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Multiple outputation for the analysis of longitudinal data subject to irregular observation
Author(s) -
Pullenayegum Eleanor M.
Publication year - 2015
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.6829
Subject(s) - longitudinal data , computer science , subject (documents) , observational study , range (aeronautics) , cluster (spacecraft) , feature (linguistics) , statistics , econometrics , data mining , mathematics , linguistics , materials science , philosophy , library science , composite material , programming language
Observational cohort studies often feature longitudinal data subject to irregular observation. Moreover, the timings of observations may be associated with the underlying disease process and must thus be accounted for when analysing the data. This paper suggests that multiple outputation, which consists of repeatedly discarding excess observations, may be a helpful way of approaching the problem. Multiple outputation was designed for clustered data where observations within a cluster are exchangeable; an adaptation for longitudinal data subject to irregular observation is proposed. We show how multiple outputation can be used to expand the range of models that can be fitted to irregular longitudinal data. Copyright © 2015 John Wiley & Sons, Ltd.