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Response‐adaptive randomization for survival trials: the parametric approach

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  • Lanju Zhang
  • William F. Rosenberger

Abstract

Summary. Few references deal with response‐adaptive randomization procedures for survival outcomes and those that do either dichotomize the outcomes or use a non‐parametric approach. In this paper, the optimal allocation approach and a parametric response‐adaptive randomization procedure are used under exponential and Weibull distributions. The optimal allocation proportions are derived for both distributions and the doubly adaptive biased coin design is applied to target the optimal allocations. The asymptotic variance of the procedure is obtained for the exponential distribution. The effect of intrinsic delay of survival outcomes is treated. These findings are based on rigorous theory but are also verified by simulation. It is shown that using a doubly adaptive biased coin design to target the optimal allocation proportion results in more patients being randomized to the better performing treatment without loss of power. We illustrate our procedure by redesigning a clinical trial.

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  • Lanju Zhang & William F. Rosenberger, 2007. "Response‐adaptive randomization for survival trials: the parametric approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(2), pages 153-165, March.
    • Handle: RePEc:bla:jorssc:v:56:y:2007:i:2:p:153-165
    DOI: 10.1111/j.1467-9876.2007.00571.x
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    Cited by:

    1. Igor G. Pospelov & Stanislav A. Radionov, 2015. "Optimal Dividend Policy When Cash Surplus Follows The Telegraph Process," HSE Working papers WP BRP 48/FE/2015, National Research University Higher School of Economics.
    2. Guosheng Yin & Nan Chen & J. Jack Lee, 2018. "Bayesian Adaptive Randomization and Trial Monitoring with Predictive Probability for Time-to-Event Endpoint," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 420-438, August.
    3. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 297-310.
    4. James E. Barrett, 2016. "Information-adaptive clinical trials: a selective recruitment design," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 797-808, November.
    5. Williamson, S. Faye & Jacko, Peter & Jaki, Thomas, 2022. "Generalisations of a Bayesian decision-theoretic randomisation procedure and the impact of delayed responses," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    6. Atkinson, Anthony C. & Biswas, Atanu, 2017. "Optimal response and covariate-adaptive biased-coin designs for clinical trials with continuous multivariate or longitudinal responses," LSE Research Online Documents on Economics 66761, London School of Economics and Political Science, LSE Library.
    7. Yuan Ji & B. Nebiyou Bekele, 2009. "Adaptive Randomization for Multiarm Comparative Clinical Trials Based on Joint Efficacy/Toxicity Outcomes," Biometrics, The International Biometric Society, vol. 65(3), pages 876-884, September.

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