title: 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 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.
authors.affiliation: 1 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Zhang
authors.affiliation: 2 Department of Mathematics, Harbin Institute of Technology, Harbin, P.R. China.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.affiliation: 1 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, USA.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhang
authors.affiliation: 1 Division of Biostatistics, Office of Surveillance and Biometrics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, USA.
authors.forename: Yanping
authors.initials: Y
authors.lastname: Qu
authors.affiliation: 3 Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, USA.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Chen
authors.affiliation: 3 Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, USA.
authors.forename: Paul S
authors.initials: PS
authors.lastname: Albert
mesh_terms: D000818:Animals; D016000:Cluster Analysis; D005026:Ethylene Glycols / Q000633:toxicity; D005260:Female; D016014:Linear Models; D008738:Methyl Ethers / Q000633:toxicity; D011817:Rabbits; D015203:Reproducibility of Results; D018675:Toxicity Tests
chemical_list: D005026:Ethylene Glycols; D008738:Methyl Ethers; C007391:diglyme
keywords: Joint random effects model; hypothesis testing; nonignorable cluster size; power; within-cluster resampling