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Testing for the presence of multiple sources of informative dropout in longitudinal data
Author(s) -
Crawford S. B.,
Hanfelt J. J.
Publication year - 2008
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.3287
Subject(s) - dropout (neural networks) , homogeneity (statistics) , score test , cluster analysis , computer science , longitudinal data , random effects model , econometrics , statistics , artificial intelligence , psychology , statistical hypothesis testing , machine learning , mathematics , data mining , medicine , meta analysis
Longitudinal studies tracking the rate of change are subject to patient dropout. This dropout process might not only be informative but also heterogeneous in the sense that different causes might contribute to multiple patterns of informative dropout. We propose a random‐effects approach to test for homogeneity of informative dropout that accommodates the realistic situation where reasons for dropout are not fully understood, or perhaps are even entirely unknown. The proposed score test is robust in that it does not depend on the underlying distribution of the informative dropout random effects. The test allows for an additional level of clustering among participating subjects, as might be found in a family study, provided the informative dropout random effects have a known correlation structure. Copyright © 2008 John Wiley & Sons, Ltd.