Modeling of correlated data with informative cluster sizes: An evaluation of joint modeling and within-cluster resampling approaches. (August 2017)

• Record Type: Journal Article
• Authors: Zhang, Bo; Liu, Wei; Zhang, Zhiwei; Qu, Yanping; Chen, Zhen; Albert, Paul S
• Other Names: Davidian Marie guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 26:Number 4(2017)
• Page Start: 1881
• Page End: 1895
• Publication Date: 2017-08
• Subjects: Joint random effects model -- nonignorable cluster size -- hypothesis testing -- power -- within-cluster resampling; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280215592268
• Languages: English
• ISSNs: 0962-2802

Abstract

Joint modeling and within-cluster resampling are two approaches that are used for analyzing correlated data with informative cluster sizes. Motivated by a developmental toxicity study, we examined the performances and validity of these two approaches in testing covariate effects in generalized linear mixed-effects models. We show that the joint modeling approach is robust to the misspecification of cluster size models in terms of Type I and Type II errors when the corresponding covariates are not included in the random effects structure; otherwise, statistical tests may be affected. We also evaluate the performance of the within-cluster resampling procedure and thoroughly investigate the validity of it in modeling correlated data with informative cluster sizes. We show that within-cluster resampling is a valid alternative to joint modeling for cluster-specific covariates, but it is invalid for time-dependent covariates. The two methods are applied to a developmental toxicity study that investigated the effect of exposure to diethylene glycol dimethyl ether.