Interaction Trees with Censored Survival Data

Su Xiaogang, Zhou Tianni, Yan Xin, Fan Juanjuan and Yang Song
Additional contact information
Su Xiaogang: University of Central Florida
Zhou Tianni: University of Southern California
Yan Xin: University of Missouri - Kansas City
Fan Juanjuan: San Diego State University
Yang Song: National Institutes of Health

The International Journal of Biostatistics, 2008, vol. 4, issue 1, 28

Abstract: We propose an interaction tree (IT) procedure to optimize the subgroup analysis in comparative studies that involve censored survival times. The proposed method recursively partitions the data into two subsets that show the greatest interaction with the treatment, which results in a number of objectively defined subgroups: in some of them the treatment effect is prominent while in others the treatment may have a negligible or even negative effect. The resultant tree structure can be used to explore the overall interaction between treatment and other covariates and help identify and describe possible target populations on which an experimental treatment demonstrates desired efficacy. We follow the standard CART (Breiman, et al., 1984) methodology to develop the interaction tree structure. Variable importance information is extracted via random forests of interaction trees. Both simulated experiments and an analysis of the primary billiary cirrhosis (PBC) data are provided for evaluation and illustration of the proposed procedure.

Keywords: CART; censored survival times; random forests; subgroup analysis (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (9)

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DOI: 10.2202/1557-4679.1071

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