Developing Multivariate Survival Trees with a Proportional Hazards Structure

10.6339/JDS.2006.04(3).284

Feng Gao；Amita K. Manatunga；Shande Chen

CART ； multivariate survival data ； survival tree ； WLW model

Journal of Data Science

4卷3期（2006 / 07 / 01）

343 - 356

英文

In this paper, a tree-structured method is proposed to extend Classification and Regression Trees (CART) algorithm to multivariate survival data, assuming a proportional hazard structure in the whole tree. The method works on the marginal survivor distributions and uses a sandwich estimator of variance to account for the association between survival times. The Wald-test statistics is defined as the splitting rule and the survival trees are developed by maximizing between-node separation. The proposed method intends to classify patients into subgroups with distinctively different prognosis. However, unlike the conventional tree-growing algorithms which work on a subset of data at every partition, the proposed method deals with the whole data set and searches the global optimal split at each partition. The method is applied to a prostate cancer data and its performance is also evaluated by several simulation studies.