Introduction
In conventional fixed randomised controlled trials, participants are randomised to treatment groups and followed until outcomes are evaluated, generally using intention-to-treat principles. While these designs are widely considered the preferred method for evaluating the efficacy and effectiveness of healthcare interventions,1 their limitations have been well described.1 Most notable among these limitations is their relative inefficiency.2–4 In many traditional randomised controlled trials, in health services and implementation research, interventions to be tested are set at the beginning of the study, and regardless of what happens during the course of the study, neither treatment assignment or allocation probabilities are modified.2
By contrast, in adaptive randomised trials, outcomes are observed and analysed at prespecified interim time points and modifications to study design can be made based on these observations, including modifying randomisation strategies or dropping inferior treatment arms (figure 1).5 Adaptive multiarm designs might require fewer patients than traditional randomised controlled trials6 and could allow for the testing of multiple interventions with more efficiency, but they also have important caveats, most notably increasing trial and methodological complexity.7 The most common types of adaptive trial designs (in order) include: phase 2/3 studies that combine phase 2 and 3 trials, adaptive group sequential trials (which use interim stopping rules), biomarker adaptive trials (which adapt according to biomarkers), adaptive dose finding studies (which adjust allocation probabilities), pick-the-winner or drop-the-loser design (which drops inferior arms), and sample size re-estimation (which adjusts sample size based on interim data).6 8 Other types of adaptive designs and variations of these existing ones have also been used.9
While adaptive trials have been widely used in early phase clinical studies, particularly in oncology,10–15 they also appear well suited for research domains further along the translational research spectrum. Implementation science and health services research studies often seek to identify the most effective intervention, policy, or tool among a wide variety of possible strategies. Yet their use in these contexts remains extremely limited. Trials in this field typically evaluate healthcare delivery interventions for their real world effectiveness on health outcomes. As such, of the most common adaptive trial designs, those that adjust allocation probabilities, drop inferior arms, or adjust sample size would be particularly helpful in this type of research.16 17
In this article, we describe the potential advantages, disadvantages, and important considerations when applying these types of adaptive trials to implementation and health services research. We specifically consider: which interventions can be tested with adaptive designs; how eligibility criteria, enrolment procedures, and allocation probabilities can be modified; what outcomes can be evaluated and conducting interim analyses; and what the implications are on trial sample sizes and length of follow-up. Each consideration is outlined in table 1 and described in further detail throughout.