Marginalized Transition Models for Longitudinal Binary Data With Ignorable and Nonignorable Dropout

Brenda Kurland and Patrick Heagerty
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Brenda Kurland: University of Washington
Patrick Heagerty: University of Washington

No 1054, UW Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: 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

Keywords: nonignorable missing data; longitudinal binary data; marginalized model; misspecification; likelihood (search for similar items in EconPapers)
Date: 2004-09-09
Note: oai:bepress.com:uwbiostat-1054
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Citations: View citations in EconPapers (1)

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