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

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

Abstract 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...

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Bibliographic Details
Main Authors: Ke Ju, Lifeng Lin, Haitao Chu, Liang-Liang Cheng, Chang Xu
Format: Article
Language:English
Published: BMC 2020-06-01
Series:BMC Medical Research Methodology
Subjects:
Rare events
Meta-analysis
One-stage approach
Both-arm zero events
Online Access:http://link.springer.com/article/10.1186/s12874-020-01035-6

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http://link.springer.com/article/10.1186/s12874-020-01035-6

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