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Efficacy and toxicity monitoring via Bayesian predictive probabilities in phase II clinical trials

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  • Valeria Sambucini

    (Università degli Studi di Roma La Sapienza)

Abstract

Bayesian monitoring strategies based on predictive probabilities are widely used in phase II clinical trials that involve a single efficacy binary variable. The essential idea is to control the predictive probability that the trial will show a conclusive result at the scheduled end of the study, given the information at the interim stage and the prior beliefs. In this paper, we present an extension of this approach to incorporate toxicity considerations in single-arm phase II trials. We consider two binary endpoints representing response and toxicity of the experimental treatment and define the result as successful at the conclusion of the study if the posterior probability of an high efficacy and that of a small toxicity are both sufficiently large. At any interim look, the Multinomial-Dirichlet distribution provides the predictive probability of each possible combination of future efficacy and toxicity outcomes. It is exploited to obtain the predictive probability that the trial will yield a positive outcome, if it continues to the planned end. Different possible interim situations are considered to investigate the behaviour of the proposed predictive rules and the differences with the monitoring strategies based on posterior probabilities are highlighted. Simulation studies are also performed to evaluate the frequentist operating characteristics of the proposed design and to calibrate the design parameters.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:stmapp:v:30:y:2021:i:2:d:10.1007_s10260-020-00537-3
    DOI: 10.1007/s10260-020-00537-3
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    References listed on IDEAS

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    1. 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.
    2. 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.
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