A transition model for quality-of-life data with non-ignorable non-monotone missing data.

Abstract:

:In this paper, we consider a full likelihood method to analyze continuous longitudinal responses with non-ignorable non-monotone missing data. We consider a transition probability model for the missingness mechanism. A first-order Markov dependence structure is assumed for both the missingness mechanism and observed data. This process fits the natural data structure in the longitudinal framework. Our main interest is in estimating the parameters of the marginal model and evaluating the missing-at-random assumption in the Effects of Public Information Study, a cancer-related study recently conducted at the University of Pennsylvania. We also present a simulation study to assess the performance of the model.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Liao K,Freres DR,Troxel AB

doi

10.1002/sim.5359

subject

Has Abstract

pub_date

2012-12-10 00:00:00

pages

3444-66

issue

28

eissn

0277-6715

issn

1097-0258

journal_volume

31

pub_type

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