A COMPARISON OF FRAILTY MODELS FOR MULTIVARIATE SURVIVAL-DATA

This paper reviews some of the main approaches to the analysis of mult ivariate censored survival data. Such data typically have correlated f ailure times. The correlation can be a consequence of the observationa l design, for example with clustered sampling and matching, or it can be a focus of interest as in genetic studies, longitudinal studies of recurrent events and other studies involving multiple measurements. We assume that the correlation between the failure or survival times can be accounted for by fixed or random frailty effects. We then compare the performance of conditional and mixture likelihood approaches to es timating models with these frailty effects in censored bivariate survi val data. We find that the mixture methods are surprisingly robust to misspecification of the frailty distribution. The paper also contains an illustrative example on the times to onset of chest pain brought on by three endurance exercise tests during a drug treatment trial of he art patients.