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Estimating treatment effects with treatment switching via semicompeting risks models: an application to a colorectal cancer study

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  • Donglin Zeng
  • Qingxia Chen
  • Ming-Hui Chen
  • Joseph G. Ibrahim

Abstract

Treatment switching is a frequent occurrence in clinical trials, where, during the course of the trial, patients who fail on the control treatment may change to the experimental treatment. Analysing the data without accounting for switching yields highly biased and inefficient estimates of the treatment effect. In this paper, we propose a novel class of semiparametric semicompeting risks transition survival models to accommodate treatment switches. Theoretical properties of the proposed model are examined and an efficient expectation-maximization algorithm is derived for obtaining the maximum likelihood estimates. Simulation studies are conducted to demonstrate the superiority of the model compared with the intent-to-treat analysis and other methods proposed in the literature. The proposed method is applied to data from a colorectal cancer clinical trial. Copyright 2012, Oxford University Press.

Suggested Citation

  • Donglin Zeng & Qingxia Chen & Ming-Hui Chen & Joseph G. Ibrahim, 2012. "Estimating treatment effects with treatment switching via semicompeting risks models: an application to a colorectal cancer study," Biometrika, Biometrika Trust, vol. 99(1), pages 167-184.
    • Handle: RePEc:oup:biomet:v:99:y:2012:i:1:p:167-184
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    File URL: http://hdl.handle.net/10.1093/biomet/asr062
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    Cited by:

    1. 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.
    2. Fei Jiang & Sebastien Haneuse, 2017. "A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 112-129, March.
    3. Matthias Brueckner & Andrew Titman & Thomas Jaki, 2019. "Instrumental variable estimation in semi‐parametric additive hazards models," Biometrics, The International Biometric Society, vol. 75(1), pages 110-120, March.
    4. Mário de Castro & Ming‐Hui Chen & Yuanye Zhang & Anthony V. D'Amico, 2020. "A Bayesian multi‐risks survival (MRS) model in the presence of double censorings," Biometrics, The International Biometric Society, vol. 76(4), pages 1297-1309, December.
    5. Mário de Castro & Ming‐Hui Chen & Yuanye Zhang, 2015. "Bayesian path specific frailty models for multi‐state survival data with applications," Biometrics, The International Biometric Society, vol. 71(3), pages 760-771, September.

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