Joint analysis of longitudinal data comprising repeated measures and times to events

Jane Xu and Scott L. Zeger

Journal of the Royal Statistical Society Series C, 2001, vol. 50, issue 3, 375-387

Abstract: In biomedical and public health research, both repeated measures of biomarkers Y as well as times T to key clinical events are often collected for a subject. The scientific question is how the distribution of the responses [T, Y|X] changes with covariates X. [T|X] may be the focus of the estimation where Y can be used as a surrogate for T. Alternatively, T may be the time to drop‐out in a study in which [Y|X] is the target for estimation. Also, the focus of a study might be on the effects of covariates X on both T and Y or on some underlying latent variable which is thought to be manifested in the observable outcomes. In this paper, we present a general model for the joint analysis of [T, Y|X] and apply the model to estimate [T|X] and other related functionals by using the relevant information in both T and Y. We adopt a latent variable formulation like that of Fawcett and Thomas and use it to estimate several quantities of clinical relevance to determine the efficacy of a treatment in a clinical trial setting. We use a Markov chain Monte Carlo algorithm to estimate the model's parameters. We illustrate the methodology with an analysis of data from a clinical trial comparing risperidone with a placebo for the treatment of schizophrenia.

Date: 2001
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