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Semiparametric estimation in generalized linear mixed models with auxiliary covariates: A pairwise likelihood approach
Author(s) -
Liu Li,
Xiang Liming
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12208
Subject(s) - covariate , estimator , pairwise comparison , mathematics , random effects model , statistics , inference , empirical likelihood , generalized linear model , asymptotic distribution , nuisance parameter , data set , estimating equations , econometrics , computer science , artificial intelligence , medicine , meta analysis
Summary Auxiliary covariates are often encountered in biomedical research settings where the primary exposure variable is measured only for a subgroup of study subjects. This article is concerned with generalized linear mixed models in the presence of auxiliary covariate information for clustered data. We propose a novel semiparametric estimation method based on a pairwise likelihood function and develop an estimating equation‐based inference procedure by treating both the error structure and random effects as nuisance parameters. This method is robust against misspecification of either error structure or random‐effects distribution and allows for dependence between random effects and covariates. We show that the resulting estimators are consistent and asymptotically normal. Extensive simulation studies evaluate the finite sample performance of the proposed estimators and demonstrate their advantage over the validation set based method and the existing method. We illustrate the method with two real data examples.