Premium
Selection models and pattern‐mixture models to analyse longitudinal quality of life data subject to drop‐out
Author(s) -
Michiels Bart,
Molenberghs Geert,
Bijnens Luc,
Vangeneugden Tony,
Thijs Herbert
Publication year - 2002
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.1064
Subject(s) - selection (genetic algorithm) , computer science , drop out , drop (telecommunication) , model selection , mixture model , data set , statistics , artificial intelligence , mathematics , telecommunications , economics , demographic economics
Longitudinally observed quality of life data with large amounts of drop‐out are analysed. First we used the selection modelling framework, frequently used with incomplete studies. An alternative method consists of using pattern‐mixture models. These are also straightforward to implement, but result in a different set of parameters for the measurement and drop‐out mechanisms. Since selection models and pattern‐mixture models are based upon different factorizations of the joint distribution of measurement and drop‐out mechanisms, comparing both models concerning, for example, treatment effect, is a useful form of a sensitivity analysis. Copyright © 2002 John Wiley & Sons, Ltd.