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

• Published in: Applied statistics Vol. 50; no. 3; pp. 375 - 387
• Main Authors: Xu, Jane, Zeger, Scott L.
• Format: Journal Article
• Language: English
• Published: Oxford, UK and Boston, USA Blackwell Publishers Ltd 2001; Blackwell Publishers; Blackwell; Royal Statistical Society
• Series: Journal of the Royal Statistical Society Series C
• Subjects: AIDS; Applications; Biological markers; Biometrics; Clinical trials; Estimation; Exact sciences and technology; Informative drop-out; Latent variable; Longitudinal data; Longitudinal data analysis; Markov chain Monte Carlo methods; Markovian processes; Mathematical economics; Mathematics; Medical research; Medical sciences; Modeling; Monte Carlo simulation; Multivariate analysis; Parametric models; Placebos; Probability and statistics; Regression; Regression analysis; Schizophrenia; Sciences and techniques of general use; Statistics; Surrogate end point; Survival analysis; Symptoms; Monte Carlo method; Data analysis; Treatment efficiency; Schizophrenia; Longitudinal data; Covariate; Joint distribution; Algorithm; Placebo effect; Markov chain; Functional; Distribution function; Repeated measurement; Clinical trial; Risperidone; Public health
• ISSN: 0035-9254; 1467-9876
• DOI: 10.1111/1467-9876.00241

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.