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Exact statistical analysis for response-adaptive clinical trials: A general and computationally tractable approach

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  • Baas, Stef
  • Jacko, Peter
  • Villar, Sofía S.

Abstract

Response-adaptive clinical trial designs allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. Response-adaptive designs face greater regulatory scrutiny due to potential type I error rate inflation, which limits their uptake in practice. Existing approaches for type I error control either only work for specific designs, have a risk of Monte Carlo/approximation error, are conservative, or computationally intractable. To this end, a general and computationally tractable approach is developed for exact analysis in two-arm response-adaptive designs with binary outcomes. This approach can construct exact tests for designs using either a randomized or deterministic response-adaptive procedure. The constructed conditional and unconditional exact tests generalize Fisher's and Barnard's exact tests, respectively. Furthermore, the approach allows for complexities such as delayed outcomes, early stopping, or allocation of participants in blocks. The efficient implementation of forward recursion allows for testing of two-arm trials with 1,000 participants on a standard computer. Through an illustrative computational study of trials using randomized dynamic programming it is shown that, contrary to what is known for equal allocation, the conditional exact Wald test based on total successes has, almost uniformly, higher power than the unconditional exact Wald test. Two real-world trials with the above-mentioned complexities are re-analyzed to demonstrate the value of the new approach in controlling type I errors and/or improving the statistical power.

Suggested Citation

  • Baas, Stef & Jacko, Peter & Villar, Sofía S., 2025. "Exact statistical analysis for response-adaptive clinical trials: A general and computationally tractable approach," Computational Statistics & Data Analysis, Elsevier, vol. 211(C).
    • Handle: RePEc:eee:csdana:v:211:y:2025:i:c:s0167947325000830
    DOI: 10.1016/j.csda.2025.108207
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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. 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.
    3. Yi, Yanqing, 2013. "Exact statistical power for response adaptive designs," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 201-209.
    4. Simon, Richard & Simon, Noah Robin, 2011. "Using randomization tests to preserve type I error with response adaptive and covariate adaptive randomization," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 767-772, July.
    5. Devan V. Mehrotra & Ivan S. F. Chan & Roger L. Berger, 2003. "A Cautionary Note on Exact Unconditional Inference for a Difference between Two Independent Binomial Proportions," Biometrics, The International Biometric Society, vol. 59(2), pages 441-450, June.
    6. Adam L. Smith & Sofía S. Villar, 2018. "Bayesian adaptive bandit-based designs using the Gittins index for multi-armed trials with normally distributed endpoints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(6), pages 1052-1076, April.
    7. Zhiwei Zhang & Meijuan Li & Min Lin & Guoxing Soon & Tom Greene & Changyu Shen, 2017. "Subgroup selection in adaptive signature designs of confirmatory clinical trials," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 345-361, February.
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