Shen C and Weissfeld L. A copula model for repeated measurements with non-ignorable non-monotone missing outcome. Statistics in Medicine, 25, 2427-2440.

ABSTRACT:

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.