Mixed-effect Models for Truncated Longitudinal Outcomes with Nonignorable Missing Data

10.6339/JDS.2009.07(1).418

Su-Juan Gao；Rodolphe Thiébaut

Longitudinal data ； mixed-effects model ； nonignorable missing ； truncation

Journal of Data Science

7卷1期（2009 / 01 / 01）

13 - 25

英文

Mixed effects models are often used for estimating fixed effects and variance components in continuous longitudinal outcomes. An EM based estimation approach for mixed effects models when the outcomes are truncated was proposed by Hughes (1999). We consider the situation when the longitudinal outcomes are also subject to non-ignorable missing in addition to truncation. A shared random effect parameter model is presented where the missing data mechanism depends on the random effects used to model the longitudinal outcomes. Data from the Indianapolis-Ibadan dementia project is used to illustrate the proposed approach.