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A Semiparametric Likelihood Approach to Joint Modeling of Longitudinal and Time‐to‐Event Data
Author(s) -
Song Xiao,
Davidian Marie,
Tsiatis Anastasios A.
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00742.x
Subject(s) - covariate , econometrics , random effects model , event (particle physics) , parametric statistics , computer science , proportional hazards model , inference , semiparametric model , statistics , semiparametric regression , mixed model , normality , hazard , parametric model , mathematics , artificial intelligence , medicine , meta analysis , physics , chemistry , organic chemistry , quantum mechanics
Summary. Joint models for a time‐to‐event (e.g., survival) and a longitudinal response have generated considerable recent interest. The longitudinal data are assumed to follow a mixed effects model, and a proportional hazards model depending on the longitudinal random effects and other covariates is assumed for the survival endpoint. Interest may focus on inference on the longitudinal data process, which is informatively censored, or on the hazard relationship. Several methods for fitting such models have been proposed, most requiring a parametric distributional assumption (normality) on the random effects. A natural concern is sensitivity to violation of this assumption; moreover, a restrictive distributional assumption may obscure key features in the data. We investigate these issues through our proposal of a likelihood‐based approach that requires only the assumption that the random effects have a smooth density. Implementation via the EM algorithm is described, and performance and the benefits for uncovering noteworthy features are illustrated by application to data from an HIV clinical trial and by simulation.