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Parametric latent class joint model for a longitudinal biomarker and recurrent events
Author(s) -
Han Jun,
Slate Elizabeth H.,
Peña Edsel A.
Publication year - 2007
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.2915
Subject(s) - covariate , event (particle physics) , computer science , parametric statistics , biomarker , population , econometrics , expectation–maximization algorithm , parametric model , class (philosophy) , statistics , artificial intelligence , maximum likelihood , machine learning , mathematics , medicine , environmental health , quantum mechanics , chemistry , biochemistry , physics
A joint model for a longitudinal biomarker and recurrent events is proposed. This general model accommodates the effects of covariates on the biomarker and event processes, the effects of accumulating event occurrences, and effects caused by interventions after each event occurrence. Association between the biomarker and recurrent event processes is captured through a latent class structure, which also serves to handle an underlying heterogeneous population. We use the EM algorithm for maximum likelihood estimation of the model parameters and a penalized likelihood measure to determine the number of latent classes. This joint model is validated by simulation and illustrated with a data set from epileptic seizure study. Copyright © 2007 John Wiley & Sons, Ltd.