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Marginal methods for clustered longitudinal binary data with incomplete covariates.

ABSTRACT: Many analyses for incomplete longitudinal data are directed to examining the impact of covariates on the marginal mean responses. We consider the setting in which longitudinal responses are collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean and association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters when covariates are missing at random. Empirical studies demonstrate that estimators from the proposed method have negligible finite sample biases in moderate samples. An application to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) demonstrates the utility of the proposed method.

SUBMITTER: Chen B

PROVIDER: S-EPMC3690662 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Marginal methods for clustered longitudinal binary data with incomplete covariates.

Chen Baojiang B Yi Grace Y GY Cook Richard J RJ Zhou Xiao-Hua XH

Journal of statistical planning and inference 20121001 10

Many analyses for incomplete longitudinal data are directed to examining the impact of covariates on the marginal mean responses. We consider the setting in which longitudinal responses are collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean and association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association pa ...[more]

PMID: 23805025

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