Identification of prognostic factors with multivariate survival data

• Published in: Computational statistics & data analysis Vol. 45; no. 4; pp. 813 - 824
• Main Authors: Gao, Feng, Manatunga, Amita K, Chen, Shande
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 10.05.2004; Elsevier Science; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Applications; CART; Exact sciences and technology; Frailty model; Mathematics; Medical sciences; Multivariate analysis; Multivariate survival data; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Sciences and techniques of general use; Statistics; Survival tree; Frailty model; Multivariate survival data; Survival tree; CART; Statistical regression; Survival analysis; Data analysis; Statistical computation; Correlation analysis; Recurrence relation; Classification; Multivariate analysis; Statistical simulation; Survival data
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/S0167-9473(03)00089-6

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.