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Bayesian predictive monitoring with bivariate binary outcomes in phase II clinical trials

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  • Sambucini, Valeria

Abstract

Traditionally, phase II single-arm trials are based on a binary response variable that represents the efficacy of the experimental treatment. However, the introduction of an additional binary endpoint to assess whether the new therapy is also sufficiently safe for a further evaluation in larger phase III studies is often suggested. A Bayesian predictive strategy for interim monitoring in phase II trials focused on bivariate binary outcomes is proposed. At any interim analysis, the stopping rules are based on the evaluation of the predictive probability that the trial will show a conclusive result at the planned end of the study, given the observed data. The proposed procedure is applied using hypothetical scenarios that represent different situations which may occur at the interim stage. A real data application is also illustrated with the use of both non-informative and informative prior distributions. Finally, simulation studies to evaluate the operating characteristics of the design have been performed.

Suggested Citation

  • Sambucini, Valeria, 2019. "Bayesian predictive monitoring with bivariate binary outcomes in phase II clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 18-30.
    • Handle: RePEc:eee:csdana:v:132:y:2019:i:c:p:18-30
    DOI: 10.1016/j.csda.2018.06.015
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    References listed on IDEAS

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    1. Yong Lin & Weichung J. Shih, 2004. "Adaptive Two-Stage Designs for Single-Arm Phase IIA Cancer Clinical Trials," Biometrics, The International Biometric Society, vol. 60(2), pages 482-490, June.
    2. Guosheng Yin & Nan Chen & J. Jack Lee, 2012. "Phase II trial design with Bayesian adaptive randomization and predictive probability," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 219-235, March.
    3. Nigel Stallard & Peter F. Thall & John Whitehead, 1999. "Decision Theoretic Designs for Phase II Clinical Trials with Multiple Outcomes," Biometrics, The International Biometric Society, vol. 55(3), pages 971-977, September.
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    Cited by:

    1. Fulvio De Santis & Stefania Gubbiotti, 2021. "Sample Size Requirements for Calibrated Approximate Credible Intervals for Proportions in Clinical Trials," IJERPH, MDPI, vol. 18(2), pages 1-11, January.
    2. Valeria Sambucini, 2021. "Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data," IJERPH, MDPI, vol. 18(16), pages 1-17, August.
    3. Valeria Sambucini, 2021. "Efficacy and toxicity monitoring via Bayesian predictive probabilities in phase II clinical trials," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 637-663, June.

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