On a Bayesian multivariate survival tree approach based on three frailty models.

Abstract

This study compared the performance of the Bayesian multivariate survival tree approach constructed from extended Cox proportional hazard with gamma frailty term, and two shared gamma frailty models with exponential and Weibull baseline hazard function, respectively. A simulation study was applied to evaluate the impact of the baseline hazard function, number of clusters (200, 500, 1000), cluster size (5, 10, 20), and right censoring rate (10%, 50%, 80%) on the performance of classification. We generated 90 clustered survival datasets having correlated failure times and 50 covariates at cluster level and at individual level. Each dataset was resampling 1000 times by selecting clusters at random 70% as training datasets and the rest 30% as the test datasets. The performance of a Bayesian multivariate survival tree approach based on shared gamma frailty models with Weibull distribution provided the highest accuracy. All three models, the accuracy tended to increase with an increase in the cluster size and the number of clusters. The accuracy decreased monotonically with increasing the percentage of censoring rate. In conclusion, the use of the Bayesian multivariate survival tree approach constructed from the shared gamma frailty with baseline hazard function as Weibull distribution was recommended.