Marginalized transition models for longitudinal binary data with ignorable and non-ignorable drop-out.
We extend the marginalized transition model of Heagerty to accommodate non-ignorable monotone drop-out. Using a selection model, weakly identified drop-out 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 per cent compared to a likelihood-based marginalized transition model (MTM) with comparable modelling burden. MTM and IPCW-GEE regression parameters both display misspecification bias for MAR and non-ignorable missing data, and both reduce bias noticeably by improving model fit.
National Alzheimer's Coordinating Center, University of Washington, Department of Epidemiology, 4311 11th Ave NE #300, Seattle, WA 98105, USA. kurland@u.washington.edu
Brenda F
BF
Kurland
Patrick J
PJ
Heagerty
D014150:Antipsychotic Agents / Q000627:therapeutic use; D003198:Computer Simulation; D006220:Haloperidol / Q000627:therapeutic use; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D018967:Risperidone / Q000627:therapeutic use; D012559:Schizophrenia / Q000188:drug therapy
D014150:Antipsychotic Agents; D006220:Haloperidol; D018967:Risperidone