% versus 43.97%), receiving care at the hospital versus clinic (67.95% versus 52.33%), and receiving vildagliptin/metformin fixed DC versus free DC (70.69% versus 55.42%). Multivariate logistic regression analysis indicated that disease duration ( P < 0.0001), baseline HbA1c ( P < 0.0001), and DC type ( P = 0.0103) had significant effects on drug effectiveness. Vildagliptin plus metformin appeared as an effective treatment option for patients with T2DM in clinical practice settings in Korea.

Tailoring treatment of haemophilia B: accounting for the distribution and clearance of standard and extended half-life FIX concentrates.

PubMed

Iorio, Alfonso; Fischer, Kathelijn; Blanchette, Victor; Rangarajan, Savita; Young, Guy; Morfini, Massimo

2017-06-02

The prophylactic administration of factor IX (FIX) is considered the most effective treatment for haemophilia B. The inter-individual variability and complexity of the pharmacokinetics (PK) of FIX, and the rarity of the disease have hampered identification of an optimal treatment regimens. The recent introduction of extended half-life recombinant FIX molecules (EHL-rFIX), has prompted a thorough reassessment of the clinical efficacy, PK and pharmacodynamics of plasma-derived and recombinant FIX. First, using longer sampling times and multi-compartmental PK models has led to more precise (and favourable) PK for FIX than was appreciated in the past. Second, investigating the distribution of FIX in the body beyond the vascular space (which is implied by its complex kinetics) has opened a new research field on the role for extravascular FIX. Third, measuring plasma levels of EHL-rFIX has shown that different aPTT reagents have different accuracy in measuring different FIX molecules. How will this new knowledge reflect on clinical practice? Clinical decision making in haemophilia B requires some caution and expertise. First, comparisons between different FIX molecules must be assessed taking into consideration the comparability of the populations studied and the PK models used. Second, individual PK estimates must rely on multi-compartmental models, and would benefit from adopting a population PK approach. Optimal sampling times need to be adapted to the prolonged half-life of the new EHL FIX products. Finally, costs considerations may apply, which is beyond the scope of this manuscript but might be deeply connected with the PK considerations discussed in this communication.

Optimizing Preprocessing and Analysis Pipelines for Single-Subject FMRI. I. Standard Temporal Motion and Physiological Noise Correction Methods

PubMed Central

Churchill, Nathan W.; Oder, Anita; Abdi, Hervé; Tam, Fred; Lee, Wayne; Thomas, Christopher; Ween, Jon E.; Graham, Simon J.; Strother, Stephen C.

2016-01-01

Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747–771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89–95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or “pipeline”) in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was demonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods. PMID:21455942

The politics of hope and despair: the effect of presidential election outcomes on suicide rates.

PubMed

Classen, Timothy J; Dunn, Richard A

2010-01-01

This article examines the effect of election outcomes on suicide rates by combining the theory of social integration developed by Durkheim with the models of rational choice used in economics. Theory predicts that states with a greater percentage of residents who supported the losing candidate would tend to exhibit a relative increase in suicide rates. However, being around others who also supported the losing candidate may indicate a greater degree of social integration at the local level, thereby lowering relative suicide rates. We therefore use fixed-effects regression of state suicide rates from 1981 to 2005 on state election outcomes during presidential elections to determine which effect is stronger. We find that the local effect of social integration is dominant. The suicide rate when a state supports the losing candidate will tend to be lower than if the state had supported the winning candidate-4.6 percent lower for males and 5.3 percent lower for females. Social integration works at many levels; it not only affects suicide risk directly, but can mediate other shocks that influence suicide risk.

Effects of relationship duration, cohabitation, and marriage on the frequency of intercourse in couples: Findings from German panel data.

PubMed

Schröder, Jette; Schmiedeberg, Claudia

2015-07-01

Research into the changes in the frequency of sexual intercourse is (with few exceptions) limited to cross-sectional analyses of marital duration. We investigate the frequency of intercourse while taking into account relationship duration as well as the duration of cohabitation and marriage, effects of parenthood, and relationship quality. For the analysis we apply fixed effects regression models using data from the German Family Panel (pairfam), a nationwide randomly sampled German panel survey. Our findings imply that the drop in sex frequency occurs early in the relationship, whereas neither cohabitation nor marriage affects the frequency of intercourse to a significant extent. Sex frequency is reduced during pregnancy and as long as the couple has small children, but becomes revived later on. Relationship quality is found to play a role as well. These results are contrary to the honeymoon effect found in earlier research, but indicate that in times of postponed marriage an analogous effect may be at work in the initial period of the relationship. Copyright © 2015 Elsevier Inc. All rights reserved.

The Causal Effects of Father Absence

PubMed Central

McLanahan, Sara; Tach, Laura; Schneider, Daniel

2014-01-01

The literature on father absence is frequently criticized for its use of cross-sectional data and methods that fail to take account of possible omitted variable bias and reverse causality. We review studies that have responded to this critique by employing a variety of innovative research designs to identify the causal effect of father absence, including studies using lagged dependent variable models, growth curve models, individual fixed effects models, sibling fixed effects models, natural experiments, and propensity score matching models. Our assessment is that studies using more rigorous designs continue to find negative effects of father absence on offspring well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. The evidence is strongest and most consistent for outcomes such as high school graduation, children’s social-emotional adjustment, and adult mental health. PMID:24489431

Sedation-analgesia with propofol and remifentanil: concentrations required to avoid gag reflex in upper gastrointestinal endoscopy.

PubMed

Borrat, Xavier; Valencia, José Fernando; Magrans, Rudys; Gimenez-Mila, Marc; Mellado, Ricard; Sendino, Oriol; Perez, Maria; Nunez, Matilde; Jospin, Mathieu; Jensen, Erik Weber; Troconiz, Inaki; Gambus, Pedro L

2015-07-01

The purpose of this study was to identify optimal target propofol and remifentanil concentrations to avoid a gag reflex in response to insertion of an upper gastrointestinal endoscope. Patients presenting for endoscopy received target-controlled infusions (TCI) of both propofol and remifentanil for sedation-analgesia. Patients were randomized to 4 groups of fixed target effect-site concentrations: remifentanil 1 ng•mL (REMI 1) or 2 ng•mL (REMI 2) and propofol 2 μg•mL (PROP 2) or 3 μg•mL (PROP 3). For each group, the other drug (propofol for the REMI groups and vice versa) was increased or decreased using the "up-down" method based on the presence or absence of a gag response in the previous patient. A modified isotonic regression method was used to estimate the median effective Ce,50 from the up-down method in each group. A concentration-effect (sigmoid Emax) model was built to estimate the corresponding Ce,90 for each group. These data were used to estimate propofol bolus doses and remifentanil infusion rates that would achieve effect-site concentrations between Ce,50 and Ce,90 when a TCI system is not available for use. One hundred twenty-four patients were analyzed. To achieve between a 50% and 90% probability of no gag response, propofol TCIs were between 2.40 and 4.23 μg•mL (that could be achieved with a bolus of 1 mg•kg) when remifentanil TCI was fixed at 1 ng•mL, and target propofol TCIs were between 2.15 and 2.88 μg•mL (that could be achieved with a bolus of 0.75 mg•kg) when remifentanil TCI was fixed at 2 ng•mL. Remifentanil ranges were 1.00 to 4.79 ng•mL and 0.72 to 3.19 ng•mL when propofol was fixed at 2 and 3 μg•mL, respectively. We identified a set of propofol and remifentanil TCIs that blocked the gag response to endoscope insertion in patients undergoing endoscopy. Propofol bolus doses and remifentanil infusion rates designed to achieve similar effect-site concentrations can be used to prevent gag response when TCI is not available.

THE ROLE OF LOCATION IN EVALUATING RACIAL WAGE DISPARITY.

PubMed

Black, Dan A; Kolesnikova, Natalia; Sanders, Seth G; Taylor, Lowell J

2013-05-01

A standard object of empirical analysis in labor economics is a modified Mincer wage function in which an individual's log wage is specified to be a function of education, experience, and an indicator variable identifying race. We analyze this approach in a context in which individuals live and work in different locations (and thus face different housing prices and wages). Our model provides a justification for the traditional approach, but with the important caveat that the regression should include location-specific fixed effects. Empirical analyses of men in U.S. labor markets demonstrate that failure to condition on location causes us to (i) overstate the decline in black-white wage disparity over the past 60 years, and (ii) understate racial and ethnic wage gaps that remain after taking into account measured cognitive skill differences that emerge when workers are young.

Single-parent households and children's educational achievement: A state-level analysis.

PubMed

Amato, Paul R; Patterson, Sarah; Beattie, Brett

2015-09-01

Although many studies have examined associations between family structure and children's educational achievement at the individual level, few studies have considered how the increase in single-parent households may have affected children's educational achievement at the population level. We examined changes in the percentage of children living with single parents between 1990 and 2011 and state mathematics and reading scores on the National Assessment of Educational Progress. Regression models with state and year fixed effects revealed that changes in the percentage of children living with single parents were not associated with test scores. Increases in maternal education, however, were associated with improvements in children's test scores during this period. These results do not support the notion that increases in single parenthood have had serious consequences for U.S. children's school achievement. Copyright © 2015 Elsevier Inc. All rights reserved.

The role of personality in health care use: Results of a population-based longitudinal study in Germany

PubMed Central

König, Hans-Helmut

2017-01-01

Objective To determine the role of personality in health care use longitudinally. Methods Data were derived from the German Socio-Economic Panel (GSOEP), a nationally representative, longitudinal cohort study of German households starting in 1984. Concentrating on the role of personality, we used data from the years 2005, 2009 and 2013. Personality was measured by using the GSOEP Big Five Inventory (BFI-S). Number of physician visits in the last 3 months and hospital stays in the last year were used as measures of health care use. Results Adjusting for predisposing factors, enabling resources, and need factors, fixed effects regressions revealed that physician visits increased with increasing neuroticism, whereas extraversion, openness to experience, agreeableness and conscientiousness did not affect physician visits in a significant way. The effect of self-rated health on physician visits was significantly moderated by neuroticism. Moreover, fixed effects regressions revealed that the probability of hospitalization in the past year increased with increasing extraversion, whereas the other personality factors did not affect this outcome measure significantly. Conclusion Our findings suggest that changes in neuroticism are associated with changes in physician visits and that changes in extraversion are associated with the probability of hospitalization. Since recent studies have shown that treatments can modify personality traits, developing interventional strategies should take into account personality factors. For example, efforts to intervene in changing neuroticism might have beneficial effects for the healthcare system. PMID:28746388

Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

PubMed

Holmes, John B; Dodds, Ken G; Lee, Michael A

2017-03-02

An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

A flexible Bayesian hierarchical model of preterm birth risk among US Hispanic subgroups in relation to maternal nativity and education

PubMed Central

2011-01-01

Background Previous research has documented heterogeneity in the effects of maternal education on adverse birth outcomes by nativity and Hispanic subgroup in the United States. In this article, we considered the risk of preterm birth (PTB) using 9 years of vital statistics birth data from New York City. We employed finer categorizations of exposure than used previously and estimated the risk dose-response across the range of education by nativity and ethnicity. Methods Using Bayesian random effects logistic regression models with restricted quadratic spline terms for years of completed maternal education, we calculated and plotted the estimated posterior probabilities of PTB (gestational age < 37 weeks) for each year of education by ethnic and nativity subgroups adjusted for only maternal age, as well as with more extensive covariate adjustments. We then estimated the posterior risk difference between native and foreign born mothers by ethnicity over the continuous range of education exposures. Results The risk of PTB varied substantially by education, nativity and ethnicity. Native born groups showed higher absolute risk of PTB and declining risk associated with higher levels of education beyond about 10 years, as did foreign-born Puerto Ricans. For most other foreign born groups, however, risk of PTB was flatter across the education range. For Mexicans, Central Americans, Dominicans, South Americans and "Others", the protective effect of foreign birth diminished progressively across the educational range. Only for Puerto Ricans was there no nativity advantage for the foreign born, although small numbers of foreign born Cubans limited precision of estimates for that group. Conclusions Using flexible Bayesian regression models with random effects allowed us to estimate absolute risks without strong modeling assumptions. Risk comparisons for any sub-groups at any exposure level were simple to calculate. Shrinkage of posterior estimates through the use of random effects allowed for finer categorization of exposures without restricting joint effects to follow a fixed parametric scale. Although foreign born Hispanic women with the least education appeared to generally have low risk, this seems likely to be a marker for unmeasured environmental and behavioral factors, rather than a causally protective effect of low education itself. PMID:21504612

A flexible Bayesian hierarchical model of preterm birth risk among US Hispanic subgroups in relation to maternal nativity and education.

PubMed

Kaufman, Jay S; MacLehose, Richard F; Torrone, Elizabeth A; Savitz, David A

2011-04-19

Previous research has documented heterogeneity in the effects of maternal education on adverse birth outcomes by nativity and Hispanic subgroup in the United States. In this article, we considered the risk of preterm birth (PTB) using 9 years of vital statistics birth data from New York City. We employed finer categorizations of exposure than used previously and estimated the risk dose-response across the range of education by nativity and ethnicity. Using Bayesian random effects logistic regression models with restricted quadratic spline terms for years of completed maternal education, we calculated and plotted the estimated posterior probabilities of PTB (gestational age < 37 weeks) for each year of education by ethnic and nativity subgroups adjusted for only maternal age, as well as with more extensive covariate adjustments. We then estimated the posterior risk difference between native and foreign born mothers by ethnicity over the continuous range of education exposures. The risk of PTB varied substantially by education, nativity and ethnicity. Native born groups showed higher absolute risk of PTB and declining risk associated with higher levels of education beyond about 10 years, as did foreign-born Puerto Ricans. For most other foreign born groups, however, risk of PTB was flatter across the education range. For Mexicans, Central Americans, Dominicans, South Americans and "Others", the protective effect of foreign birth diminished progressively across the educational range. Only for Puerto Ricans was there no nativity advantage for the foreign born, although small numbers of foreign born Cubans limited precision of estimates for that group. Using flexible Bayesian regression models with random effects allowed us to estimate absolute risks without strong modeling assumptions. Risk comparisons for any sub-groups at any exposure level were simple to calculate. Shrinkage of posterior estimates through the use of random effects allowed for finer categorization of exposures without restricting joint effects to follow a fixed parametric scale. Although foreign born Hispanic women with the least education appeared to generally have low risk, this seems likely to be a marker for unmeasured environmental and behavioral factors, rather than a causally protective effect of low education itself.

Time series sightability modeling of animal populations

USGS Publications Warehouse

ArchMiller, Althea A.; Dorazio, Robert; St. Clair, Katherine; Fieberg, John R.

2018-01-01

Logistic regression models—or “sightability models”—fit to detection/non-detection data from marked individuals are often used to adjust for visibility bias in later detection-only surveys, with population abundance estimated using a modified Horvitz-Thompson (mHT) estimator. More recently, a model-based alternative for analyzing combined detection/non-detection and detection-only data was developed. This approach seemed promising, since it resulted in similar estimates as the mHT when applied to data from moose (Alces alces) surveys in Minnesota. More importantly, it provided a framework for developing flexible models for analyzing multiyear detection-only survey data in combination with detection/non-detection data. During initial attempts to extend the model-based approach to multiple years of detection-only data, we found that estimates of detection probabilities and population abundance were sensitive to the amount of detection-only data included in the combined (detection/non-detection and detection-only) analysis. Subsequently, we developed a robust hierarchical modeling approach where sightability model parameters are informed only by the detection/non-detection data, and we used this approach to fit a fixed-effects model (FE model) with year-specific parameters and a temporally-smoothed model (TS model) that shares information across years via random effects and a temporal spline. The abundance estimates from the TS model were more precise, with decreased interannual variability relative to the FE model and mHT abundance estimates, illustrating the potential benefits from model-based approaches that allow information to be shared across years.

