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Zbl 1112.62312
Xu, Jane; Zeger, Scott L.
Joint analysis of longitudinal data comprising repeated measures and times to events. (English)
[J] J. R. Stat. Soc., Ser. C, Appl. Stat. 50, No. 3, 375-387 (2001). ISSN 0035-9254; ISSN 1467-9876/e

Summary: 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. 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.

MSC 2000:: *62-99 Statistics

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Zentralblatt MATH Berlin [Germany]