A comparison of frailty models for multivariate survival data

Abstract

This paper reviews some of the main approaches to the analysis of multivariate censored survival data. Such data typically have correlated failure times. The correlation can be a consequence of the observational 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 estimating models with these frailty effects in censored bivariate survival 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 heart patients.

Robust frailty modelling using non-proportional hazards models

“…In particular, the robustness of the C-N model in relation to β 1 confirms the simulation results of Pickles and Crouchley (1995), Ng and Cook (2000) and Lee (2003, 2005). Table 4 shows the results for the high censoring simulation (73%).…”

Resampling Plans for Frailty Models

“…This means that there is robustness in terms of estimation of the fixed effects. This is in agreement with results in, e.g., Pickles and Crouchley (1995).…”

Choice of conditional models in bivariate survival

“…The exact choice of frailty may not be important; Pickles and Crouchley [9] found that ÿtted mixture distributions were robust to choice of mixture (frailty) distribution.…”