The Relationship Between Gun Ownership and Stranger and Nonstranger Firearm Homicide Rates in the United States, 1981–2010

PubMed Central

Negussie, Yamrot; Vanture, Sarah; Pleskunas, Jane; Ross, Craig S.; King, Charles

2014-01-01

Objectives. We examined the relationship between gun ownership and stranger versus nonstranger homicide rates. Methods. Using data from the Supplemental Homicide Reports of the Federal Bureau of Investigation’s Uniform Crime Reports for all 50 states for 1981 to 2010, we modeled stranger and nonstranger homicide rates as a function of state-level gun ownership, measured by a proxy, controlling for potential confounders. We used a negative binomial regression model with fixed effects for year, accounting for clustering of observations among states by using generalized estimating equations. Results. We found no robust, statistically significant correlation between gun ownership and stranger firearm homicide rates. However, we found a positive and significant association between gun ownership and nonstranger firearm homicide rates. The incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% confidence interval = 1.009, 1.019). Conclusions. Our findings challenge the argument that gun ownership deters violent crime, in particular, homicides. PMID:25121817

Impact of price deregulation policy on the affordability of essential medicines for women's health: a panel data analysis.

PubMed

Liu, Junjie; Wang, Liming; Liu, Chenxi; Zhang, Xinping

2017-12-01

A new policy which required deregulation on prices of off-patent medicines for women's health during procurement was introduced in China in September 2015. The current study examines this policy's impact on the affordability of essential medicines for women's health. Based on product-level panel data, a fixed effect regression model is employed by using procurement records from Hubei Centralist Tender for Drug Purchase platform. In the model, Affordability was measured with prices. The Competition consists of two parts: generic competition and therapeutic class competition which are measured with generic competitors and therapeutic substitutes. Instrument variable is used to deal with endogeneity. The policy helped control prices of essential medicines for women's health. Generic competition helped control prices, however, therapeutic class competition caused higher prices. The new policy helped enhance the affordability of essential medicines for women's health as expected, which provides empirical evidence on price deregulation. Besides, generic competition is important in price control despite strict regulatory system in China.

The relationship between gun ownership and stranger and nonstranger firearm homicide rates in the United States, 1981-2010.

PubMed

Siegel, Michael; Negussie, Yamrot; Vanture, Sarah; Pleskunas, Jane; Ross, Craig S; King, Charles

2014-10-01

We examined the relationship between gun ownership and stranger versus nonstranger homicide rates. Using data from the Supplemental Homicide Reports of the Federal Bureau of Investigation's Uniform Crime Reports for all 50 states for 1981 to 2010, we modeled stranger and nonstranger homicide rates as a function of state-level gun ownership, measured by a proxy, controlling for potential confounders. We used a negative binomial regression model with fixed effects for year, accounting for clustering of observations among states by using generalized estimating equations. We found no robust, statistically significant correlation between gun ownership and stranger firearm homicide rates. However, we found a positive and significant association between gun ownership and nonstranger firearm homicide rates. The incidence rate ratio for nonstranger firearm homicide rate associated with gun ownership was 1.014 (95% confidence interval=1.009, 1.019). Our findings challenge the argument that gun ownership deters violent crime, in particular, homicides.

Evaluation of some random effects methodology applicable to bird ringing data

USGS Publications Warehouse

Burnham, K.P.; White, Gary C.

2002-01-01

Existing models for ring recovery and recapture data analysis treat temporal variations in annual survival probability (S) as fixed effects. Often there is no explainable structure to the temporal variation in S1,..., Sk; random effects can then be a useful model: Si = E(S) + ??i. Here, the temporal variation in survival probability is treated as random with average value E(??2) = ??2. This random effects model can now be fit in program MARK. Resultant inferences include point and interval estimation for process variation, ??2, estimation of E(S) and var (E??(S)) where the latter includes a component for ??2 as well as the traditional component for v??ar(S??\\S??). Furthermore, the random effects model leads to shrinkage estimates, Si, as improved (in mean square error) estimators of Si compared to the MLE, S??i, from the unrestricted time-effects model. Appropriate confidence intervals based on the Si are also provided. In addition, AIC has been generalized to random effects models. This paper presents results of a Monte Carlo evaluation of inference performance under the simple random effects model. Examined by simulation, under the simple one group Cormack-Jolly-Seber (CJS) model, are issues such as bias of ??s2, confidence interval coverage on ??2, coverage and mean square error comparisons for inference about Si based on shrinkage versus maximum likelihood estimators, and performance of AIC model selection over three models: Si ??? S (no effects), Si = E(S) + ??i (random effects), and S1,..., Sk (fixed effects). For the cases simulated, the random effects methods performed well and were uniformly better than fixed effects MLE for the Si.

Estimation of genetic parameters for milk yield in Murrah buffaloes by Bayesian inference.

PubMed

Breda, F C; Albuquerque, L G; Euclydes, R F; Bignardi, A B; Baldi, F; Torres, R A; Barbosa, L; Tonhati, H

2010-02-01

Random regression models were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Bayesian inference. Data comprised 17,935 test-day milk records from 1,433 buffaloes. Twelve models were tested using different combinations of third-, fourth-, fifth-, sixth-, and seventh-order orthogonal polynomials of weeks of lactation for additive genetic and permanent environmental effects. All models included the fixed effects of contemporary group, number of daily milkings and age of cow at calving as covariate (linear and quadratic effect). In addition, residual variances were considered to be heterogeneous with 6 classes of variance. Models were selected based on the residual mean square error, weighted average of residual variance estimates, and estimates of variance components, heritabilities, correlations, eigenvalues, and eigenfunctions. Results indicated that changes in the order of fit for additive genetic and permanent environmental random effects influenced the estimation of genetic parameters. Heritability estimates ranged from 0.19 to 0.31. Genetic correlation estimates were close to unity between adjacent test-day records, but decreased gradually as the interval between test-days increased. Results from mean squared error and weighted averages of residual variance estimates suggested that a model considering sixth- and seventh-order Legendre polynomials for additive and permanent environmental effects, respectively, and 6 classes for residual variances, provided the best fit. Nevertheless, this model presented the largest degree of complexity. A more parsimonious model, with fourth- and sixth-order polynomials, respectively, for these same effects, yielded very similar genetic parameter estimates. Therefore, this last model is recommended for routine applications. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Workplace Determinants of Social Capital: Cross-Sectional and Longitudinal Evidence from a Finnish Cohort Study

PubMed Central

Oksanen, Tuula; Kawachi, Ichiro; Kouvonen, Anne; Takao, Soshi; Suzuki, Etsuji; Virtanen, Marianna; Pentti, Jaana; Kivimäki, Mika; Vahtera, Jussi

2013-01-01

Objective To examine which contextual features of the workplace are associated with social capital. Methods This is a cohort study of 43,167 employees in 3090 Finnish public sector workplaces who responded to a survey of individual workplace social capital in 2000–02 (response rate 68%). We used ecometrics approach to estimate social capital of work units. Features of the workplace were work unit's demographic and employment patterns and size, obtained from employers' administrative records. We used multilevel-multinomial logistic regression models to examine cross-sectionally whether these features were associated with social capital between individuals and work units. Fixed effects models were used for longitudinal analyses in a subsample of 12,108 individuals to examine the effects of changes in workplace characteristics on changes in social capital between 2000 and 2004. Results After adjustment for individual characteristics, an increase in work unit size reduced the odds of high levels of individual workplace social capital (odds ratio 0.94, 95% confidence interval 0.91–0.98 per 30-person-year increase). A 20% increase in the proportion of manual and male employees reduced the odds of high levels of social capital by 8% and 23%, respectively. A 30% increase in temporary employees and a 20% increase in employee turnover were associated with 11% (95% confidence interval 1.04–1.17) and 24% (95% confidence interval 1.18–1.30) higher odds of having high levels of social capital respectively). Results from fixed effects models within individuals, adjusted for time-varying covariates, and from social capital of the work units yielded consistent results. Conclusions These findings suggest that workplace social capital is contextually patterned. Workplace demographic and employment patterns as well as the size of the work unit are important in understanding variations in workplace social capital between individuals and workplaces. PMID:23776555

Comparison of biomechanical function at ideal and varied surgical placement for two lumbar artificial disc implant designs: mobile-core versus fixed-core.

PubMed

Moumene, Missoum; Geisler, Fred H

2007-08-01

Finite element model. To estimate the effect of lumbar mobile-core and fixed-core artificial disc design and placement on the loading of the facet joints, and stresses on the polyethylene core. Although both mobile-core and fixed-core lumbar artificial disc designs have been used clinically, the effect of their design and the effect of placement within the disc space on the structural element loading, and in particular the facets and the implant itself, have not been investigated. A 3D nonlinear finite element model of an intact ligamentous L4-L5 motion segment was developed and validated in all 6 df based on previous experiments conducted on human cadavers. Facet loading of a mobile-core TDR and a fixed-core TDR were estimated with 4 different prosthesis placements for 3 different ranges of motion. Placing the mobile-core TDR anywhere within the disc space reduced facet loading by more than 50%, while the fixed-core TDR increased facet loading by more than 10% when compared with the intact disc in axial rotation. For central (ideal) placement, the mobile- and fixed-core implants were subjected to compressive stresses on the order of 3 MPa and 24 MPa, respectively. The mobile-core stresses were not affected by implant placement, while the fixed-core stresses increased by up to 40%. A mobile-core artificial disc design is less sensitive to placement, and unloads the facet joints, compared with a fixed-core design. The decreased core stress may result in a reduced potential for wear in a mobile-core prosthesis compared with a fixed-core prosthesis, which may increase the functional longevity of the device.

Association between hepatitis B virus/hepatitis C virus infection and primary hepatocellular carcinoma risk: A meta-analysis based on Chinese population.

PubMed

Li, Libo; Lan, Xiaolin

2016-12-01

To assess the relationship between hepatitis B virus (HBV), hepatitis C virus (HCV), and HBV/HCV double infection and hepatocellular carcinoma risk in Chinese population. The databases of PubMed and CNKI were electronic searched by reviewers according to the searching words of HBV, HCV, and hepatocellular carcinoma. The related case-control studies or cohort studies were included. The association between virus infection and hepatocellular carcinoma risk was demonstrated by odds ratio (OR) and 95% confidence interval (95% CI). The data were pooled by fixed or random effects model according to the statistical heterogeneity. The publication bias was assessed by Begg's funnel plot and Egger's linear regression test. Finally, 13 publications were included in this meta-analysis. For significant statistical heterogeneity (I2 = 99.8%,P = 0.00), the OR was pooled by random effects model. The pooled results showed that HBV infection can significantly increase the risk of developing hepatocellular carcinoma (OR = 58.01, 95% CI: 44.27-71.75); statistical heterogeneity analysis showed that significant heterogeneity existed in evaluation of HCV infection and hepatocellular carcinoma risk across the included 13 studies I2 = 77.78%, P = 0.00). The OR was pooled by random effects model. The pooled results showed that HCV infection can significantly increase the risk of developing hepatocellular carcinoma (OR = 2.34, 95% CI: 1.20-3.47); significant heterogeneity did not exist in evaluation HBV/HCV double infection and hepatocellular carcinoma risk for the included 13 studies (I2 = 0.00%,P = 0.80). The OR was pooled by fixed effects model. The pooled results showed that HBV/HCV double infection can significantly increase the risk of developing hepatocellular carcinoma (OR = 11.39, 95% CI: 4.58-18.20). No publication bias was found in the aspects of HBV, HCV, and HBV/HCV double infection and hepatocellular carcinoma. For Chinese population, HBV, HCV or HBV/HCV double infection can significantly increase the risk of developing hepatocellular carcinoma.

Workplace bullying a risk for permanent employees.

PubMed

Keuskamp, Dominic; Ziersch, Anna M; Baum, Fran E; Lamontagne, Anthony D

2012-04-01

We tested the hypothesis that the risk of experiencing workplace bullying was greater for those employed on casual contracts compared to permanent or ongoing employees. A cross-sectional population-based telephone survey was conducted in South Australia in 2009. Employment arrangements were classified by self-report into four categories: permanent, casual, fixed-term and self-employed. Self-report of workplace bullying was modelled using multiple logistic regression in relation to employment arrangement, controlling for sex, age, working hours, years in job, occupational skill level, marital status and a proxy for socioeconomic status. Workplace bullying was reported by 174 respondents (15.2%). Risk of workplace bullying was higher for being in a professional occupation, having a university education and being separated, divorced or widowed, but did not vary significantly by sex, age or job tenure. In adjusted multivariate logistic regression models, casual workers were significantly less likely than workers on permanent or fixed-term contracts to report bullying. Those separated, divorced or widowed had higher odds of reporting bullying than married, de facto or never-married workers. Contrary to expectation, workplace bullying was more often reported by permanent than casual employees. It may represent an exposure pathway not previously linked with the more idealised permanent employment arrangement. A finer understanding of psycho-social hazards across all employment arrangements is needed, with equal attention to the hazards associated with permanent as well as casual employment. © 2012 The Authors. ANZJPH © 2012 Public Health Association of Australia.

Why to Treat Subjects as Fixed Effects

ERIC Educational Resources Information Center

Adelman, James S.; Estes, Zachary

2015-01-01

Adelman, Marquis, Sabatos-DeVito, and Estes (2013) collected word naming latencies from 4 participants who read 2,820 words 50 times each. Their recommendation and practice was that R2 targets set for models should take into account subject idiosyncrasies as replicable patterns, equivalent to a subjects-as-fixed-effects assumption. In light of an…

Ownership conversions and nursing home performance.

PubMed

Grabowski, David C; Stevenson, David G

2008-08-01

To examine the effects of ownership conversions on nursing home performance. Online Survey, Certification, and Reporting system data from 1993 to 2004, and the Minimum Data Set (MDS) facility reports from 1998 to 2004. Regression specification incorporating facility fixed effects, with terms to identify trends in the pre- and postconversion periods. The annual rate of nursing home conversions almost tripled between 1994 and 2004. Our regression results indicate converting facilities are generally different throughout the pre/postconversion years, suggesting little causal effect of ownership conversions on nursing home performance. Before and after conversion, nursing homes converting from nonprofit to for-profit status generally exhibit deterioration in their performance, while nursing homes converting from for-profit to nonprofit status generally exhibit improvement. Policy makers have expressed concern regarding the implications of ownership conversions for nursing home performance. Our results imply that regulators and policy makers should not only monitor the outcomes of nursing home conversions, but also the targets of these conversions.

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

PubMed

McLaughlin, Michael

2017-05-01

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

Inconsistent Responding in a Criminal Forensic Setting: An Evaluation of the VRIN-r and TRIN-r Scales of the MMPI-2-RF.

PubMed

Gu, Wen; Reddy, Hima B; Green, Debbie; Belfi, Brian; Einzig, Shanah

2017-01-01

Criminal forensic evaluations are complicated by the risk that examinees will respond in an unreliable manner. Unreliable responding could occur due to lack of personal investment in the evaluation, severe mental illness, and low cognitive abilities. In this study, 31% of Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008/2011) profiles were invalid due to random or fixed-responding (T score ≥ 80 on the VRIN-r or TRIN-r scales) in a sample of pretrial criminal defendants evaluated in the context of treatment for competency restoration. Hierarchical regression models showed that symptom exaggeration variables, as measured by inconsistently reported psychiatric symptoms, contributed over and above education and intellectual functioning in their prediction of both random responding and fixed responding. Psychopathology variables, as measured by mood disturbance, better predicted fixed responding after controlling for estimates of cognitive abilities, but did not improve the prediction for random responding. These findings suggest that random responding and fixed responding are not only affected by education and intellectual functioning, but also by intentional exaggeration and aspects of psychopathology. Measures of intellectual functioning and effort and response style should be considered for administration in conjunction with self-report personality measures to rule out rival hypotheses of invalid profiles.

