We extend the marginalized transition model of Heagerty (2002) to accommodate nonignorable monotone dropout. Using a selection model, weakly identified dropout parameters are held constant and their effects evaluated through sensitivity analysis. For data missing at random (MAR), efficiency of inverse probability of censoring weighted generalized estimating equations (IPCW-GEE) is as low as 40% compared to a likelihood-based marginalized transition model (MTM) with comparable modeling burden. MTM and IPCW-GEE regression parameters both display misspecification bias for MAR and nonignorable missing data, and both reduce bias noticeably by improving model fit
Categorical Data Analysis | Design of Experiments and Sample Surveys | Longitudinal Data Analysis and Time Series | Statistical Models
Kurland, Brenda F. and Heagerty, Patrick J., "Marginalized Transition Models for Longitudinal Binary Data With Ignorable and Nonignorable Dropout" (December 2003). UW Biostatistics Working Paper Series. Working Paper 222.