Bayesian adaptive decision-theoretic designs for multi-arm multi-stage clinical trials

Andrea Bassi, Johannes Berkhof, Daphne de Jong, Peter M. van de Ven*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Multi-arm multi-stage clinical trials in which more than two drugs are simultaneously investigated provide gains over separate single- or two-arm trials. In this paper we propose a generic Bayesian adaptive decision-theoretic design for multi-arm multi-stage clinical trials with K ((Formula presented.)) arms. The basic idea is that after each stage a decision about continuation of the trial and accrual of patients for an additional stage is made on the basis of the expected reduction in loss. For this purpose, we define a loss function that incorporates the patient accrual costs as well as costs associated with an incorrect decision at the end of the trial. An attractive feature of our loss function is that its estimation is computationally undemanding, also when K > 2. We evaluate the frequentist operating characteristics for settings with a binary outcome and multiple experimental arms. We consider both the situation with and without a control arm. In a simulation study, we show that our design increases the probability of making a correct decision at the end of the trial as compared to nonadaptive designs and adaptive two-stage designs.

Original languageEnglish
JournalStatistical Methods in Medical Research
Publication statusAccepted/In press - 2020

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