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.

A genome-wide association study suggests new candidate genes for milk production traits in Chinese Holstein cattle.

PubMed

Yue, S J; Zhao, Y Q; Gu, X R; Yin, B; Jiang, Y L; Wang, Z H; Shi, K R

2017-12-01

A genome-wide association study (GWAS) was conducted on 15 milk production traits in Chinese Holstein. The experimental population consisted of 445 cattle, each genotyped by the GGP (GeneSeek genomic profiling)-BovineLD V3 SNP chip, which had 26 151 public SNPs in its manifest file. After data cleaning, 20 326 SNPs were retained for the GWAS. The phenotypes were estimated breeding values of traits, provided by a public dairy herd improvement program center that had been collected once a month for 3 years. Two statistical models, a fixed-effect linear regression model and a mixed-effect linear model, were used to estimate the association effects of SNPs on each of the phenotypes. Genome-wide significant and suggestive thresholds were set at 2.46E-06 and 4.95E-05 respectively. The two statistical models concurrently identified two genome-wide significant (P < 0.05) SNPs on milk production traits in this Chinese Holstein population. The positional candidate genes, which were the ones closest to these two identified SNPs, were EEF2K (eukaryotic elongation factor 2 kinase) and KLHL1 (kelch like family member 1). These two genes could serve as new candidate genes for milk yield and lactation persistence, yet their roles need to be verified in further function studies. © 2017 Stichting International Foundation for Animal Genetics.

Does higher income inequality adversely influence infant mortality rates? Reconciling descriptive patterns and recent research findings.

PubMed

Siddiqi, Arjumand; Jones, Marcella K; Erwin, Paul Campbell

2015-04-01

As the struggle continues to explain the relatively high rates of infant mortality (IMR) exhibited in the United States, a renewed emphasis is being placed on the role of possible 'contextual' determinants. Cross-sectional and short time-series studies have found that higher income inequality is associated with higher IMR at the state level. Yet, descriptively, the longer-term trends in income inequality and in IMR seem to call such results into question. To assess whether, over the period 1990-2007, state-level income inequality is associated with state-level IMR; to examine whether the overall effect of income inequality on IMR over this period varies by state; to test whether the association between income inequality and IMR varies across this time period. IMR data--number of deaths per 1000 live births in a given state and year--were obtained from the U.S. Centers for Disease Control Wonder database. Income inequality was measured using the Gini coefficient, which varies from zero (complete equality) to 100 (complete inequality). Covariates included state-level poverty rate, median income, and proportion of high school graduates. Fixed and random effects regressions were conducted to test hypotheses. Fixed effects models suggested that, overall, during the period 1990-2007, income inequality was inversely associated with IMR (β = -0.07, SE (0.01)). Random effects models suggested that when the relationship was allowed to vary at the state-level, it remained inverse (β = -0.05, SE (0.01)). However, an interaction between income inequality and time suggested that, as time increased, the effect of income inequality had an increasingly positive association with total IMR (β = 0.009, SE (0.002)). The influence of state income inequality on IMR is dependent on time, which may proxy for time-dependent aspects of societal context. Copyright © 2015 Elsevier Ltd. All rights reserved.

Data-driven discovery of partial differential equations

PubMed Central

Rudy, Samuel H.; Brunton, Steven L.; Proctor, Joshua L.; Kutz, J. Nathan

2017-01-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg–de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable. PMID:28508044

The economic impact of Mexico City's smoke-free law

PubMed Central

Guerrero López, Carlos Manuel; Jiménez Ruiz, Jorge Alberto; Reynales Shigematsu, Luz Myriam

2011-01-01

Objective To evaluate the economic impact of Mexico City's 2008 smoke-free law—The Non-Smokers' Health Protection Law on restaurants, bars and nightclubs. Material and methods We used the Monthly Services Survey of businesses from January 2005 to April 2009—with revenues, employment and payments to employees as the principal outcomes. The results are estimated using a differences-in-differences regression model with fixed effects. The states of Jalisco, Nuevo León and México, where the law was not in effect, serve as a counterfactual comparison group. Results In restaurants, after accounting for observable factors and the fixed effects, there was a 24.8% increase in restaurants' revenue associated with the smoke-free law. This difference is not statistically significant but shows that, on average, restaurants did not suffer economically as a result of the law. Total wages increased by 28.2% and employment increased by 16.2%. In nightclubs, bars and taverns there was a decrease of 1.5% in revenues and an increase of 0.1% and 3.0%, respectively, in wages and employment. None of these effects are statistically significant in multivariate analysis. Conclusions There is no statistically significant evidence that the Mexico City smoke-free law had a negative impact on restaurants' income, employees' wages and levels of employment. On the contrary, the results show a positive, though statistically non-significant, impact of the law on most of these outcomes. Mexico City's experience suggests that smoke-free laws in Mexico and elsewhere will not hurt economic productivity in the restaurant and bar industries. PMID:21292808

The contribution of 3D quantitative meniscal and cartilage measures to variation in normal radiographic joint space width-Data from the Osteoarthritis Initiative healthy reference cohort.

PubMed

Roth, Melanie; Wirth, Wolfgang; Emmanuel, Katja; Culvenor, Adam G; Eckstein, Felix

2017-02-01

To explore to what extent three-dimensional measures of the meniscus and femorotibial cartilage explain the variation in medial and lateral femorotibial radiographic joint space width (JSW), in healthy men and women. The right knees of 87 Osteoarthritis Initiative healthy reference participants (no symptoms, radiographic signs or risk factors of osteoarthritis; 37 men, 50 women; age 55.0±7.6; BMI 24.4±3.1) were assessed. Quantitative measures of subregional femorotibial cartilage thickness and meniscal position and morphology were computed from segmented magnetic resonance images. Minimal and medial/lateral fixed-location JSW were determined from fixed-flexion radiographs. Correlation and regression analyses were used to explore the contribution of demographic, cartilage and meniscal parameters to JSW in healthy subjects. The correlation with (medial) minimal JSW was somewhat stronger for cartilage thickness (0.54≤r≤0.67) than for meniscal (-0.31≤r≤0.50) or demographic measures (-0.15≤r≤0.48), in particular in men. In women, in contrast, the strength of the correlations of cartilage thickness and meniscal measures with minimal JSW were in the same range. Fixed-location JSW measures showed stronger correlations with cartilage thickness (r≥0.68 medially; r≥0.59 laterally) than with meniscal measures (r≤|0.32| medially; r≤|0.32| laterally). Stepwise regression models revealed that meniscal measures added significant independent information to the total variance explained in minimal JSW (adjusted multiple r 2 =58%) but not in medial or lateral fixed-location JSW (r 2 =60/51%, respectively). In healthy subjects, minimal JSW was observed to reflect a combination of cartilage and meniscal measures, particularly in women. Fixed-location JSW, in contrast, was found to be dominated by variance in cartilage thickness in both men and women, with somewhat higher correlations between cartilage and JSW in the medial than lateral femorotibial compartment. The significant contribution of the meniscus' position on minimal JSW reinforces concerns over validity of JSW as an indirect measure of hyaline cartilage. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in leafy green vegetables consumed at salad bars, based on modeling supply chain logistics.

PubMed

Tromp, S O; Rijgersberg, H; Franz, E

2010-10-01

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

Comparing the Income Elasticity of Health Spending in Middle-Income and High-Income Countries: The Role of Financial Protection.

PubMed

Vargas Bustamante, Arturo; V Shimoga, Sandhya

2017-07-19

As middle-income countries become more affluent, economically sophisticated and productive, health expenditure patterns are likely to change. Other socio-demographic and political changes that accompany rapid economic growth are also likely to influence health spending and financial protection. This study investigates the relationship between growth on per-capita healthcare expenditure and gross domestic product (GDP) in a group of 27 large middle-income economies and compares findings with those of 24 high-income economies from the Organization for Economic Cooperation and Development (OECD) group. This comparison uses national accounts data from 1995-2014. We hypothesize that the aggregated income elasticity of health expenditure in middle-income countries would be less than one (meaning healthcare is a normal good). An initial exploratory analysis tests between fixed-effects and random-effects model specifications. A fixed-effects model with time-fixed effects is implemented to assess the relationship between the two measures. Unit root, Hausman and serial correlation tests are conducted to determine model fit. Additional explanatory variables are introduced in different model specifications to test the robustness of our regression results. We include the out-of-pocket (OOP) share of health spending in each model to study the potential role of financial protection in our sample of high- and middle-income countries. The first-difference of study variables is implemented to address non-stationarity and cointegration properties. The elasticity of per-capita health expenditure and GDP growth is positive and statistically significant among sampled middle-income countries (51 per unit-growth in GDP) and high-income countries (50 per unit-growth in GDP). In contrast with previous research that has found that income elasticity of health spending in middle-income countries is larger than in high-income countries, our findings show that elasticity estimates can change if different criteria are used to assemble a more homogenous group of middle-income countries. Financial protection differences between middle- and high-income countries, however, are not associated with their respective income elasticity of health spending. The study findings show that in spite of the rapid economic growth experienced by the sampled middleincome countries, the aggregated income elasticity of health expenditure in them is less than one, and equals that of high-income countries. © 2018 The Author(s); Published by Kerman University of Medical Sciences. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Power of the wingbeat: modelling the effects of flapping wings in vertebrate flight.

PubMed

Heerenbrink, M Klein; Johansson, L C; Hedenström, A

2015-05-08

Animal flight performance has been studied using models developed for man-made aircraft. For an aeroplane with fixed wings, the energetic cost as a function of flight speed can be expressed in terms of weight, wing span, wing area and body area, where more details are included in proportionality coefficients. Flying animals flap their wings to produce thrust. Adopting the fixed wing flight model implicitly incorporates the effects of wing flapping in the coefficients. However, in practice, these effects have been ignored. In this paper, the effects of reciprocating wing motion on the coefficients of the fixed wing aerodynamic power model for forward flight are explicitly formulated in terms of thrust requirement, wingbeat frequency and stroke-plane angle, for optimized wingbeat amplitudes. The expressions are obtained by simulating flights over a large parameter range using an optimal vortex wake method combined with a low-level blade element method. The results imply that previously assumed acceptable values for the induced power factor might be strongly underestimated. The results also show the dependence of profile power on wing kinematics. The expressions introduced in this paper can be used to significantly improve animal flight models.

Power of the wingbeat: modelling the effects of flapping wings in vertebrate flight

PubMed Central

Heerenbrink, M. Klein; Johansson, L. C.; Hedenström, A.

2015-01-01

Animal flight performance has been studied using models developed for man-made aircraft. For an aeroplane with fixed wings, the energetic cost as a function of flight speed can be expressed in terms of weight, wing span, wing area and body area, where more details are included in proportionality coefficients. Flying animals flap their wings to produce thrust. Adopting the fixed wing flight model implicitly incorporates the effects of wing flapping in the coefficients. However, in practice, these effects have been ignored. In this paper, the effects of reciprocating wing motion on the coefficients of the fixed wing aerodynamic power model for forward flight are explicitly formulated in terms of thrust requirement, wingbeat frequency and stroke-plane angle, for optimized wingbeat amplitudes. The expressions are obtained by simulating flights over a large parameter range using an optimal vortex wake method combined with a low-level blade element method. The results imply that previously assumed acceptable values for the induced power factor might be strongly underestimated. The results also show the dependence of profile power on wing kinematics. The expressions introduced in this paper can be used to significantly improve animal flight models. PMID:27547098

Modeling and mapping abundance of American Woodcock across the Midwestern and Northeastern United States

USGS Publications Warehouse

Thogmartin, W.E.; Sauer, J.R.; Knutson, M.G.

2007-01-01

We used an over-dispersed Poisson regression with fixed and random effects, fitted by Markov chain Monte Carlo methods, to model population spatial patterns of relative abundance of American woodcock (Scolopax minor) across its breeding range in the United States. We predicted North American woodcock Singing Ground Survey counts with a log-linear function of explanatory variables describing habitat, year effects, and observer effects. The model also included a conditional autoregressive term representing potential correlation between adjacent route counts. Categories of explanatory habitat variables in the model included land-cover composition, climate, terrain heterogeneity, and human influence. Woodcock counts were higher in landscapes with more forest, especially aspen (Populus tremuloides) and birch (Betula spp.) forest, and in locations with a high degree of interspersion among forest, shrubs, and grasslands. Woodcock counts were lower in landscapes with a high degree of human development. The most noteworthy practical application of this spatial modeling approach was the ability to map predicted relative abundance. Based on a map of predicted relative abundance derived from the posterior parameter estimates, we identified major concentrations of woodcock abundance in east-central Minnesota, USA, the intersection of Vermont, USA, New York, USA, and Ontario, Canada, the upper peninsula of Michigan, USA, and St. Lawrence County, New York. The functional relations we elucidated for the American woodcock provide a basis for the development of management programs and the model and map may serve to focus management and monitoring on areas and habitat features important to American woodcock.

Predicting Organizational Performance: Application of Neurocomputing as an Alternative to Statistical Regression

DTIC Science & Technology

1989-09-01

separate network architetures would otherwise have to be performed for each 𔃼 5 of the nearly 70 cross-validation regressions. Fixing the composition...presentation. The generalized delta rule says the weight of each connection should be changed by an amount proportional to the product of the processing

Effect of the quartic gradient terms on the critical exponents of the Wilson-Fisher fixed point in O(N) models

NASA Astrophysics Data System (ADS)

Péli, Zoltán; Nagy, Sándor; Sailer, Kornel

2018-02-01

The effect of the O(partial4) terms of the gradient expansion on the anomalous dimension η and the correlation length's critical exponent ν of the Wilson-Fisher fixed point has been determined for the Euclidean 3-dimensional O( N) models with N≥ 2 . Wetterich's effective average action renormalization group method is used with field-independent derivative couplings and Litim's optimized regulator. It is shown that the critical theory is well approximated by the effective average action preserving O( N) symmetry with an accuracy of O(η).

The association between job stress and leisure-time physical inactivity adjusted for individual attributes: evidence from a Japanese occupational cohort survey.

PubMed

Oshio, Takashi; Tsutsumi, Akizumi; Inoue, Akiomi

2016-05-01

We examined the association between job stress and leisure-time physical inactivity, adjusting for individual time-invariant attributes. We used data from a Japanese occupational cohort survey, which included 31 025 observations of 9871 individuals. Focusing on the evolution of job stress and leisure-time physical inactivity within the same individual over time, we employed fixed-effects logistic models to examine the association between job stress and leisure-time physical inactivity. We compared the results with those in pooled cross-sectional models and fixed-effects ordered logistic models. Fixed-effects models showed that the odds ratio (OR) of physical inactivity were 22% higher for those with high strain jobs [high demands/low control; OR 1.22, 95% confidence interval (95% CI) 1.03-1.43] and 17% higher for those with active jobs (high demands/high control; OR 1.17, 95% CI 1.02-1.34) than those with low strain jobs (low demands/high control). The models also showed that the odds of physical inactivity were 28% higher for those with high effort/low reward jobs (OR 1.28, 95% CI 1.10-1.50) and 24% higher for those with high effort/high reward jobs (OR 1.24, 95% CI 1.07-1.43) than those with low effort/high reward jobs. Fixed-effects ordered logistic models led to similar results. Job stress, especially high job strain and effort-reward imbalance, was modestly associated with higher risks of physical inactivity, even after controlling for individual time-invariant attributes.

Long non-coding RNA CCAT1 as a diagnostic and prognostic molecular marker in various cancers: a meta-analysis.

