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Interaction Trees with Censored Survival Data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Su Xiaogang & Zhou Tianni & Yan Xin & Fan Juanjuan & Yang Song, 2008. "Interaction Trees with Censored Survival Data," The International Journal of Biostatistics, De Gruyter, vol. 4(1), pages 1-26, January.
    • Handle: RePEc:bpj:ijbist:v:4:y:2008:i:1:n:2
    DOI: 10.2202/1557-4679.1071
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    Citations

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    Cited by:

    1. L. Doove & E. Dusseldorp & K. Deun & I. Mechelen, 2014. "A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 403-425, December.
    2. Cho, Youngjoo & Zhan, Xiang & Ghosh, Debashis, 2022. "Nonlinear predictive directions in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    3. Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
    4. Yaoyao Xu & Menggang Yu & Ying‐Qi Zhao & Quefeng Li & Sijian Wang & Jun Shao, 2015. "Regularized outcome weighted subgroup identification for differential treatment effects," Biometrics, The International Biometric Society, vol. 71(3), pages 645-653, September.
    5. Piyali Basak & Antonio Linero & Debajyoti Sinha & Stuart Lipsitz, 2022. "Semiparametric analysis of clustered interval‐censored survival data using soft Bayesian additive regression trees (SBART)," Biometrics, The International Biometric Society, vol. 78(3), pages 880-893, September.
    6. E. B. Laber & Y. Q. Zhao, 2015. "Tree-based methods for individualized treatment regimes," Biometrika, Biometrika Trust, vol. 102(3), pages 501-514.
    7. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    8. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    9. Rui Zhang & Guoyou Qin & Dongsheng Tu, 2023. "A robust threshold t linear mixed model for subgroup identification using multivariate T distributions," Computational Statistics, Springer, vol. 38(1), pages 299-326, March.

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