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A transition model for quality‐of‐life data with non‐ignorable non‐monotone missing data
Author(s) -
Liao Kaijun,
Freres Derek R.,
Troxel Andrea B.
Publication year - 2012
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.5359
Subject(s) - missing data , computer science , monotone polygon , econometrics , quality (philosophy) , statistics , data quality , mathematics , operations management , economics , geometry , metric (unit) , philosophy , epistemology
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. Copyright © 2012 John Wiley & Sons, Ltd.