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A copula model for repeated measurements with non‐ignorable non‐monotone missing outcome
Author(s) -
Shen Changyu,
Weissfeld Lisa
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2355
Subject(s) - missing data , copula (linguistics) , covariate , statistics , econometrics , mathematics , estimating equations , model selection , logistic regression , computer science , maximum likelihood
A normal copula‐based selection model is proposed for continuous longitudinal data with a non‐ignorable non‐monotone missing‐data process. The normal copula is used to combine the distribution of the outcome of interest and that of the missing‐data indicators given the covariates. Parameters in the model are estimated by a pseudo‐likelihood method. We first use the GEE with a logistic link to estimate the parameters associated with the marginal distribution of the missing‐data indicator given the covariates, assuming that covariates are always observed. Then we estimate other parameters by inserting the estimates from the first step into the full likelihood function. A simulation study is conducted to assess the robustness of the assumed model under different missing‐data processes. The proposed method is then applied to one example from a community cohort study to demonstrate its capability to reduce bias. Copyright © 2005 John Wiley & Sons, Ltd.