Developing Multivariate Survival Trees with a Proportional Hazards Structure

Feng Gao ； Amita K. Manatunga ； Shande Chen

《Journal of Data Science》 4卷3期 (2006/07) Pp. 343-356

https://doi.org/10.6339/JDS.2006.04(3).284

並列摘要

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.

並列關鍵字

CART ； multivariate survival data ； survival tree ； WLW model