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A linear mixed model for predicting a binary event from longitudinal data under random effects misspecification
Author(s) -
Albert Paul S.
Publication year - 2011
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.4405
Subject(s) - random effects model , binary data , event (particle physics) , computer science , binary number , econometrics , statistics , mixed model , gaussian , focus (optics) , estimator , mathematics , medicine , arithmetic , meta analysis , physics , quantum mechanics , optics
The use of longitudinal data for predicting a subsequent binary event is often the focus of diagnostic studies. This is particularly important in obstetrics, where ultrasound measurements taken during fetal development may be useful for predicting various poor pregnancy outcomes. We propose a modeling framework for predicting a binary event from longitudinal measurements where a shared random effect links the two processes together. Under a Gaussian random effects assumption, the approach is simple to implement with standard statistical software. Using asymptotic and simulation results, we show that estimates of predictive accuracy under a Gaussian random effects distribution are robust to severe misspecification of this distribution. However, under some circumstances, estimates of individual risk may be sensitive to severe random effects misspecification. We illustrate the methodology with data from a longitudinal fetal growth study. Copyright © 2011 John Wiley & Sons, Ltd.