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Generalized linear mixed models for meta‐analysis
Author(s) -
Platt Robert W.,
Leroux Brian G.,
Breslow Norman
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19990330)18:6<643::aid-sim76>3.0.co;2-m
Subject(s) - inference , generalized linear model , covariate , generalized linear mixed model , random effects model , statistics , linear model , mathematics , linear regression , quasi likelihood , mixed model , computer science , meta analysis , count data , poisson distribution , artificial intelligence , medicine
We examine two strategies for meta‐analysis of a series of 2×2 tables with the odds ratio modelled as a linear combination of study level covariates and random effects representing between‐study variation. Penalized quasi‐likelihood (PQL), an approximate inference technique for generalized linear mixed models, and a linear model fitted by weighted least squares to the observed log‐odds ratios are used to estimate regression coefficients and dispersion parameters. Simulation results demonstrate that both methods perform adequate approximate inference under many conditions, but that neither method works well in the presence of highly sparse data. Under certain conditions with small cell frequencies the PQL method provides better inference. Copyright © 1999 John Wiley & Sons, Ltd.