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Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data

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  • Valeria Sambucini

    (Dipartimento di Scienze Statistiche, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185 Rome, Italy)

Abstract

In clinical trials, futility rules are widely used to monitor the study while it is in progress, with the aim of ensuring early termination if the experimental treatment is unlikely to provide the desired level of efficacy. In this paper, we focus on Bayesian strategies to perform interim analyses in single-arm trials based on a binary response variable. Designs that exploit both posterior and predictive probabilities are described and a slight modification of the futility rules is introduced when a fixed historical response rate is used, in order to add uncertainty in the efficacy probability of the standard treatment through the use of prior distributions. The stopping boundaries of the designs are compared under the same trial settings and simulation studies are performed to evaluate the operating characteristics when analogous procedures are used to calibrate the probability cut-offs of the different decision rules.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8816-:d:618740
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    References listed on IDEAS

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    1. Steffen Ventz & Lorenzo Trippa, 2015. "Bayesian designs and the control of frequentist characteristics: A practical solution," Biometrics, The International Biometric Society, vol. 71(1), pages 218-226, March.
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    3. 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.
    4. 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.
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