8 results match your criteria penalized glmm

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Variable selection methods for identifying predictor interactions in data with repeatedly measured binary outcomes.

J Clin Transl Sci 2020 Nov 16;5(1):e59. Epub 2020 Nov 16.

Department of Medicine, Division of Rheumatology and Immunology, Medical University of South Carolina, Charleston, SC, USA.

Introduction: Identifying predictors of patient outcomes evaluated over time may require modeling interactions among variables while addressing within-subject correlation. Generalized linear mixed models (GLMMs) and generalized estimating equations (GEEs) address within-subject correlation, but identifying interactions can be difficult if not hypothesized . We evaluate the performance of several variable selection approaches for clustered binary outcomes to provide guidance for choosing between the methods. Read More

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November 2020

Laplace approximation, penalized quasi-likelihood, and adaptive Gauss-Hermite quadrature for generalized linear mixed models: towards meta-analysis of binary outcome with sparse data.

BMC Med Res Methodol 2020 06 11;20(1):152. Epub 2020 Jun 11.

Department of Population Medicine, College of Medicine, Qatar University, Al Jamiaa Street, P. O. Box 2713, Doha, Qatar.

Background: In meta-analyses of a binary outcome, double zero events in some studies cause a critical methodology problem. The generalized linear mixed model (GLMM) has been proposed as a valid statistical tool for pooling such data. Three parameter estimation methods, including the Laplace approximation (LA), penalized quasi-likelihood (PQL) and adaptive Gauss-Hermite quadrature (AGHQ) were frequently used in the GLMM. Read More

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MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL.

Stat Sin 2019 ;29(3):1585-1605

University of California.

Logistic linear mixed models are widely used in experimental designs and genetic analyses of binary traits. Motivated by modern applications, we consider the case of many groups of random effects, where each group corresponds to a variance component. When the number of variance components is large, fitting a logistic linear mixed model is challenging. Read More

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January 2019

Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model.

BioData Min 2018 15;11:12. Epub 2018 Jun 15.

1Department of Computational & Systems Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261 USA.

Background: Human microbiome studies in clinical settings generally focus on distinguishing the microbiota in health from that in disease at a specific point in time. However, microbiome samples may be associated with disease severity or continuous clinical health indicators that are often assessed at multiple time points. While the temporal data from clinical and microbiome samples may be informative, analysis of this type of data can be problematic for standard statistical methods. Read More

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Estimating the variance for heterogeneity in arm-based network meta-analysis.

Pharm Stat 2018 05 19;17(3):264-277. Epub 2018 Apr 19.

Statistical Consulting Unit, Australian National University, Canberra, ACT, Australia.

Network meta-analysis can be implemented by using arm-based or contrast-based models. Here we focus on arm-based models and fit them using generalized linear mixed model procedures. Full maximum likelihood (ML) estimation leads to biased trial-by-treatment interaction variance estimates for heterogeneity. Read More

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A comparison of analytic approaches for individual patient data meta-analyses with binary outcomes.

BMC Med Res Methodol 2017 02 16;17(1):28. Epub 2017 Feb 16.

Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada.

Background: Individual patient data meta-analyses (IPD-MA) are often performed using a one-stage approach-- a form of generalized linear mixed model (GLMM) for binary outcomes. We compare (i) one-stage to two-stage approaches (ii) the performance of two estimation procedures (Penalized Quasi-likelihood-PQL and Adaptive Gaussian Hermite Quadrature-AGHQ) for GLMMs with binary outcomes within the one-stage approach and (iii) using stratified study-effect or random study-effects.

Methods: We compare the different approaches via a simulation study, in terms of bias, mean-squared error (MSE), coverage and numerical convergence, of the pooled treatment effect (β ) and between-study heterogeneity of the treatment effect (τ ). Read More

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February 2017

Permutation-based variance component test in generalized linear mixed model with application to multilocus genetic association study.

BMC Med Res Methodol 2015 Apr 22;15:37. Epub 2015 Apr 22.

Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, , Jiangsu, People's Republic of China.

Background: In many medical studies the likelihood ratio test (LRT) has been widely applied to examine whether the random effects variance component is zero within the mixed effects models framework; whereas little work about likelihood-ratio based variance component test has been done in the generalized linear mixed models (GLMM), where the response is discrete and the log-likelihood cannot be computed exactly. Before applying the LRT for variance component in GLMM, several difficulties need to be overcome, including the computation of the log-likelihood, the parameter estimation and the derivation of the null distribution for the LRT statistic.

Methods: To overcome these problems, in this paper we make use of the penalized quasi-likelihood algorithm and calculate the LRT statistic based on the resulting working response and the quasi-likelihood. Read More

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Modelling spatial disease rates using maximum likelihood.

Authors:
B G Leroux

Stat Med 2000 Sep 15-30;19(17-18):2321-32

Department of Biostatistics, Box 357232, University of Washington, Seattle WA 98195, USA.

This paper concerns maximum likelihood estimation for a generalized linear mixed model (GLMM) useful for modelling spatial disease rates. The model allows for log-linear covariate adjustment and local smoothing of rates through estimation of spatially correlated random effects. The covariance structure of the random effects is based on a recently proposed model which parameterizes spatial dependence through the inverse covariance matrix. Read More

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