PubMed

Zhang, Zhihui; Xie, Haibiao; Liang, Daqiang; Huang, Lanbing; Liang, Feiguo; Qi, Qiang; Yang, Xinjian

2018-05-04

Long non-coding RNA colon cancer-associated transcript-1 (CCAT1) is newly found to be related with diagnoses and prognosis of cancer. This meta-analysis was performed to investigate the relationship between CCAT1 expression and clinical parameters, including survival condition, lymph node metastasis and tumor node metastasis grade. The primary literatures were collected through initial search criteria from electronic databases, including PubMed, OVID Evidence-based medicine Reviews and others (up to May 12, 2017). Eligible studies were identified and selected by the inclusion and exclusion criteria. Data was extracted and computed into Hazard ratio (HR) for the assessment of overall survival, subgroup analyses were prespecified based on the digestive tract cancer or others. Analysis of different CCAT1 expression related with lymph node metastasis or tumor node metastasis grade was conducted. Risk of bias was assessed by the Newcastle-Ottawa Scale. 9 studies were included. This meta-analysis showed that high CCAT1 expression level was related to poor overall survival, the pooled HR was 2.42 (95% confidence interval, CI: 1.86-3.16; P < 0.001; fix- effects model), similarly in the cancer type subgroups: digestive tract cancer (HR, 2.42; 95% CI, 1.79-3.29; P < 0.001; fix- effects model) and others (HR, 2.42; 95% CI, 1.42-4.13; P = 0.001; fix- effects model). The analysis showed that high CCAT1 was strongly related to positive lymph node metastasis (Odds ratio, OR: 3.24; 95% CI, 2.04-5.16; P < 0.001; fix- effects model), high tumor node metastasis stage (OR, 3.87; 95% CI, 2.53-5.92; P < 0.001; fix- effects model). In conclusion, this meta-analysis revealed that CCAT1 had potential as a diagnostic and prognostic biomarker in various cancers.

The spatial impact of neighbouring on the exports activities of COMESA countries by using spatial panel models

NASA Astrophysics Data System (ADS)

Hamzalouh, L.; Ismail, M. T.; Rahman, R. A.

2017-09-01

In this paper, spatial panel models were used and the method for selecting the best model amongst the spatial fixed effects model and the spatial random effects model to estimate the fitting model by using the robust Hausman test for analysis of the exports pattern of the Common Market for Eastern and Southern African (COMESA) countries. And examine the effects of the interactions of the economic statistic of explanatory variables on the exports of the COMESA. Results indicated that the spatial Durbin model with fixed effects specification should be tested and considered in most cases of this study. After that, the direct and indirect effects among COMESA regions were assessed, and the role of indirect spatial effects in estimating exports was empirically demonstrated. Regarding originality and research value, and to the best of the authors’ knowledge, this is the first attempt to examine exports between COMESA and its member countries through spatial panel models using XSMLE, which is a new command for spatial analysis using STATA.

Beyond Absenteeism: Father Incarceration and Child Development*

PubMed Central

Geller, Amanda; Cooper, Carey E.; Garfinkel, Irwin; Schwartz-Soicher, Ofira; Mincy, Ronald B.

2013-01-01

High rates of incarceration among American men, coupled with high rates of fatherhood among men in prison, have motivated recent research on the effects of parental imprisonment on children’s development. We use data from the Fragile Families and Child Wellbeing Study to examine the relationship between paternal incarceration and developmental outcomes for approximately 3,000 urban children. We estimate cross-sectional and longitudinal regression models that control not only for fathers’ basic demographic characteristics and a rich set of potential confounders, but also for several measures of pre-incarceration child development and family fixed effects. We find significant increases in aggressive behaviors among children whose fathers are incarcerated, and some evidence of increased attention problems. The estimated effects of paternal incarceration are stronger than those of other forms of father absence, suggesting that children with incarcerated fathers may require specialized support from caretakers, teachers, and social service providers. The estimated effects are stronger for children who lived with their fathers prior to incarceration, but are also significant for children of nonresident fathers, suggesting that incarceration places children at risk through family hardships including and beyond parent-child separation. PMID:22203452

When Do Laws Matter? National Minimum-Age-of-Marriage Laws, Child Rights, and Adolescent Fertility, 1989–2007

PubMed Central

Kim, Minzee; Longhofer, Wesley; Boyle, Elizabeth Heger; Nyseth, Hollie

2014-01-01

Using the case of adolescent fertility, we ask the questions of whether and when national laws have an effect on outcomes above and beyond the effects of international law and global organizing. To answer these questions, we utilize a fixed-effect time-series regression model to analyze the impact of minimum-age-of-marriage laws in 115 poor- and middle-income countries from 1989 to 2007. We find that countries with strict laws setting the minimum age of marriage at 18 experienced the most dramatic decline in rates of adolescent fertility. Trends in countries that set this age at 18 but allowed exceptions (for example, marriage with parental consent) were indistinguishable from countries that had no such minimum-age-of-marriage law. Thus, policies that adhere strictly to global norms are more likely to elicit desired outcomes. The article concludes with a discussion of what national law means in a diffuse global system where multiple actors and institutions make the independent effect of law difficult to identify. PMID:25525281

The impact of unemployment cycles on child and maternal health in Argentina.

PubMed

Wehby, George L; Gimenez, Lucas G; López-Camelo, Jorge S

2017-03-01

The purpose of this study is to examine the effects of economic cycles in Argentina on infant and maternal health between 1994 and 2006, a period that spans the major economic crisis in 1999-2002. We evaluate the effects of province-level unemployment rates on several infant health outcomes, including birth weight, gestational age, fetal growth rate, and hospital discharge status after birth in a sample of 15,000 infants born in 13 provinces. Maternal health and healthcare outcomes include acute and chronic illnesses, infectious diseases, and use of prenatal visits and technology. Regression models control for hospital and year fixed effects and province-specific time trends. Unemployment rise reduces fetal growth rate particularly among high educated parents. Also, maternal poverty-related infectious diseases increase, although reporting of acute illnesses declines (an effect more pronounced among low educated parents). There is also some evidence for reduced access to prenatal care and technology among less educated parents with higher unemployment. Unemployment rise in Argentina has adversely affected certain infant and maternal health outcomes, but several measures show no evidence of significant change.

Spontaneous Velocity Effect of Musical Expression on Self-Paced Walking.

PubMed

Buhmann, Jeska; Desmet, Frank; Moens, Bart; Van Dyck, Edith; Leman, Marc

2016-01-01

The expressive features of music can influence the velocity of walking. So far, studies used instructed (and intended) synchronization. But is this velocity effect still present with non-instructed (spontaneous) synchronization? To figure that out, participants were instructed to walk in their own comfort tempo on an indoor track, first in silence and then with tempo-matched music. We compared velocities of silence and music conditions. The results show that some music has an activating influence, increasing velocity and motivation, while other music has a relaxing influence, decreasing velocity and motivation. The influence of musical expression on the velocity of self-paced walking can be predicted with a regression model using only three sonic features explaining 56% of the variance. Phase-coherence between footfall and beat did not contribute to the velocity effect, due to its implied fixed pacing. The findings suggest that the velocity effect depends on vigor entrainment that influences both stride length and pacing. Our findings are relevant for preventing injuries, for gait improvement in walking rehabilitation, and for improving performance in sports activities.

Spontaneous Velocity Effect of Musical Expression on Self-Paced Walking

PubMed Central

Buhmann, Jeska; Desmet, Frank; Moens, Bart; Van Dyck, Edith; Leman, Marc

2016-01-01

The expressive features of music can influence the velocity of walking. So far, studies used instructed (and intended) synchronization. But is this velocity effect still present with non-instructed (spontaneous) synchronization? To figure that out, participants were instructed to walk in their own comfort tempo on an indoor track, first in silence and then with tempo-matched music. We compared velocities of silence and music conditions. The results show that some music has an activating influence, increasing velocity and motivation, while other music has a relaxing influence, decreasing velocity and motivation. The influence of musical expression on the velocity of self-paced walking can be predicted with a regression model using only three sonic features explaining 56% of the variance. Phase-coherence between footfall and beat did not contribute to the velocity effect, due to its implied fixed pacing. The findings suggest that the velocity effect depends on vigor entrainment that influences both stride length and pacing. Our findings are relevant for preventing injuries, for gait improvement in walking rehabilitation, and for improving performance in sports activities. PMID:27167064

Legume Shrubs Are More Nitrogen-Homeostatic than Non-legume Shrubs

PubMed Central

Guo, Yanpei; Yang, Xian; Schöb, Christian; Jiang, Youxu; Tang, Zhiyao

2017-01-01

Legumes are characterized as keeping stable nutrient supply under nutrient-limited conditions. However, few studies examined the legumes' stoichiometric advantages over other plants across various taxa in natural ecosystems. We explored differences in nitrogen (N) and phosphorus (P) stoichiometry of different tissue types (leaf, stem, and root) between N2-fixing legume shrubs and non-N2-fixing shrubs from 299 broadleaved deciduous shrubland sites in northern China. After excluding effects of taxonomy and environmental variables, these two functional groups differed considerably in nutrient regulation. N concentrations and N:P ratios were higher in legume shrubs than in non-N2-fixing shrubs. N concentrations were positively correlated between the plants and soil for non-N2-fixing shrubs, but not for legume shrubs, indicating a stronger stoichiometric homeostasis in legume shrubs than in non-N2-fixing shrubs. N concentrations were positively correlated among three tissue types for non-N2-fixing shrubs, but not between leaves and non-leaf tissues for legume shrubs, demonstrating that N concentrations were more dependent among tissues for non-N2-fixing shrubs than for legume shrubs. N and P concentrations were correlated within all tissues for both functional groups, but the regression slopes were flatter for legume shrubs than non-N2-fixing shrubs, implying that legume shrubs were more P limited than non-N2-fixing shrubs. These results address significant differences in stoichiometry between legume shrubs and non-N2-fixing shrubs, and indicate the influence of symbiotic nitrogen fixation (SNF) on plant stoichiometry. Overall, N2-fixing legume shrubs are higher and more stoichiometrically homeostatic in N concentrations. However, due to excess uptake of N, legumes may suffer from potential P limitation. With their N advantage, legume shrubs could be good nurse plants in restoration sites with degraded soil, but their P supply should be taken care of during management according to our results. PMID:29018468

Legume Shrubs Are More Nitrogen-Homeostatic than Non-legume Shrubs.

PubMed

Guo, Yanpei; Yang, Xian; Schöb, Christian; Jiang, Youxu; Tang, Zhiyao

2017-01-01

Legumes are characterized as keeping stable nutrient supply under nutrient-limited conditions. However, few studies examined the legumes' stoichiometric advantages over other plants across various taxa in natural ecosystems. We explored differences in nitrogen (N) and phosphorus (P) stoichiometry of different tissue types (leaf, stem, and root) between N 2 -fixing legume shrubs and non-N 2 -fixing shrubs from 299 broadleaved deciduous shrubland sites in northern China. After excluding effects of taxonomy and environmental variables, these two functional groups differed considerably in nutrient regulation. N concentrations and N:P ratios were higher in legume shrubs than in non-N 2 -fixing shrubs. N concentrations were positively correlated between the plants and soil for non-N 2 -fixing shrubs, but not for legume shrubs, indicating a stronger stoichiometric homeostasis in legume shrubs than in non-N 2 -fixing shrubs. N concentrations were positively correlated among three tissue types for non-N 2 -fixing shrubs, but not between leaves and non-leaf tissues for legume shrubs, demonstrating that N concentrations were more dependent among tissues for non-N 2 -fixing shrubs than for legume shrubs. N and P concentrations were correlated within all tissues for both functional groups, but the regression slopes were flatter for legume shrubs than non-N 2 -fixing shrubs, implying that legume shrubs were more P limited than non-N 2 -fixing shrubs. These results address significant differences in stoichiometry between legume shrubs and non-N 2 -fixing shrubs, and indicate the influence of symbiotic nitrogen fixation (SNF) on plant stoichiometry. Overall, N 2 -fixing legume shrubs are higher and more stoichiometrically homeostatic in N concentrations. However, due to excess uptake of N, legumes may suffer from potential P limitation. With their N advantage, legume shrubs could be good nurse plants in restoration sites with degraded soil, but their P supply should be taken care of during management according to our results.

Exploring the link between environmental pollution and economic growth in EU-28 countries: Is there an environmental Kuznets curve?

PubMed Central

Armeanu, Daniel; Vintilă, Georgeta; Gherghina, Ştefan Cristian; Drăgoi, Mihaela Cristina; Teodor, Cristian

2018-01-01

This study examines the Environmental Kuznets Curve hypothesis (EKC), considering the primary energy consumption among other country-specific variables, for a panel of the EU-28 countries during the period 1990–2014. By estimating pooled OLS regressions with Driscoll-Kraay standard errors in order to account for cross-sectional dependence, the results confirm the EKC hypothesis in the case of emissions of sulfur oxides and emissions of non-methane volatile organic compounds. In addition to pooled estimations, the output of fixed-effects regressions with Driscoll-Kraay standard errors support the EKC hypothesis for greenhouse gas emissions, greenhouse gas emissions intensity of energy consumption, emissions of nitrogen oxides, emissions of non-methane volatile organic compounds and emissions of ammonia. Additionally, the empirical findings from panel vector error correction model reveal a short-run unidirectional causality from GDP per capita growth to greenhouse gas emissions, as well as a bidirectional causal link between primary energy consumption and greenhouse gas emissions. Furthermore, since there occurred no causal link between economic growth and primary energy consumption, the neo-classical view was confirmed, namely the neutrality hypothesis. PMID:29742169

Exploring the link between environmental pollution and economic growth in EU-28 countries: Is there an environmental Kuznets curve?

PubMed

Armeanu, Daniel; Vintilă, Georgeta; Andrei, Jean Vasile; Gherghina, Ştefan Cristian; Drăgoi, Mihaela Cristina; Teodor, Cristian

2018-01-01

This study examines the Environmental Kuznets Curve hypothesis (EKC), considering the primary energy consumption among other country-specific variables, for a panel of the EU-28 countries during the period 1990-2014. By estimating pooled OLS regressions with Driscoll-Kraay standard errors in order to account for cross-sectional dependence, the results confirm the EKC hypothesis in the case of emissions of sulfur oxides and emissions of non-methane volatile organic compounds. In addition to pooled estimations, the output of fixed-effects regressions with Driscoll-Kraay standard errors support the EKC hypothesis for greenhouse gas emissions, greenhouse gas emissions intensity of energy consumption, emissions of nitrogen oxides, emissions of non-methane volatile organic compounds and emissions of ammonia. Additionally, the empirical findings from panel vector error correction model reveal a short-run unidirectional causality from GDP per capita growth to greenhouse gas emissions, as well as a bidirectional causal link between primary energy consumption and greenhouse gas emissions. Furthermore, since there occurred no causal link between economic growth and primary energy consumption, the neo-classical view was confirmed, namely the neutrality hypothesis.

Do Australian Football players have sensitive groins? Players with current groin pain exhibit mechanical hyperalgesia of the adductor tendon.

PubMed

Drew, Michael K; Lovell, Gregory; Palsson, Thorvaldur S; Chiarelli, Pauline E; Osmotherly, Peter G

2016-10-01

This is the first study to evaluate the mechanical sensitivity, clinical classifications and prevalence of groin pain in Australian football players. Case-control. Professional (n=66) and semi-professional (n=9) Australian football players with and without current or previous groin injuries were recruited. Diagnoses were mapped to the Doha Agreement taxonomy. Point and career prevalence of groin pain was calculated. Pressure pain thresholds (PPTs) were assessed at regional and distant sites using handheld pressure algometry across four sites bilaterally (adductor longus tendon, pubic bone, rectus femoris, tibialis anterior muscle). To assess the relationship between current groin pain and fixed effects of hyperalgesia of each site and a history of groin pain, a mixed-effect logistic regression model was utilised. Receiver Operator Characteristic (ROC) curve were determined for the model. Point prevalence of groin pain in the preseason was 21.9% with a career prevalence of 44.8%. Adductor-related groin pain was the most prevalent classification in the pre-season period. Hyperalgesia was observed in the adductor longus tendon site in athletes with current groin pain (OR=16.27, 95% CI 1.86 to 142.02). The ROC area under the curve of the regression model was fair (AUC=0.76, 95% CI 0.54 to 0.83). Prevalence data indicates that groin pain is a larger issue than published incidence rates imply. Adductor-related groin pain is the most common diagnosis in pre-season in this population. This study has shown that hyperalgesia exists in Australian football players experiencing groin pain indicating the value of assessing mechanical pain sensitivity as a component of the clinical assessment. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Effects of calcium feeding strategy on true ileal phosphorus digestibility and true phosphorus retention determined with growing broilers.

