Identification of prognostic factors with multivariate survival data

Feng Gao, Amita K. Manatunga, Shande Chen

Research output: Contribution to journalArticle

17 Scopus citations

Abstract

Multivariate survival data arises when subjects in the same group are related to each other or when there are multiple recurrences of the disease in the same subject. A common goal of survival analysis is to relate the outcome (time to event) to a set of covariates. In this paper, we focus on prognostic classification for multivariate survival data where identifying subgroups of patients with similar prognosis is of interest. We propose a computationally feasible method to identify prognostic groups with the widely used Classification and Regression Trees (CART) algorithm. The proposed method extends CART algorithm to multivariate survival data by introducing a gamma frailty to account for dependence among correlated events. The method is applied to a catheter infection data, and the performance of the method is also investigated by several simulation studies.

Original languageEnglish
Pages (from-to)813-824
Number of pages12
JournalComputational Statistics and Data Analysis
Volume45
Issue number4
DOIs
StatePublished - 10 May 2004

Keywords

  • CART
  • Frailty model
  • Multivariate survival data
  • Survival tree

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