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Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study

Author

Listed:
  • Qingxia Chen

    (Vanderbilt University Medical Center)

  • Fan Zhang

    (Pfizer Inc.)

  • Ming-Hui Chen

    (University of Connecticut)

  • Xiuyu Julie Cong

    (Everest Medicines)

Abstract

Treatment switching frequently occurs in clinical trials due to ethical reasons. Intent-to-treat analysis without adjusting for switching yields biased and inefficient estimates of the treatment effects. In this paper, we propose a class of semiparametric semi-competing risks transition survival models to accommodate two-way time-varying switching. Theoretical properties of the proposed method are examined. An efficient expectation–maximization algorithm is derived to obtain maximum likelihood estimates and model diagnostic tools. Existing software is used to implement the algorithm. Simulation studies are conducted to demonstrate the validity of the model. The proposed method is further applied to data from a clinical trial with patients having recurrent or metastatic squamous-cell carcinoma of head and neck.

Suggested Citation

  • Qingxia Chen & Fan Zhang & Ming-Hui Chen & Xiuyu Julie Cong, 2020. "Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 685-707, October.
    • Handle: RePEc:spr:lifeda:v:26:y:2020:i:4:d:10.1007_s10985-020-09495-0
    DOI: 10.1007/s10985-020-09495-0
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    References listed on IDEAS

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    Cited by:

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