JMIR Publications

ABSTRACT

Reducing research waste and protecting research participants from unnecessary harm should be top priorities for researchers studying interventions. However, the traditional use of fixed sample sizes exposes trials to risks of under- and over-recruitment by requiring that effect sizes be determined a-priori. One mitigating approach is to adopt a Bayesian sequential design which enables evaluation of the available evidence continuously over the trial period to decide when to stop recruitment. Target criteria are defined which encode researchers’ intentions for what is considered findings of interest – and the trial is stopped once, based on available data and the defined target criteria, the scientific question is sufficiently addressed. We revisit a trial of a digital alcohol intervention which used a fixed sample size of 2129 participants. We show that, had a Bayesian sequential design been used, the trial could have ended after collecting data from approximately 300 participants. This would have meant exposing far fewer individuals to trial procedures, including being allocated to the waiting list control condition, and the evidence from the trial could have been made public much sooner.