PubMed

Perryman, K R; Masey O'Neill, H V; Bedford, M R; Dozier, W A

2016-05-01

An experiment utilizing 960 Ross × Ross 708 male broilers was conducted to determine the effects of Ca feeding strategy on true ileal (prececal) P digestibility (TIPD) and true P retention (TPR) of corn. Experimental diets were formulated with 1 of 3 dietary Ca feeding strategies (0.95%, 0.13%, or variable Ca concentrations to maintain a 2.1:1 Ca:P ratio) and contain 0, 25, 50, or 75% corn. A practical corn-soybean meal diet (1.4:1 Ca:P ratio) was fed as a control. After receiving a common starter diet, experimental diets were fed from 19 to 26 d of age. After a 48-h dietary adaptation period, a 48-h retention assay was conducted. At 25 and 26 d of age, ileal digesta were collected from 8 birds per cage. Broilers consuming the control diet had higher (P<0.001) BW gain, feed intake, digesta P, and excreta P than broilers consuming the corn titration diets. Digesta and excreta P increased (linear, P<0.05) with graded increases of corn. True ileal P digestibility and TPR were highest (P<0.05) for diets with 0.13% Ca (57.3 and 69.5%, respectively) compared with diets formulated with a 2.1:1 Ca:P ratio (41.2 and 37.8%, respectively) or 0.95% Ca (25.4 and 39.0%, respectively). Values for TPR were higher (P<0.05) than those for TIPD except when the dietary Ca:P ratio was fixed. Additionally, negative endogenous P losses were predicted by regression equations when TPR was estimated for birds fed titration diets with the fixed Ca:P ratio. Changing the Ca concentration of the diets to maintain a fixed Ca:P ratio influenced (P<0.001) apparent P retention, which affected the estimate for TPR due to the prediction of negative endogenous P losses. These data demonstrated that regression analysis may have limitations when estimating the TIPD or TPR of corn when formulating diets with different Ca feeding strategies. More research is necessary to elucidate the factors that contributed to regression equations predicting negative endogenous P losses. © 2016 Poultry Science Association Inc.

Variations in non-prescription drug consumption and expenditure: Determinants and policy implications.

PubMed

Otto, Monica; Armeni, Patrizio; Jommi, Claudio

2018-01-31

This paper analyses the determinants of cross-regional variations in expenditure and consumption for non-prescription drugs using the Italian Health Care Service as a case study. This research question has never been posed in other literature contributions. Per capita income, the incidence of elderly people, the presence of distribution points alternative to community pharmacies (para-pharmacies and drug corners in supermarkets), and the disease prevalence were included as possible explanatory variables. A trade-off between consumption of non-prescription and prescription-only drugs was also investigated. Correlation was tested through linear regression models with regional fixed-effects. Demand-driven variables, including the prevalence of the target diseases and income, were found to be more influential than supply-side variables, such as the presence of alternative distribution points. Hence, the consumption of non-prescription drugs appears to respond to needs and is not induced by the supply. The expected trade-off between consumption for prescription-only and non-prescription drugs was not empirically found: increasing the use of non-prescription drugs did not automatically imply savings on prescription-only drugs covered by third payers. Despite some caveats (the short period of time covered by the longitudinal data and some missing monthly data), the regression model revealed a high explanatory power of the variability and a strong predictive ability of future values. Copyright © 2018 Elsevier B.V. All rights reserved.

A Comparison of Mobile and Fixed Device Access on User Engagement Associated With Women, Infants, and Children (WIC) Online Nutrition Education

PubMed Central

2016-01-01

Background Online health education has expanded its reach due to cost-effective implementation and demonstrated effectiveness. However, a limitation exists with the evaluation of online health education implementations and how the impact of the system is attenuated by the extent to which a user engages with it. Moreover, the current online health education research does not consider how this engagement has been affected by the transition from fixed to mobile user access over the last decade. Objective This paper focuses on comparing the impact mobile versus fixed devices have on user engagement key performance indicators (KPI) associated with the wichealth website (.org), an Internet-based parent-child feeding intervention offered to clients associated with the US Department of Agriculture’s Special Supplemental Nutrition Program for Women, Infants, and Children (WIC). Methods Data were collected from 612,201 nutrition education lessons completed by 305,735 unique WIC participants in 21 states over a 1-year period. Data consisted of system-collected measures, profile items, and items from an exit survey administered at the conclusion of each lesson. User engagement was defined based on 3 KPIs associated with usage of the wichealth website: number of link views, link view time, and progression in stage of readiness to change. Independent samples t tests were used to compare KPIs between fixed only and mobile only device users and paired samples t tests were used to compare KPIs within users who completed at least one lesson each on both a fixed and mobile device. A logistic regression was performed to estimate the odds of KPI performance thresholds in the independent samples study group given access device type while controlling for confounding of user characteristics associated with these KPIs. Results Analysis of 8 user characteristics (lessons completed, race, ethnicity, language, state of residence, pregnancy status, beginning stage of change, and preferred nutrition education method) were significantly (P<.001) related to various KPI differences between mobile and fixed device access. Non-mobile users were significantly (P<.001) more likely to engage based on all 3 KPIs, even after logistic regression control for the potential confounding related to the strongly associated user characteristics identified. Conclusions The findings of this study support the idea that online health education developers need to seriously consider access device when creating programs. Online health education developers need to take extra effort to truly understand access patterns of populations being served, and whether or not access device will influence user engagement performance indicators. PMID:27847351

Displacement efficiency of alternative energy and trans-provincial imported electricity in China.

PubMed

Hu, Yuanan; Cheng, Hefa

2017-02-17

China has invested heavily on alternative energy, but the effectiveness of such energy sources at substituting the dominant coal-fired generation remains unknown. Here we analyse the displacement of fossil-fuel-generated electricity by alternative energy, primarily hydropower, and by trans-provincial imported electricity in China between 1995 and 2014 using two-way fixed-effects panel regression models. Nationwide, each unit of alternative energy displaces nearly one-quarter of a unit of fossil-fuel-generated electricity, while each unit of imported electricity (regardless of the generation source) displaces ∼0.3 unit of fossil-fuel electricity generated locally. Results from the six regional grids indicate that significant displacement of fossil-fuel-generated electricity occurs once the share of alternative energy in the electricity supply mix exceeds ∼10%, which is accompanied by 10-50% rebound in the consumption of fossil-fuel-generated electricity. These findings indicate the need for a policy that integrates carbon taxation, alternative energy and energy efficiency to facilitate China's transition towards a low-carbon economy.

Rich and Well Educated: Are These Requirements Necessary to Claim Healthcare Tax Credits in Italy?

PubMed

Brenna, Elenka

2018-04-01

The paper investigates the use of healthcare tax credits (HTCs) in Italy through the analysis of a panel data, which provides information on individual income tax from 2008 to 2014. There is evidence of disparities in the per-capita HTCs between Northern and Southern regions, which need to be analyzed and addressed. The aim of the paper is to investigate the socioeconomic determinants in the use of Healthcare Tax Credits in Italy. A fixed effects Ordinary Least Square model is run to analyze the impact of selected socioeconomic variables on regional per capita HTCs, with a particular focus on the role of education. The results corroborate literature findings on the regressive effects of HTCs; they also provide highlights on the role of education in explaining the distribution of HTCs among Italian regions. Public money is reimbursed to regions where people are, on average, richer and better educated. More equitable objectives could be reached by allocating the same resources in the provision of services covered by the NHS.

Displacement efficiency of alternative energy and trans-provincial imported electricity in China

NASA Astrophysics Data System (ADS)

Hu, Yuanan; Cheng, Hefa

2017-02-01

China has invested heavily on alternative energy, but the effectiveness of such energy sources at substituting the dominant coal-fired generation remains unknown. Here we analyse the displacement of fossil-fuel-generated electricity by alternative energy, primarily hydropower, and by trans-provincial imported electricity in China between 1995 and 2014 using two-way fixed-effects panel regression models. Nationwide, each unit of alternative energy displaces nearly one-quarter of a unit of fossil-fuel-generated electricity, while each unit of imported electricity (regardless of the generation source) displaces ~0.3 unit of fossil-fuel electricity generated locally. Results from the six regional grids indicate that significant displacement of fossil-fuel-generated electricity occurs once the share of alternative energy in the electricity supply mix exceeds ~10%, which is accompanied by 10-50% rebound in the consumption of fossil-fuel-generated electricity. These findings indicate the need for a policy that integrates carbon taxation, alternative energy and energy efficiency to facilitate China's transition towards a low-carbon economy.

A Demographic Deficit? Local Population Aging and Access to Services in Rural America, 1990–2010

PubMed Central

Thiede, Brian; Brown, David L.; Sanders, Scott R.; Glasgow, Nina; Kulcsar, Laszlo J.

2017-01-01

Population aging is being experienced by many rural communities in the U.S., as evidenced by increases in the median age and the high incidence of natural population decrease. The implications of these changes in population structure for the daily lives of the residents in such communities have received little attention. We address this issue in the current study by examining the relationship between population aging and the availability of service-providing establishments in the rural U.S. between 1990 and 2010. Using data mainly from the U.S. Census Bureau and the Bureau of Labor Statistics, we estimate a series of fixed-effects regression models to identify the relationship between median age and establishment counts net of changes in overall population and other factors. We find a significant, but non-linear relationship between county median age and the total number of service-providing establishments, and counts of most specific types of services. We find a positive effect of total population size across all of our models. This total population effect is consistent with other research, but the independent effects of age structure that we observe represent a novel finding and suggest that age structure is a salient factor in local rural development and community wellbeing. PMID:28757660

Random-Effects Models for Meta-Analytic Structural Equation Modeling: Review, Issues, and Illustrations

ERIC Educational Resources Information Center

Cheung, Mike W.-L.; Cheung, Shu Fai

2016-01-01

Meta-analytic structural equation modeling (MASEM) combines the techniques of meta-analysis and structural equation modeling for the purpose of synthesizing correlation or covariance matrices and fitting structural equation models on the pooled correlation or covariance matrix. Both fixed-effects and random-effects models can be defined in MASEM.…

Examination of universal purchase programs as a driver of vaccine uptake among US States, 1995-2014.

PubMed

Mulligan, Karen; Snider, Julia Thornton; Arthur, Phyllis; Frank, Gregory; Tebeka, Mahlet; Walker, Amy; Abrevaya, Jason

2018-06-01

Immunization against numerous potentially life-threatening illnesses has been a great public health achievement. In the United States, the Vaccines for Children (VFC) program has provided vaccines to uninsured and underinsured children since the early 1990s, increasing vaccination rates. In recent years, some states have adopted Universal Purchase (UP) programs with the stated aim of further increasing vaccination rates. Under UP programs, states also purchase vaccines for privately-insured children at federally-contracted VFC prices and bill private health insurers for the vaccines through assessments. In this study, we estimated the effect of UP adoption in a state on children's vaccination rates using state-level and individual-level data from the 1995-2014 National Immunization Survey. For the state-level analysis, we performed ordinary least squares regression to estimate the state's vaccination rate as a function of whether the state had UP in the given year, state demographic characteristics, other vaccination policies, state fixed effects, and a time trend. For the individual analysis, we performed logistic regression to estimate a child's likelihood of being vaccinated as a function of whether the state had UP in the given year, the child's demographic characteristics, state characteristics and vaccine policies, state fixed effects, and a time trend. We performed separate regressions for each of nine recommended vaccines, as well as composite measures on whether a child was up-to-date on all required vaccines. In the both the state-level and individual-level analyses, we found UP had no significant (p < 0.10) effect on any of the vaccines or composite measures in our base case specifications. Results were similar in alternative specifications. We hypothesize that UP was ineffective in increasing vaccination rates. Policymakers seeking to increase vaccination rates would do well to consider other policies such as addressing provider practice issues and vaccine hesitancy. Copyright © 2018. Published by Elsevier Ltd.

Functional linear models for association analysis of quantitative traits.

PubMed

Fan, Ruzong; Wang, Yifan; Mills, James L; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao

2013-11-01

Functional linear models are developed in this paper for testing associations between quantitative traits and genetic variants, which can be rare variants or common variants or the combination of the two. By treating multiple genetic variants of an individual in a human population as a realization of a stochastic process, the genome of an individual in a chromosome region is a continuum of sequence data rather than discrete observations. The genome of an individual is viewed as a stochastic function that contains both linkage and linkage disequilibrium (LD) information of the genetic markers. By using techniques of functional data analysis, both fixed and mixed effect functional linear models are built to test the association between quantitative traits and genetic variants adjusting for covariates. After extensive simulation analysis, it is shown that the F-distributed tests of the proposed fixed effect functional linear models have higher power than that of sequence kernel association test (SKAT) and its optimal unified test (SKAT-O) for three scenarios in most cases: (1) the causal variants are all rare, (2) the causal variants are both rare and common, and (3) the causal variants are common. The superior performance of the fixed effect functional linear models is most likely due to its optimal utilization of both genetic linkage and LD information of multiple genetic variants in a genome and similarity among different individuals, while SKAT and SKAT-O only model the similarities and pairwise LD but do not model linkage and higher order LD information sufficiently. In addition, the proposed fixed effect models generate accurate type I error rates in simulation studies. We also show that the functional kernel score tests of the proposed mixed effect functional linear models are preferable in candidate gene analysis and small sample problems. The methods are applied to analyze three biochemical traits in data from the Trinity Students Study. © 2013 WILEY PERIODICALS, INC.

Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

NASA Astrophysics Data System (ADS)

Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

2015-12-01

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Methods for calculating confidence and credible intervals for the residual between-study variance in random effects meta-regression models

PubMed Central

2014-01-01

Background Meta-regression is becoming increasingly used to model study level covariate effects. However this type of statistical analysis presents many difficulties and challenges. Here two methods for calculating confidence intervals for the magnitude of the residual between-study variance in random effects meta-regression models are developed. A further suggestion for calculating credible intervals using informative prior distributions for the residual between-study variance is presented. Methods Two recently proposed and, under the assumptions of the random effects model, exact methods for constructing confidence intervals for the between-study variance in random effects meta-analyses are extended to the meta-regression setting. The use of Generalised Cochran heterogeneity statistics is extended to the meta-regression setting and a Newton-Raphson procedure is developed to implement the Q profile method for meta-analysis and meta-regression. WinBUGS is used to implement informative priors for the residual between-study variance in the context of Bayesian meta-regressions. Results Results are obtained for two contrasting examples, where the first example involves a binary covariate and the second involves a continuous covariate. Intervals for the residual between-study variance are wide for both examples. Conclusions Statistical methods, and R computer software, are available to compute exact confidence intervals for the residual between-study variance under the random effects model for meta-regression. These frequentist methods are almost as easily implemented as their established counterparts for meta-analysis. Bayesian meta-regressions are also easily performed by analysts who are comfortable using WinBUGS. Estimates of the residual between-study variance in random effects meta-regressions should be routinely reported and accompanied by some measure of their uncertainty. Confidence and/or credible intervals are well-suited to this purpose. PMID:25196829

Local regression type methods applied to the study of geophysics and high frequency financial data

NASA Astrophysics Data System (ADS)

Mariani, M. C.; Basu, K.

