title: A survival tree method for the analysis of discrete event times in clinical and epidemiological studies.
abstract: Survival trees are a popular alternative to parametric survival modeling when there are interactions between the predictor variables or when the aim is to stratify patients into prognostic subgroups. A limitation of classical survival tree methodology is that most algorithms for tree construction are designed for continuous outcome variables. Hence, classical methods might not be appropriate if failure time data are measured on a discrete time scale (as is often the case in longitudinal studies where data are collected, e.g., quarterly or yearly). To address this issue, we develop a method for discrete survival tree construction. The proposed technique is based on the result that the likelihood of a discrete survival model is equivalent to the likelihood of a regression model for binary outcome data. Hence, we modify tree construction methods for binary outcomes such that they result in optimized partitions for the estimation of discrete hazard functions. By applying the proposed method to data from a randomized trial in patients with filarial lymphedema, we demonstrate how discrete survival trees can be used to identify clinically relevant patient groups with similar survival behavior.
authors.affiliation: Institute of Medical Biometry, Informatics and Epidemiology, University of Bonn, Sigmund-Freud-Str. 25, Bonn, 53127, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Schmid
authors.affiliation: Department of Statistics, University of Munich, Ludwigstr. 33, Munich, 80539, Germany.
authors.forename: Helmut
authors.initials: H
authors.lastname: Küchenhoff
authors.affiliation: Institute of Medical Microbiology, Immunology and Parasitology, University of Bonn, Sigmund-Freud-Str. 25, Bonn, 53127, Germany.|German Center for Infection Research (DZIF), Partner Site Bonn-Cologne, Bonn, 53127, Germany.
authors.forename: Achim
authors.initials: A
authors.lastname: Hoerauf
authors.affiliation: Department of Statistics, University of Munich, Ludwigstr. 33, Munich, 80539, Germany.
authors.forename: Gerhard
authors.initials: G
authors.lastname: Tutz
mesh_terms: D000465:Algorithms; D016021:Epidemiologic Studies; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D018570:Risk Assessment / Q000706:statistics & numerical data; D016019:Survival Analysis
keywords: discrete event times; hazard models; recursive partitioning; risk stratification; survival analysis