JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1126 10.6339/24-JDS1126 Statistical Data Science Precision Medicine: Interaction Survival Tree for Recurrent Event Data YangYushan1 PereraChamila2 MillerPhilip3 SuXiaogang4 LiuLeilei.liu@wustl.edu3∗ Institute for Health and Equity, Medical College of Wisconsin, Milwaukee, WI, USA Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX, USA Corresponding author. Email: lei.liu@wustl.edu. 202417420242222983131820231532024 2024 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2024 Open access article under the CC BY license.

In randomized controlled trials, individual subjects experiencing recurrent events may display heterogeneous treatment effects. That is, certain subjects might experience beneficial effects, while others might observe negligible improvements or even encounter detrimental effects. To identify subgroups with heterogeneous treatment effects, an interaction survival tree approach is developed in this paper. The Classification and Regression Tree (CART) methodology (Breiman et al., 1984) is inherited to recursively partition the data into subsets that show the greatest interaction with the treatment. The heterogeneity of treatment effects is assessed through Cox’s proportional hazards model, with a frailty term to account for the correlation among recurrent events on each subject. A simulation study is conducted for evaluating the performance of the proposed method. Additionally, the method is applied to identify subgroups from a randomized, double-blind, placebo-controlled study for chronic granulomatous disease. R implementation code is publicly available on GitHub at the following URL: https://github.com/xgsu/IT-Frailty.

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