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Published on: September 17, 2019
Marginalized transition models for longitudinal binary data with ignorable and non-ignorable drop-out
Brenda F Kurland1, Patrick J Heagerty
1National Alzheimer's Coordinating Center, University of Washington, Department of Epidemiology, 4311 11th Ave NE #300, Seattle, WA 98105, USA. kurland@u.washington.edu
Abstract:
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.
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