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Proportional hazards model with a change point for clustered event data

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  • Yu Deng
  • Donglin Zeng
  • Jinying Zhao
  • Jianwen Cai

Abstract

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  • Yu Deng & Donglin Zeng & Jinying Zhao & Jianwen Cai, 2017. "Proportional hazards model with a change point for clustered event data," Biometrics, The International Biometric Society, vol. 73(3), pages 835-845, September.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:3:p:835-845
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    File URL: http://hdl.handle.net/10.1111/biom.12655
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    References listed on IDEAS

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    1. Bibhas Chakraborty & Eric B. Laber & Yingqi Zhao, 2013. "Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m-Out-of-n Bootstrap Scheme," Biometrics, The International Biometric Society, vol. 69(3), pages 714-723, September.
    2. Jason Abrevaya & Jian Huang, 2005. "On the Bootstrap of the Maximum Score Estimator," Econometrica, Econometric Society, vol. 73(4), pages 1175-1204, July.
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    Cited by:

    1. Qiyue Huang & Xiaoyi Wang & Xingwei Tong & Meng Li & Jianguo Sun, 2026. "Estimation of the change-point Cox proportional hazards model based on case-I interval-censored data," Computational Statistics, Springer, vol. 41(1), pages 1-22, January.
    2. Qiyue Huang & Anyin Feng & Qiang Wu & Xingwei Tong, 2026. "Deep learning for the change-point Cox model with current status data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 32(1), pages 1-22, March.
    3. Junyao Ren & Shishun Zhao & Dianliang Deng & Tianshu You & Hui Huang, 2025. "Semiparametric Transformation Models with a Change Point for Interval-Censored Failure Time Data," Mathematics, MDPI, vol. 13(15), pages 1-17, August.

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