Mixture survival trees for cancer risk classification

Beilin Jia (), Donglin Zeng, Jason J. Z. Liao, Guanghan F. Liu, Xianming Tan, Guoqing Diao and Joseph G. Ibrahim
Additional contact information
Beilin Jia: University of North Carolina at Chapel Hill
Donglin Zeng: University of North Carolina at Chapel Hill
Jason J. Z. Liao: Incyte Corporation
Guanghan F. Liu: Merck & Co., Inc
Xianming Tan: University of North Carolina at Chapel Hill
Guoqing Diao: The George Washington University
Joseph G. Ibrahim: University of North Carolina at Chapel Hill

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2022, vol. 28, issue 3, No 2, 356-379

Abstract: Abstract In oncology studies, it is important to understand and characterize disease heterogeneity among patients so that patients can be classified into different risk groups and one can identify high-risk patients at the right time. This information can then be used to identify a more homogeneous patient population for developing precision medicine. In this paper, we propose a mixture survival tree approach for direct risk classification. We assume that the patients can be classified into a pre-specified number of risk groups, where each group has distinct survival profile. Our proposed tree-based methods are devised to estimate latent group membership using an EM algorithm. The observed data log-likelihood function is used as the splitting criterion in recursive partitioning. The finite sample performance is evaluated by extensive simulation studies and the proposed method is illustrated by a case study in breast cancer.

Keywords: Censoring; Latent model; Mixture distribution; Risk classification; Tree-based method (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10985-022-09552-w

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