2014-09-01

In this work we applied locally weighted scatterplot smoothing techniques (Lowess/Loess) to Geophysical and high frequency financial data. We first analyze and apply this technique to the California earthquake geological data. A spatial analysis was performed to show that the estimation of the earthquake magnitude at a fixed location is very accurate up to the relative error of 0.01%. We also applied the same method to a high frequency data set arising in the financial sector and obtained similar satisfactory results. The application of this approach to the two different data sets demonstrates that the overall method is accurate and efficient, and the Lowess approach is much more desirable than the Loess method. The previous works studied the time series analysis; in this paper our local regression models perform a spatial analysis for the geophysics data providing different information. For the high frequency data, our models estimate the curve of best fit where data are dependent on time.

Associations between birth registration and early child growth and development: evidence from 31 low- and middle-income countries.

PubMed

Jeong, Joshua; Bhatia, Amiya; Fink, Günther

2018-05-30

Lack of legal identification documents can impose major challenges for children in low- and middle-income countries (LMICs). The aim of this study was to investigate the association between not having a birth certificate and young children's physical growth and developmental outcomes in LMICs. We combined nationally representative data from the Multiple Indicator Cluster Surveys in 31 LMICs. For our measure of birth registration, primary caregivers reported on whether the child had a birth certificate. Early child outcome measures focused on height-for-age z-scores (HAZ), weight-for-age z-scores (WAZ), weight-for-height z-scores (WHZ), and standardized scores of the Early Childhood Development Index (ECDI) for a subsample of children aged 36-59 months. We used linear regression models with country fixed effects to estimate the relationship between birth registration and child outcomes. In fully adjusted models, we controlled for a variety of child, caregiver, household, and access to child services covariates, including cluster-level fixed effects. In the total sample, 34.7% of children aged 0-59 months did not possess a birth certificate. After controlling for covariates, not owning a birth certificate was associated with lower HAZ (β = - 0.18; 95% CI: -0.23, - 0.14), WAZ (β = - 0.10, 95% CI: -0.13, - 0.07), and ECDI z-scores (β = - 0.10; 95% CI: -0.13, - 0.07) among children aged 36-59 months. Our findings document links between birth registration and children's early growth and development outcomes. Efforts to increase birth registration may be promising for promoting early childhood development in LMICs.

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.

Calf birth weight, gestation length, calving ease, and neonatal calf mortality in Holstein, Jersey, and crossbred cows in a pasture system.

PubMed

Dhakal, K; Maltecca, C; Cassady, J P; Baloche, G; Williams, C M; Washburn, S P

2013-01-01

Holstein (HH), Jersey (JJ), and crosses of these breeds were mated to HH or JJ bulls to form purebreds, reciprocal crosses, backcrosses, and other crosses in a rotational mating system. The herd was located at the Center for Environmental Farming Systems in Goldsboro, North Carolina. Data for calf birth weight (CBW), calving ease (0 for unassisted, n=1,135, and 1 for assisted, n=96), and neonatal calf mortality (0 for alive, n=1,150, and 1 for abortions recorded after mid-gestation, stillborn, and dead within 48 h, n=81) of calves (n=1,231) were recorded over 9 calving seasons from 2003 through 2011. Gestation length (GL) was calculated as the number of days from last insemination to calving. Linear mixed models for CBW and GL included fixed effects of sex, parity (first vs. later parities), twin status, and 6 genetic groups: HH, JJ, reciprocal F(1) crosses (HJ, JH), crosses >50% Holsteins (HX) and crosses >50% Jerseys (JX), where sire breed is listed first. The CBW model also included GL as a covariate. Logistic regression for calving ease and neonatal calf mortality included fixed effects of sex, parity, and genetic group. Genetic groups were replaced by linear regression using percentage of HH genes as coefficients on the above models and included as covariates to determine various genetic effects. Year and dam were included as random effects in all models. Female calves (27.57±0.54 kg), twins (26.39±1.0 kg), and calves born to first-parity cows (27.67±0.56 kg) had lower CBW than respective male calves (29.53±0.53 kg), single births (30.71±0.19 kg), or calves born to multiparous cows (29.43±0.52 kg). Differences in genetic groups were observed for CBW and GL. Increased HH percentage in the calf increased CBW (+9.3±0.57 kg for HH vs. JJ calves), and increased HH percentage in the dams increased CBW (+1.71±0.53 kg for calves from HH dams vs. JJ dams); JH calves weighed 1.33 kg more than reciprocal HJ calves. Shorter GL was observed for twin births (272.6±1.1 d), female calves (273.9±0.6 d), and for first-parity dams (273.8±0.6 d). Direct genetic effects of HH alleles shortened GL (-3.5±0.7 d), whereas maternal HH alleles increased GL (2.7±0.6 d). Female calves had lower odds ratio (0.32, confidence interval=0.10-0.99) for neonatal calf mortality in second and later parities than did male calves. Maternal heterosis in crossbred primiparous dams was associated with reduced calf mortality. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The impact of social engagement on health-related quality of life and depressive symptoms in old age - evidence from a multicenter prospective cohort study in Germany.

PubMed

Hajek, André; Brettschneider, Christian; Mallon, Tina; Ernst, Annette; Mamone, Silke; Wiese, Birgitt; Weyerer, Siegfried; Werle, Jochen; Pentzek, Michael; Fuchs, Angela; Stein, Janine; Luck, Tobias; Bickel, Horst; Weeg, Dagmar; Wagner, Michael; Heser, Kathrin; Maier, Wolfgang; Scherer, Martin; Riedel-Heller, Steffi G; König, Hans-Helmut

2017-07-14

Thus far, only a few longitudinal studies investigated the impact of social engagement on health-related quality of life (HRQoL) and depressive symptoms in old age. Therefore, we aimed to examine the impact of social engagement on HRQoL and depressive symptoms in late life. Individuals aged 75 years and over at baseline were interviewed every 1.5 years in a multicenter prospective cohort study in Germany. While HRQoL was quantified by using the Visual Analogue Scale (EQ VAS) of the EQ-5D instrument, depressive symptoms was assessed by using the Geriatric Depression Scale (GDS). Individuals reported the frequency ("never" to "every day") of social engagement (e.g., engagement in the church, as a volunteer, in a party, or in a club) in the last four weeks. Fixed effects regressions were used to estimate the effect of social engagement on the outcome variables. After adjusting for age, marital status, functional status and chronic diseases, fixed effects regressions revealed that the onset of social engagement markedly increased HRQoL and considerably decreased depressive symptoms in the total sample and in women, but not men. Our findings corroborate the relevance of social engagement for HRQoL and depressive symptoms in old age. Encouraging the individuals to start, maintain and expand social engagement in late life might help to maintain and improve HRQoL and decrease depressive symptoms.

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2018-05-29

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

Economic crisis and suicidal behaviour: the role of unemployment, sex and age in Andalusia, Southern Spain

PubMed Central

2014-01-01

Introduction Although suicide rates have increased in some European countries in relation to the current economic crisis and austerity policies, that trend has not been observed in Spain. This study examines the impact of the economic crisis on suicide attempts, the previously neglected endpoint of the suicidal process, and its relation to unemployment, age and sex. Methods The study was carried out in Andalusia, the most populated region of Spain, and which has a high level of unemployment. Information on suicide attempts attended by emergency services was extracted from the Health Emergencies Public Enterprise Information System (SIEPES). Suicide attempts occurring between 2003 and 2012 were included, in order to cover five years prior to the crisis (2003–2007) and five years after its onset (2008–2012). Information was retrieved from 24,380 cases (11,494 men and 12,886 women) on sex, age, address, and type of attention provided. Age-adjusted suicide attempt rates were calculated. Excess numbers of attempts from 2008 to 2012 were estimated for each sex using historical trends of the five previous years, through time regression models using negative binomial regression analysis. To assess the association between unemployment and suicide attempts rates, linear regression models with fixed effects were performed. Results A sharp increase in suicide attempt rates in Andalusia was detected after the onset of the crisis, both in men and in women. Adults aged 35 to 54 years were the most affected in both sexes. Suicide attempt rates were associated with unemployment rates in men, accounting for almost half of the cases during the five initial years of the crisis. Women were also affected during the recession period but this association could not be specifically attributed to unemployment. Conclusions This study enhances our understanding of the potential effects of the economic crisis on the rapidly increasing suicide attempt rates in women and men, and the association of unemployment with growing suicidal behaviour in men. Research on the suicide effects of the economic crisis may need to take into account earlier stages of the suicidal process, and that this effect may differ by age and sex. PMID:25062772

Incremental Net Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, Michael

2005-01-01

A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…

Effects of supplemental vibrational force on space closure, treatment duration, and occlusal outcome: A multicenter randomized clinical trial.

PubMed

DiBiase, Andrew T; Woodhouse, Neil R; Papageorgiou, Spyridon N; Johnson, Nicola; Slipper, Carmel; Grant, James; Alsaleh, Maryam; Khaja, Yousef; Cobourne, Martyn T

2018-04-01

A multicenter parallel 3-arm randomized clinical trial was carried out in 3 university hospitals in the United Kingdom to investigate the effect of supplemental vibratory force on space closure and treatment outcome with fixed appliances. Eighty-one subjects less than 20 years of age with mandibular incisor irregularity undergoing extraction-based fixed appliance treatment were randomly allocated to supplementary (20 minutes/day) use of an intraoral vibrational device (AcceleDent; OrthoAccel Technologies, Houston, Tex) (n = 29), an identical nonfunctional (sham) device (n = 25), or fixed-appliance only (n = 27). Space closure in the mandibular arch was measured from dental study casts taken at the start of space closure, at the next appointment, and at completion of space closure. Final records were taken at completion of treatment. Data were analyzed blindly on a per-protocol basis with descriptive statistics, 1-way analysis of variance, and linear regression modeling with 95% confidence intervals. Sixty-one subjects remained in the trial at start of space closure, with all 3 groups comparable for baseline characteristics. The overall median rate of initial mandibular arch space closure (primary outcome) was 0.89 mm per month with no difference for either the AcceleDent group (difference, -0.09 mm/month; 95% CI, -0.39 to 0.22 mm/month; P = 0.57) or the sham group (difference, -0.02 mm/month; 95% CI, -0.32 to 0.29 mm/month; P = 0.91) compared with the fixed only group. Similarly, no significant differences were identified between groups for secondary outcomes, including overall treatment duration (median, 18.6 months; P >0.05), number of visits (median, 12; P >0.05), and percentage of improvement in the Peer Assessment Rating (median, 90.0%; P >0.05). Supplemental vibratory force during orthodontic treatment with fixed appliances does not affect space closure, treatment duration, total number of visits, or final occlusal outcome. NCT02314975. The protocol was not published before trial commencement. AcceleDent units were donated by OrthoAccel Technologies; no contribution to the conduct or the writing of this study was made by the manufacturer. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Mortality from breast carcinoma among US women: the role and implications of socio-economics, heterogeneous insurance, screening mammography, and geography.

PubMed

Okunade, Albert A; Karakus, Mustafa C

2003-11-01

Despite rapid advances in medicine and beneficial lifestyle changes, the incidence and mortality rate of gynecologic carcinoma remains high worldwide. This paper presents the econometric model findings of the major drivers of breast cancer mortality among US women. The results have implications for public health policy formulation on disease incidence and the drivers of mortality risks. The research methodology is a fixed-effects GLS regression model of breast cancer mortality in US females age 25 and above, using 1990-1997 time-series data pooled across 50 US states and DC. The covariates are age, years schooled, family income, 'screening' mammography, insurance coverage types, race, and US census region. The regressions have strong explanatory powers. Finding education and income to be significantly and positively correlated with mortality supports the 'life in the fast lanes' hypothesis of Phelps. The policy of raising a woman's education at a given income appears more beneficial than raising her income at a given education level. The relatively higher mortality rate for Blacks suggests implementing culturally appropriate set of disease prevention and health promotion programs and policies. Mortality differs across insurance types with Medicaid the worst suggesting need for program reform. Mortality is greater for women ages 25-44 years, females 40-49 years who have had screening mammography, smokers, and residents of some US states. These findings suggest imposing more effective tobacco use control policies (e.g., imposing a special tobacco tax on adult smokers), creating a more tractable screening mammography surveillance system, and designing region-specific programs to cut breast cancer mortality risks.

Aerial robot intelligent control method based on back-stepping

NASA Astrophysics Data System (ADS)

Zhou, Jian; Xue, Qian

2018-05-01

The aerial robot is characterized as strong nonlinearity, high coupling and parameter uncertainty, a self-adaptive back-stepping control method based on neural network is proposed in this paper. The uncertain part of the aerial robot model is compensated online by the neural network of Cerebellum Model Articulation Controller and robust control items are designed to overcome the uncertainty error of the system during online learning. At the same time, particle swarm algorithm is used to optimize and fix parameters so as to improve the dynamic performance, and control law is obtained by the recursion of back-stepping regression. Simulation results show that the designed control law has desired attitude tracking performance and good robustness in case of uncertainties and large errors in the model parameters.

The relationship between effectiveness and costs measured by a risk-adjusted case-mix system: multicentre study of Catalonian population data bases.

PubMed

Sicras-Mainar, Antoni; Navarro-Artieda, Ruth; Blanca-Tamayo, Milagrosa; Velasco-Velasco, Soledad; Escribano-Herranz, Esperanza; Llopart-López, Josep Ramon; Violan-Fors, Concepción; Vilaseca-Llobet, Josep Maria; Sánchez-Fontcuberta, Encarna; Benavent-Areu, Jaume; Flor-Serra, Ferran; Aguado-Jodar, Alba; Rodríguez-López, Daniel; Prados-Torres, Alejandra; Estelrich-Bennasar, Jose

2009-06-25

The main objective of this study is to measure the relationship between morbidity, direct health care costs and the degree of clinical effectiveness (resolution) of health centres and health professionals by the retrospective application of Adjusted Clinical Groups in a Spanish population setting. The secondary objectives are to determine the factors determining inadequate correlations and the opinion of health professionals on these instruments. We will carry out a multi-centre, retrospective study using patient records from 15 primary health care centres and population data bases. The main measurements will be: general variables (age and sex, centre, service [family medicine, paediatrics], and medical unit), dependent variables (mean number of visits, episodes and direct costs), co-morbidity (Johns Hopkins University Adjusted Clinical Groups Case-Mix System) and effectiveness.The totality of centres/patients will be considered as the standard for comparison. The efficiency index for visits, tests (laboratory, radiology, others), referrals, pharmaceutical prescriptions and total will be calculated as the ratio: observed variables/variables expected by indirect standardization.The model of cost/patient/year will differentiate fixed/semi-fixed (visits) costs of the variables for each patient attended/year (N = 350,000 inhabitants). The mean relative weights of the cost of care will be obtained. The effectiveness will be measured using a set of 50 indicators of process, efficiency and/or health results, and an adjusted synthetic index will be constructed (method: percentile 50).The correlation between the efficiency (relative-weights) and synthetic (by centre and physician) indices will be established using the coefficient of determination. The opinion/degree of acceptance of physicians (N = 1,000) will be measured using a structured questionnaire including various dimensions. multiple regression analysis (procedure: enter), ANCOVA (method: Bonferroni's adjustment) and multilevel analysis will be carried out to correct models. The level of statistical significance will be p < 0.05.

What types of social interactions reduce the risk of psychological distress? Fixed effects longitudinal analysis of a cohort of 30,271 middle-to-older aged Australians.

