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 Medical Research Methodology
Ke JuChang Xu

Abstract

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. However, the performance of GLMM via these estimation methods is unclear in meta-analysis with zero events. A simulation study was conducted to compare the performance. We fitted five random-effects GLMMs and estimated the results through the LA, PQL and AGHQ methods, respectively. Each scenario conducted 20,000 simulation iterations. The data from Cochrane Database of Systematic Reviews were collected to form the simulation settings. The estimation methods were compared in terms of the convergence rate, bias, mean square error, and coverage probability. Our results suggested that when the total events were insufficient in either of the arms, the GLMMs did not show good point estimation to pool studies of rare events. The AGHQ method did not show better properties than the LA estimation in terms of convergence rate, bias, c...Continue Reading

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Citations

Feb 17, 2021·Journal of Clinical Epidemiology·Chang XuSunita Vohra
Apr 4, 2021·International Journal of Environmental Research and Public Health·Fahad M Al AmerLifeng Lin
Jul 5, 2021·Military Medical Research·Jia-Jin WeiTie-Jun Tong
Aug 13, 2021·Osteoporosis International : a Journal Established As Result of Cooperation Between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·J ZhaoY Lin
Sep 11, 2021·Journal of General Internal Medicine·Lifeng LinHaitao Chu

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Software Mentioned

Stata14
R
Excel
lme4
GLMMadaptive

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