Bayesian Decision-Theoretic Model; Sequential Clinical Trial; Cost-Effectiveness Analysis;
Background/Aims: there is growing interest in the use of adaptive designs to improve the efficiency of clinical trials. The authors apply a Bayesian decision-theoretic model of a sequential experiment using cost and outcome data from the ProFHER pragmatic trial. The authors assess the model’s potential for delivering value-based research.Methods: Uuing parameter values estimated from the ProFHER pragmatic trial, including the costs of carrying out the trial, the authors establish when the trial could have stopped, had the model’s value-based stopping rule been used. The authors use a bootstrap analysis and simulation study to assess a range of operating characteristics, which they compare with a fixed sample size design which does not allow for early stopping.Results: the authors estimate that application of the model could have stopped the ProFHER trial early, reducing the sample size by about 14%, saving about 5% of the research budget and resulting in a technology recommendation which was the same as that of the trial. The bootstrap analysis suggests that the expected sample size would have been 38% lower, saving around 13% of the research budget, with a probability of 0.92 of making the same technology recommendation decision. It also shows a large degree of variability in the trial’s sample size.Conclusions: benefits to trial cost stewardship may be achieved by monitoring trial data as they accumulate and using a stopping rule which balances the benefit of obtaining more information through continued recruitment with the cost of obtaining that information. The authors present recommendations for further research investigating the application of value-based sequential designs.