PubMed

Feng, Xiaoqi; Astell-Burt, Thomas

2016-11-01

Research on the impact of social interactions on psychological distress tends to be limited to particular forms of support, cross-sectional designs and by the spectre of omitted variables bias. A baseline sample with 3.4±0.95 years follow-up time was extracted from the 45 and Up Study. Change in the risk of psychological distress (Kessler Psychological Distress Scale) was assessed using fixed effects logistic regressions in relation to the number of times in the past week a participant: i) spent time with friends or family they did not live with; ii) talked to friends, relatives or others on the telephone; iii) attended meetings at social clubs or religious groups; and the count of people outside their home, but within one hour travel-time, participants felt close to. Separate models were fitted for men and women, adjusting for age, income, economic and couple status. An increase in the number of social interactions was associated with a reduction in the risk of psychological distress, with some gender differences. Interactions with friends or family were important for women (adjusted OR 0.85, 95%CI 0.74, 0.98, p=0.024), whereas telephone calls were effective among men (adjusted OR 0.83, 95%CI 0.72, 0.96, p=0.011). Strong effects for the number of people that can be relied on were observed for men and women, but attendance at clubs and groups was not. No age-specific effects were observed. No indicator of positive mental health. Policies targeting greater social interactions in middle-to-older age may help protect mental health. Copyright © 2016 Elsevier B.V. All rights reserved.

Collaborative localization in wireless sensor networks via pattern recognition in radio irregularity using omnidirectional antennas.

PubMed

Jiang, Joe-Air; Chuang, Cheng-Long; Lin, Tzu-Shiang; Chen, Chia-Pang; Hung, Chih-Hung; Wang, Jiing-Yi; Liu, Chang-Wang; Lai, Tzu-Yun

2010-01-01

In recent years, various received signal strength (RSS)-based localization estimation approaches for wireless sensor networks (WSNs) have been proposed. RSS-based localization is regarded as a low-cost solution for many location-aware applications in WSNs. In previous studies, the radiation patterns of all sensor nodes are assumed to be spherical, which is an oversimplification of the radio propagation model in practical applications. In this study, we present an RSS-based cooperative localization method that estimates unknown coordinates of sensor nodes in a network. Arrangement of two external low-cost omnidirectional dipole antennas is developed by using the distance-power gradient model. A modified robust regression is also proposed to determine the relative azimuth and distance between a sensor node and a fixed reference node. In addition, a cooperative localization scheme that incorporates estimations from multiple fixed reference nodes is presented to improve the accuracy of the localization. The proposed method is tested via computer-based analysis and field test. Experimental results demonstrate that the proposed low-cost method is a useful solution for localizing sensor nodes in unknown or changing environments.

Effectiveness of Vildagliptin in Clinical Practice: Pooled Analysis of Three Korean Observational Studies (the VICTORY Study)

PubMed Central

Suh, Sunghwan; Song, Sun Ok; Kim, Jae Hyeon; Cho, Hyungjin

2017-01-01

The present observational study aimed to evaluate the clinical effectiveness of vildagliptin with metformin in Korean patients with type 2 diabetes mellitus (T2DM). Data were pooled from the vildagliptin postmarketing survey (PMS), the vildagliptin/metformin fixed drug combination (DC) PMS, and a retrospective observational study of vildagliptin/metformin (fixed DC or free DC). The effectiveness endpoint was the proportion of patients who achieved a glycemic target (HbA1c) of ≤7.0% at 24 weeks. In total, 4303 patients were included in the analysis; of these, 2087 patients were eligible. The mean patient age was 56.99 ± 11.25 years. Overall, 58.94% patients achieved an HbA1c target of ≤7.0% at 24 weeks. The glycemic target achievement rate was significantly greater in patients with baseline HbA1c < 7.5% versus ≥7.5% (84.64% versus 43.97%), receiving care at the hospital versus clinic (67.95% versus 52.33%), and receiving vildagliptin/metformin fixed DC versus free DC (70.69% versus 55.42%). Multivariate logistic regression analysis indicated that disease duration (P < 0.0001), baseline HbA1c (P < 0.0001), and DC type (P = 0.0103) had significant effects on drug effectiveness. Vildagliptin plus metformin appeared as an effective treatment option for patients with T2DM in clinical practice settings in Korea. PMID:29057274

Effect of Conjugated Linoleic Acid Feeding on the Growth Performance and Meat Fatty Acid Profiles in Broiler: Meta-analysis

PubMed Central

Cho, Sangbuem; Ryu, Chaehwa; Yang, Jinho; Mbiriri, David Tinotenda; Choi, Chang-Weon; Chae, Jung-Il; Kim, Young-Hoon; Shim, Kwan-Seob; Kim, Young Jun; Choi, Nag-Jin

2013-01-01

The effect of conjugated linoleic acid (CLA) feeding on growth performance and fatty acid profiles in thigh meat of broiler chicken was investigated using meta-analysis with a total of 9 studies. Overall effects were calculated by standardized mean differences between treatment (CLA fed) and control using Hedges’s adjusted g from fixed and random effect models. Meta-regression was conducted to evaluate the effect of CLA levels. Subgroups in the same study were designated according to used levels of CLA, CP levels or substituted oils in diets. The effects on final body weight, weight gain, feed intake and feed conversion ratio were investigated as growth parameters. Total saturated and unsaturated fatty acid concentrations and C16:0, C18:0, C18:2 and C18:3 concentrations in thigh meat of broiler chicken were used as fatty acid profile parameters. The overall effect of CLA feeding on final weight was negative and it was only significant in fixed effect model (p<0.01). Significantly lower weight gain, feed intake and higher feed conversion ratio compared to control were found (p<0.05). CLA feeding on the overall increased total saturated fatty acid concentration in broilers compared to the control diet (p<0.01). Total unsaturated fatty acid concentration was significantly decreased by CLA feeding (p<0.01). As for individual fatty acid profiles, C16:0, C18:0 and C18:3 were increased and C18:2 was significantly decreased by CLA feeding (p<0.01). In conclusion, CLA was proved not to be beneficial for improving growth performance, whereas it might be supposed that CLA is effective modulating n-6/n-3 fatty acids ratio in thigh meat. However, the economical compensation of the loss from suppressed growth performance and increased saturated fatty acids with the benefit from enhanced n-6/n-3 ratio should be investigated in further studies in order to propose an appropriate use of dietary CLA in the broiler industry. PMID:25049878

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran

NASA Astrophysics Data System (ADS)

Amini, Heresh; Taghavi-Shahri, Seyed-Mahmood; Henderson, Sarah B.; Hosseini, Vahid; Hassankhany, Hossein; Naderi, Maryam; Ahadi, Solmaz; Schindler, Christian; Künzli, Nino; Yunesian, Masud

2016-09-01

Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R2 values for the LUR models ranged from 0.69 to 0.78 for NO, 0.64 to 0.75 for NO2, and 0.61 to 0.79 for NOx. The most predictive variables were: distance to the traffic access control zone; distance to primary schools; green space; official areas; bridges; and slope. The annual average concentrations of all pollutants were high, approaching those reported for megacities in Asia. At 1000 randomly-selected locations the correlations between cooler and warmer season estimates were 0.64 for NO, 0.58 for NOX, and 0.30 for NO2. Seasonal differences in spatial patterns of pollution are likely driven by differences in source contributions and meteorology. These models provide a basis for understanding long-term exposures and chronic health effects of air pollution in Tehran, where such research has been limited.

Annual and seasonal spatial models for nitrogen oxides in Tehran, Iran

PubMed Central

Amini, Heresh; Taghavi-Shahri, Seyed-Mahmood; Henderson, Sarah B.; Hosseini, Vahid; Hassankhany, Hossein; Naderi, Maryam; Ahadi, Solmaz; Schindler, Christian; Künzli, Nino; Yunesian, Masud

2016-01-01

Very few land use regression (LUR) models have been developed for megacities in low- and middle-income countries, but such models are needed to facilitate epidemiologic research on air pollution. We developed annual and seasonal LUR models for ambient oxides of nitrogen (NO, NO2, and NOX) in the Middle Eastern city of Tehran, Iran, using 2010 data from 23 fixed monitoring stations. A novel systematic algorithm was developed for spatial modeling. The R2 values for the LUR models ranged from 0.69 to 0.78 for NO, 0.64 to 0.75 for NO2, and 0.61 to 0.79 for NOx. The most predictive variables were: distance to the traffic access control zone; distance to primary schools; green space; official areas; bridges; and slope. The annual average concentrations of all pollutants were high, approaching those reported for megacities in Asia. At 1000 randomly-selected locations the correlations between cooler and warmer season estimates were 0.64 for NO, 0.58 for NOX, and 0.30 for NO2. Seasonal differences in spatial patterns of pollution are likely driven by differences in source contributions and meteorology. These models provide a basis for understanding long-term exposures and chronic health effects of air pollution in Tehran, where such research has been limited. PMID:27622593

Modeling the Chagas’ disease after stem cell transplantation

NASA Astrophysics Data System (ADS)

Galvão, Viviane; Miranda, José Garcia Vivas

2009-04-01

A recent model for Chagas’ disease after stem cell transplantation is extended for a three-dimensional multi-agent-based model. The computational model includes six different types of autonomous agents: inflammatory cell, fibrosis, cardiomyocyte, proinflammatory cytokine tumor necrosis factor- α, Trypanosoma cruzi, and bone marrow stem cell. Only fibrosis is fixed and the other types of agents can move randomly through the empty spaces using the three-dimensional Moore neighborhood. Bone marrow stem cells can promote apoptosis in inflammatory cells, fibrosis regression and can differentiate in cardiomyocyte. T. cruzi can increase the number of inflammatory cells. Inflammatory cells and tumor necrosis factor- α can increase the quantity of fibrosis. Our results were compared with experimental data giving a fairly fit and they suggest that the inflammatory cells are important for the development of fibrosis.

A Bayesian Nonparametric Meta-Analysis Model

ERIC Educational Resources Information Center

Karabatsos, George; Talbott, Elizabeth; Walker, Stephen G.

2015-01-01

In a meta-analysis, it is important to specify a model that adequately describes the effect-size distribution of the underlying population of studies. The conventional normal fixed-effect and normal random-effects models assume a normal effect-size population distribution, conditionally on parameters and covariates. For estimating the mean overall…

Polarizability effects on the structure and dynamics of ionic liquids

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

Cavalcante, Ary de Oliveira, E-mail: arycavalcante@ufam.edu.br; Departamento de Química, Universidade Federal do Amazonas, Av. Rodrigo Octávio, 6200, Coroado, Manaus, AM; Ribeiro, Mauro C. C.

2014-04-14

Polarization effects on the structure and dynamics of ionic liquids are investigated using molecular dynamics simulations. Four different ionic liquids were simulated, formed by the anions Cl{sup −} and PF{sub 6}{sup −}, treated as single fixed charge sites, and the 1-n-alkyl-3-methylimidazolium cations (1-ethyl and 1-butyl-), which are polarizable. The partial charge fluctuation of the cations is provided by the electronegativity equalization model (EEM) and a complete parameter set for the cations electronegativity (χ) and hardness (J) is presented. Results obtained from a non-polarizable model for the cations are also reported for comparison. Relative to the fixed charged model, the equilibriummore » structure of the first solvation shell around the imidazolium cations shows that inclusion of EEM polarization forces brings cations closer to each other and that anions are preferentially distributed above and below the plane of the imidazolium ring. The polarizable model yields faster translational and reorientational dynamics than the fixed charges model in the rotational-diffusion regime. In this sense, the polarizable model dynamics is in better agreement with the experimental data.« less

Early home literacy and adolescents’ online reading behavior in comparative perspective

PubMed Central

Notten, Natascha; Becker, Birgit

2017-01-01

Online reading behavior can be regarded as a ‘new’ form of cultural capital in today’s digital world. However, it is unclear whether ‘traditional’ mechanisms of cultural and social reproduction are also found in this domain, and whether they manifest uniformly across countries at different stages of development. This article analyzes whether the early home literacy environment has an impact on informational online reading behavior among adolescents and whether this association varies between countries with different levels of digitalization and educational expansion. Data from the 2009 Programme for International Student Assessment (PISA) were used for the empirical analyses. The results of regression models with country-fixed effects indicate a positive association between literacy activities in early childhood and informational online reading at age 15. This association was quite stable across countries. These findings are discussed in light of cultural and social reproduction theory and digital divide research. PMID:29276306

THE ROLE OF LOCATION IN EVALUATING RACIAL WAGE DISPARITY

PubMed Central

Black, Dan A.; Kolesnikova, Natalia; Sanders, Seth G.; Taylor, Lowell J.

2015-01-01

A standard object of empirical analysis in labor economics is a modified Mincer wage function in which an individual’s log wage is specified to be a function of education, experience, and an indicator variable identifying race. We analyze this approach in a context in which individuals live and work in different locations (and thus face different housing prices and wages). Our model provides a justification for the traditional approach, but with the important caveat that the regression should include location-specific fixed effects. Empirical analyses of men in U.S. labor markets demonstrate that failure to condition on location causes us to (i) overstate the decline in black-white wage disparity over the past 60 years, and (ii) understate racial and ethnic wage gaps that remain after taking into account measured cognitive skill differences that emerge when workers are young. PMID:25798025

The More or the Better? How Sex Contributes to Life Satisfaction.

PubMed

Schmiedeberg, Claudia; Huyer-May, Bernadette; Castiglioni, Laura; Johnson, Matthew D

2017-02-01

Much cross-sectional research documented associations between sexuality and life satisfaction, but very little longitudinal research on the topic has considered whether changes in sexuality and life satisfaction unfold together over time. Using data from 5582 individuals in partnerships surveyed across 5786 intimate relationships (providing 18,712 observations for analysis) during five waves of the German Family Panel (pairfam), this study examined whether intraindividual changes in sexual frequency and satisfaction were associated with corresponding intraindividual changes in life satisfaction. Fixed effects regression modeling results showed that individuals reported a greater increase (decrease) in life satisfaction when they also experienced a more substantial increase (decrease) in sexual frequency and satisfaction. This finding was consistent for men and women. This study contributes to the literature by documenting that naturally occurring increases in sexual frequency and satisfaction over time predicted corresponding increases in life satisfaction.

Integrative eQTL analysis of tumor and host omics data in individuals with bladder cancer.

PubMed

Pineda, Silvia; Van Steen, Kristel; Malats, Núria

2017-09-01

Integrative analyses of several omics data are emerging. The data are usually generated from the same source material (i.e., tumor sample) representing one level of regulation. However, integrating different regulatory levels (i.e., blood) with those from tumor may also reveal important knowledge about the human genetic architecture. To model this multilevel structure, an integrative-expression quantitative trait loci (eQTL) analysis applying two-stage regression (2SR) was proposed. This approach first regressed tumor gene expression levels with tumor markers and the adjusted residuals from the previous model were then regressed with the germline genotypes measured in blood. Previously, we demonstrated that penalized regression methods in combination with a permutation-based MaxT method (Global-LASSO) is a promising tool to fix some of the challenges that high-throughput omics data analysis imposes. Here, we assessed whether Global-LASSO can also be applied when tumor and blood omics data are integrated. We further compared our strategy with two 2SR-approaches, one using multiple linear regression (2SR-MLR) and other using LASSO (2SR-LASSO). We applied the three models to integrate genomic, epigenomic, and transcriptomic data from tumor tissue with blood germline genotypes from 181 individuals with bladder cancer included in the TCGA Consortium. Global-LASSO provided a larger list of eQTLs than the 2SR methods, identified a previously reported eQTLs in prostate stem cell antigen (PSCA), and provided further clues on the complexity of APBEC3B loci, with a minimal false-positive rate not achieved by 2SR-MLR. It also represents an important contribution for omics integrative analysis because it is easy to apply and adaptable to any type of data. © 2017 WILEY PERIODICALS, INC.

Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations

PubMed Central

Good, Benjamin H.; Rouzine, Igor M.; Balick, Daniel J.; Hallatschek, Oskar; Desai, Michael M.

