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Marginalized transition models for longitudinal binary data with ignorable and non‐ignorable drop‐out
Author(s) -
Kurland Brenda F.,
Heagerty Patrick J.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1850
Subject(s) - binary number , drop (telecommunication) , econometrics , longitudinal data , drop out , binary data , statistics , computer science , mathematics , data mining , demographic economics , economics , telecommunications , arithmetic
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. Copyright © 2004 John Wiley & Sons, Ltd.