Abstract
We propose a marginalized joint-modeling approach for marginal inference on the association between longitudinal responses and covariates when longitudinal measurements are subject to informative dropouts. The proposed model is motivated by the idea of linking longitudinal responses and dropout times by latent variables while focusing on marginal inferences. We develop a simple inference procedure based on a series of estimating equations, and the resulting estimators are consistent and asymptotically normal with a sandwich-type covariance matrix ready to be estimated by the usual plug-in rule. The performance of our approach is evaluated through simulations and illustrated with a renal disease data application.
Highlights
Longitudinal studies often encounter data attrition because subjects drop out before the designated study end
We considered several combinations to specify the dependence between longitudinal outcomes and informative dropout times
We propose a semiparametric marginalized model for marginal inference of the relationship between longitudinal responses and covariates in the presence of informative dropouts
Summary
Longitudinal studies often encounter data attrition because subjects drop out before the designated study end. A widely used modeling strategy for longitudinal data with informative dropouts is to specify their joint distribution via shared or correlated latent variables. Under such model assumptions, the longitudinal parameters have a conditional, subject-specific interpretation e.g., 9–11. In the presence of informative dropouts, the class of selection models that were originally proposed to adjust selection bias in econometrics have been widely used for the marginal analysis of longitudinal data 20–22. The marginalized transition model and marginalized pattern-mixture model were proposed for binary longitudinal data with finite nonignorable nonresponse patterns These marginalized approaches provide a powerful tool for studying the marginal association between longitudinal outcomes and covariates while incorporating nonignorable nonresponses.
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