2012-01-01

When large asexual populations adapt, competition between simultaneously segregating mutations slows the rate of adaptation and restricts the set of mutations that eventually fix. This phenomenon of interference arises from competition between mutations of different strengths as well as competition between mutations that arise on different fitness backgrounds. Previous work has explored each of these effects in isolation, but the way they combine to influence the dynamics of adaptation remains largely unknown. Here, we describe a theoretical model to treat both aspects of interference in large populations. We calculate the rate of adaptation and the distribution of fixed mutational effects accumulated by the population. We focus particular attention on the case when the effects of beneficial mutations are exponentially distributed, as well as on a more general class of exponential-like distributions. In both cases, we show that the rate of adaptation and the influence of genetic background on the fixation of new mutants is equivalent to an effective model with a single selection coefficient and rescaled mutation rate, and we explicitly calculate these effective parameters. We find that the effective selection coefficient exactly coincides with the most common fixed mutational effect. This equivalence leads to an intuitive picture of the relative importance of different types of interference effects, which can shift dramatically as a function of the population size, mutation rate, and the underlying distribution of fitness effects. PMID:22371564

Addressing the unemployment-mortality conundrum: non-linearity is the answer.

PubMed

Bonamore, Giorgio; Carmignani, Fabrizio; Colombo, Emilio

2015-02-01

The effect of unemployment on mortality is the object of a lively literature. However, this literature is characterized by sharply conflicting results. We revisit this issue and suggest that the relationship might be non-linear. We use data for 265 territorial units (regions) within 23 European countries over the period 2000-2012 to estimate a multivariate regression of mortality. The estimating equation allows for a quadratic relationship between unemployment and mortality. We control for various other determinants of mortality at regional and national level and we include region-specific and time-specific fixed effects. The model is also extended to account for the dynamic adjustment of mortality and possible lagged effects of unemployment. We find that the relationship between mortality and unemployment is U shaped. In the benchmark regression, when the unemployment rate is low, at 3%, an increase by one percentage point decreases average mortality by 0.7%. As unemployment increases, the effect decays: when the unemployment rate is 8% (sample average) a further increase by one percentage point decreases average mortality by 0.4%. The effect changes sign, turning from negative to positive, when unemployment is around 17%. When the unemployment rate is 25%, a further increase by one percentage point raises average mortality by 0.4%. Results hold for different causes of death and across different specifications of the estimating equation. We argue that the non-linearity arises because the level of unemployment affects the psychological and behavioural response of individuals to worsening economic conditions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

Effect of Whey Supplementation on Circulating C-Reactive Protein: A Meta-Analysis of Randomized Controlled Trials

PubMed Central

Zhou, Ling-Mei; Xu, Jia-Ying; Rao, Chun-Ping; Han, Shufen; Wan, Zhongxiao; Qin, Li-Qiang

2015-01-01

Whey supplementation is beneficial for human health, possibly by reducing the circulating C-reactive protein (CRP) level, a sensitive marker of inflammation. Thus, a meta-analysis of randomized controlled trials was conducted to evaluate their relationship. A systematic literature search was conducted in July, 2014, to identify eligible studies. Either a fixed-effects model or a random-effects model was used to calculate pooled effects. The meta-analysis results of nine trials showed a slight, but no significant, reduction of 0.42 mg/L (95% CI −0.96, 0.13) in CRP level with the supplementation of whey protein and its derivates. Relatively high heterogeneity across studies was observed. Subgroup analyses showed that whey significantly lowered CRP by 0.72 mg/L (95% CI −0.97, −0.47) among trials with a daily whey dose ≥20 g/day and by 0.67 mg/L (95% CI −1.21, −0.14) among trials with baseline CRP ≥3 mg/L. Meta-regression analysis revealed that the baseline CRP level was a potential effect modifier of whey supplementation in reducing CRP. In conclusion, our meta-analysis did not find sufficient evidence that whey and its derivates elicited a beneficial effect in reducing circulating CRP. However, they may significantly reduce CRP among participants with highly supplemental doses or increased baseline CRP levels. PMID:25671415

Estimation of Standard Error of Regression Effects in Latent Regression Models Using Binder's Linearization. Research Report. ETS RR-07-09

ERIC Educational Resources Information Center

Li, Deping; Oranje, Andreas

2007-01-01

Two versions of a general method for approximating standard error of regression effect estimates within an IRT-based latent regression model are compared. The general method is based on Binder's (1983) approach, accounting for complex samples and finite populations by Taylor series linearization. In contrast, the current National Assessment of…

Grand Advantage: Family Wealth and Grandchildren’s Educational Achievement in Sweden

PubMed Central

Hällsten, Martin; Pfeffer, Fabian T.

2017-01-01

We study the role of family wealth for children’s educational achievement using novel and unique Swedish register data. In particular, we focus on the relationship between grandparents’ wealth and their grandchildren’s educational achievement. Doing so allows us to reliably establish the independent role of wealth in contributing to long-term inequalities in opportunity. We use regression models with rich controls to account for observed socioeconomic characteristics of families, cousin fixed effects to net out potentially unobserved grandparental effects, and marginal structural models to account for endogenous selection. We find substantial associations between grandparents’ wealth and their grandchildren’s grade point averages (GPA) in the 9th grade that are only partly mediated by the socioeconomic characteristics and wealth of parents. Our findings indicate that family wealth inequality – even in a comparatively egalitarian context like Sweden – has profound consequences for the distribution of opportunity across multiple generations. We posit that our estimates of the long-term consequences of wealth inequality may be conservative for nations other than Sweden, like the United States, where family wealth – in addition to its insurance and normative functions – allows the direct purchase of educational quality and access. PMID:29200464

Impact of South American heroin on the US heroin market 1993-2004.

PubMed

Ciccarone, Daniel; Unick, George J; Kraus, Allison

2009-09-01

The past two decades have seen an increase in heroin-related morbidity and mortality in the United States. We report on trends in US heroin retail price and purity, including the effect of entry of Colombian-sourced heroin on the US heroin market. The average standardized price ($/mg-pure) and purity (% by weight) of heroin from 1993 to 2004 was from obtained from US Drug Enforcement Agency retail purchase data for 20 metropolitan statistical areas. Univariate statistics, robust Ordinary Least Squares regression and mixed fixed and random effect growth curve models were used to predict the price and purity data in each metropolitan statistical area over time. Over the 12 study years, heroin price decreased 62%. The median percentage of all heroin samples that are of South American origin increased an absolute 7% per year. Multivariate models suggest percent South American heroin is a significant predictor of lower heroin price and higher purity adjusting for time and demographics. These analyses reveal trends to historically low-cost heroin in many US cities. These changes correspond to the entrance into and rapid domination of the US heroin market by Colombian-sourced heroin. The implications of these changes are discussed.

No association of smoke-free ordinances with profits from bingo and charitable games in Massachusetts.

PubMed

Glantz, S A; Wilson-Loots, R

2003-12-01

Because it is widely played, claims that smoking restrictions will adversely affect bingo games is used as an argument against these policies. We used publicly available data from Massachusetts to assess the impact of 100% smoke-free ordinances on profits from bingo and other gambling sponsored by charitable organisations between 1985 and 2001. We conducted two analyses: (1) a general linear model implementation of a time series analysis with net profits (adjusted to 2001 dollars) as the dependent variable, and community (as a fixed effect), year, lagged net profits, and the length of time the ordinance had been in force as the independent variables; (2) multiple linear regression of total state profits against time, lagged profits, and the percentage of the entire state population in communities that allow charitable gaming but prohibit smoking. The general linear model analysis of data from individual communities showed that, while adjusted profits fell over time, this effect was not related to the presence of an ordinance. The analysis in terms of the fraction of the population living in communities with ordinances yielded the same result. Policymakers can implement smoke-free policies without concern that these policies will affect charitable gaming.

Grand Advantage: Family Wealth and Grandchildren's Educational Achievement in Sweden.

PubMed

Hällsten, Martin; Pfeffer, Fabian T

2017-04-01

We study the role of family wealth for children's educational achievement using novel and unique Swedish register data. In particular, we focus on the relationship between grandparents' wealth and their grandchildren's educational achievement. Doing so allows us to reliably establish the independent role of wealth in contributing to long-term inequalities in opportunity. We use regression models with rich controls to account for observed socioeconomic characteristics of families, cousin fixed effects to net out potentially unobserved grandparental effects, and marginal structural models to account for endogenous selection. We find substantial associations between grandparents' wealth and their grandchildren's grade point averages (GPA) in the 9th grade that are only partly mediated by the socioeconomic characteristics and wealth of parents. Our findings indicate that family wealth inequality - even in a comparatively egalitarian context like Sweden - has profound consequences for the distribution of opportunity across multiple generations. We posit that our estimates of the long-term consequences of wealth inequality may be conservative for nations other than Sweden, like the United States, where family wealth - in addition to its insurance and normative functions - allows the direct purchase of educational quality and access.

Exercise in prevention and treatment of anxiety and depression among children and young people.

PubMed

Larun, L; Nordheim, L V; Ekeland, E; Hagen, K B; Heian, F

2006-07-19

Depression and anxiety are common psychological disorders for children and adolescents. Psychological (e.g. psychotherapy), psychosocial (e.g. cognitive behavioral therapy) and biological (e.g. SSRIs or tricyclic drugs) treatments are the most common treatments being offered. The large variety of therapeutic interventions give rise to questions of clinical effectiveness and side effects. Physical exercise is inexpensive with few, if any, side effects. To assess the effects of exercise interventions in reducing or preventing anxiety or depression in children and young people up to 20 years of age. We searched the Cochrane Controlled Trials Register (latest issue available), MEDLINE, EMBASE, CINAHL, PsycINFO, ERIC and Sportdiscus up to August 2005. Randomised trials of vigorous exercise interventions for children and young people up to the age of 20, with outcome measures for depression and anxiety. Two authors independently selected trials for inclusion, assessed methodological quality and extracted data. The trials were combined using meta-analysis methods. A narrative synthesis was performed when the reported data did not allow statistical pooling. Sixteen studies with a total of 1191 participants between 11 and 19 years of age were included.Eleven trials compared vigourous exercise versus no intervention in a general population of children. Six studies reporting anxiety scores showed a non-significant trend in favour of the exercise group (standard mean difference (SMD) (random effects model) -0.48, 95% confidence interval (CI) -0.97 to 0.01). Five studies reporting depression scores showed a statistically significant difference in favour of the exercise group (SMD (random effects model) -0.66, 95% CI -1.25 to -0.08). However, all trials were generally of low methodological quality and they were highly heterogeneous with regard to the population, intervention and measurement instruments used. One small trial investigated children in treatment showed no statistically significant difference in depression scores in favour of the control group (SMD (fixed effects model) 0.78, 95% CI -0.47 to 2.04). No studies reported anxiety scores for children in treatment. Five trials comparing vigorous exercise to low intensity exercise show no statistically significant difference in depression and anxiety scores in the general population of children. Three trials reported anxiety scores (SMD (fixed effects model) -0.14, 95% CI -0.41 to 0.13). Two trials reported depression scores (SMD (fixed effects model) -0.15, 95% CI -0.44 to 0.14). Two small trials found no difference in depression scores for children in treatment (SMD (fixed effects model) -0.31, 95% CI -0.78 to 0.16). No studies reported anxiety scores for children in treatment. Four trials comparing exercise with psychosocial interventions showed no statistically significant difference in depression and anxiety scores in the general population of children. Two trials reported anxiety scores (SMD (fixed effects model) -0.13, 95% CI -0.43 to 0.17). Two trials reported depression scores (SMD (fixed effects model) 0.10, 95% CI-0.21 to 0.41). One trial found no difference in depression scores for children in treatment (SMD (fixed effects model) -0.31, 95% CI -0.97 to 0.35). No studies reported anxiety scores for children in treatment. Whilst there appears to be a small effect in favour of exercise in reducing depression and anxiety scores in the general population of children and adolescents, the small number of studies included and the clinical diversity of participants, interventions and methods of measurement limit the ability to draw conclusions. It makes little difference whether the exercise is of high or low intensity. The effect of exercise for children in treatment for anxiety and depression is unknown as the evidence base is scarce.

The effect of cigarette price increases on cigarette consumption, tax revenue, and smoking-related death in Africa from 1999 to 2013.

PubMed

Ho, Li-Ming; Schafferer, Christian; Lee, Jie-Min; Yeh, Chun-Yuan; Hsieh, Chi-Jung

2017-11-01

This study investigates the effects of price hikes on cigarette consumption, tobacco tax revenues, and reduction in smoking-caused mortality in 36 African countries. Using panel data from the 1999-2013 Euromonitor International, the World Bank and the World Health Organization, we applied fixed-effects and random-effects regression models of panel data to estimate the elasticity of cigarette prices and simulate the effect of price fluctuations. Cigarette price elasticity was the highest for low-income countries and considerably lower for other African economies. The administered simulation shows that with an average annual cigarette price increase of 7.38%, the average annual cigarette consumption would decrease by 3.84%, and the average annual tobacco tax revenue would increase by 19.39%. By 2050, the number of averted smoking-attributable deaths (SADs) will be the highest in South Africa, followed by the Democratic Republic of Congo, Madagascar, and Ethiopia. Excise tax increases have a significant effect on the reduction of smoking prevalence and the number of averted smoking-attributable deaths, Low-income countries are most affected by high taxation policies.

Political ideology and health in Japan: a disaggregated analysis.

PubMed

Subramanian, S V; Hamano, Tsuyoshi; Perkins, Jessica M; Koyabu, Akio; Fujisawa, Yoshikazu

2010-09-01

Recent studies from the USA and Europe suggest an association between an individual's political ideology and their health status, with those claiming to be conservatives reporting better health. The presence of this association is examined in Japan. Individual-level data from the 2000-3, 2005 and 2006 Japan General Social Survey were analysed. The outcomes of interest were self-rated poor health and smoking status. The independent variable of interest was reported political beliefs on a 5-point 'left'-to-'right' scale. Covariates included age, sex, education, income, occupational status and fixed effects for survey periods. Logistic regression models were estimated. There was an inverse association between political ideology (left to right) and self-rated poor health as well as between ideology and smoking status even after adjusting for age, sex, socioeconomic status and fixed effects for survey periods. Compared with those who identified as 'left', the OR for reporting poor health and smoking among those who identified as 'right' was 0.86 (95% CI 0.74 to 0.99) and 0.80 (95% CI 0.70 to 0.91), respectively. Health differences by political ideology have typically been interpreted as reflecting socioeconomic differences. The results from Japan corroborate the previous findings from the USA and Europe that socioeconomic differences do not account for health differences by political ideologies. Political ideology is likely to be a marker of several latent values and attitudes (eg, religiosity, individual responsibility and/or community participation) that might be beneficial for health at the individual level.

Educational inequalities in hospital care for mortally ill patients in Norway.

PubMed

Elstad, Jon Ivar

2018-02-01

Health care should be allocated fairly, irrespective of patients' social standing. Previous research suggests that highly educated patients are prioritized in Norwegian hospitals. This study examines this contentious issue by a design which addresses two methodological challenges. Control for differences in medical needs is approximated by analysing patients who died from same causes of death. Area fixed effects are used for avoiding that observed educational inequalities are contaminated by geographical differences. Men and women who died 2009-2011 at age 55-94 were examined ( N=103,000) with register data from Statistics Norway and the Norwegian Patient Registry. Educational differences in quantity of hospital-based medical care during the 12-24 months before death were analysed, separate for main causes of death. Multivariate negative binomial regression models were estimated, with fixed effects for residential areas. High-educated patients who died from cancers had significantly more outpatient consultations at somatic hospitals than low-educated patients during an average observation period of 18 months prior to death. Similar, but weaker, educational inequalities appeared for outpatient visits for patients whose deaths were due to other causes. Also, educational inequalities in number of hospital admissions were marked for those who died from cancers, but insignificant for patients who died from other causes. Even when medical needs are similar for mortally ill patients, those with high education tend to receive more medical services in Norwegian somatic hospitals than patients with low education. The roles played by physicians and patients in generating these patterns should be explored further.