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

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

SUBMITTER: Zhang B 

PROVIDER: S-EPMC12665160 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature