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A Bayesian adaptive design for clinical trials in rare diseases

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  • Williamson, S. Faye
  • Jacko, Peter
  • Villar, Sofía S.
  • Jaki, Thomas

Abstract

Development of treatments for rare diseases is challenging due to the limited number of patients available for participation. Learning about treatment effectiveness with a view to treat patients in the larger outside population, as in the traditional fixed randomised design, may not be a plausible goal. An alternative goal is to treat the patients within the trial as effectively as possible. Using the framework of finite-horizon Markov decision processes and dynamic programming (DP), a novel randomised response-adaptive design is proposed which maximises the total number of patient successes in the trial and penalises if a minimum number of patients are not recruited to each treatment arm. Several performance measures of the proposed design are evaluated and compared to alternative designs through extensive simulation studies using a recently published trial as motivation. For simplicity, a two-armed trial with binary endpoints and immediate responses is considered. Simulation results for the proposed design show that: (i) the percentage of patients allocated to the superior arm is much higher than in the traditional fixed randomised design; (ii) relative to the optimal DP design, the power is largely improved upon and (iii) it exhibits only a very small bias and mean squared error of the treatment effect estimator. Furthermore, this design is fully randomised which is an advantage from a practical point of view because it protects the trial against various sources of bias. As such, the proposed design addresses some of the key issues that have been suggested as preventing so-called bandit models from being implemented in clinical practice.

Suggested Citation

  • Williamson, S. Faye & Jacko, Peter & Villar, Sofía S. & Jaki, Thomas, 2017. "A Bayesian adaptive design for clinical trials in rare diseases," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 136-153.
  • Handle: RePEc:eee:csdana:v:113:y:2017:i:c:p:136-153
    DOI: 10.1016/j.csda.2016.09.006
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    References listed on IDEAS

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    1. Yi Cheng & Donald A. Berry, 2007. "Optimal adaptive randomized designs for clinical trials," Biometrika, Biometrika Trust, vol. 94(3), pages 673-689.
    2. repec:bla:biomet:v:71:y:2015:i:4:p:969-978 is not listed on IDEAS
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    Cited by:

    1. Jeffrey A. Mills & Gary Cornwall & Beau A. Sauley & Jeffrey R. Strawn, 2018. "Improving the Analysis of Randomized Controlled Trials: a Posterior Simulation Approach," BEA Working Papers 0157, Bureau of Economic Analysis.
    2. Kasianova, Ksenia & Kelbert, Mark & Mozgunov, Pavel, 2021. "Response adaptive designs for Phase II trials with binary endpoint based on context-dependent information measures," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    3. Pavel Mozgunov & Thomas Jaki, 2020. "An information theoretic approach for selecting arms in clinical trials," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1223-1247, December.
    4. Andres Alban & Stephen E. Chick & Martin Forster, 2023. "Value-Based Clinical Trials: Selecting Recruitment Rates and Trial Lengths in Different Regulatory Contexts," Management Science, INFORMS, vol. 69(6), pages 3516-3535, June.
    5. Mavrogonatou, Lida & Sun, Yuxuan & Robertson, David S. & Villar, Sofía S., 2022. "A comparison of allocation strategies for optimising clinical trial designs under variance heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 176(C).
    6. Sofía S. Villar & William F. Rosenberger, 2018. "Covariate†adjusted response†adaptive randomization for multi†arm clinical trials using a modified forward looking Gittins index rule," Biometrics, The International Biometric Society, vol. 74(1), pages 49-57, March.
    7. Helen Yvette Barnett & Sofía S. Villar & Helena Geys & Thomas Jaki, 2023. "A novel statistical test for treatment differences in clinical trials using a response‐adaptive forward‐looking Gittins Index Rule," Biometrics, The International Biometric Society, vol. 79(1), pages 86-97, March.
    8. 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).
    9. Waverly Wei & Xinwei Ma & Jingshen Wang, 2023. "Fair Adaptive Experiments," Papers 2310.16290, arXiv.org.
    10. Stephen E. Chick & Noah Gans & Özge Yapar, 2022. "Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions," Management Science, INFORMS, vol. 68(7), pages 4919-4938, July.

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