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Table 1 Some types of adaptations used in randomised trials with examples

From: The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Trial adaptive feature or adaptation, motivation, and cited examples of useType of adaptive design (AD) and examples of underlying statistical methods
Changing the predetermined sample size in response to inaccurate assumptions of study design parameters to achieve the desired statistical power [34,35,36].Sample size re-estimation, re-assessment, or re-calculation (SSR) using aggregated interim data from all participants or interim data separated according to allocated treatment [37,38,39,40,41,42,43,44].
Stopping the trial early for efficacy, futility, or safety when there is sufficient evidence [45, 46].Group sequential design (GSD) [47, 48]; information-based GSD [49]; futility assessment using stochastic curtailment [50,51,52].
Evaluating multiple treatments in one trial allowing for early selection of promising treatments or dropping futile or unsafe treatments [53,54,55]. New treatments can also be added to an ongoing trial [56].Multi-arm multi-stage (MAMS), dose/treatment-selection, drop-the-loser, or pick-the-winner, or add arm [23, 57,58,59,60,61,62,63,64,65,66].
Changing the treatment allocation ratio to favour treatments indicating beneficial effects [67, 68].Response-adaptive randomisation (RAR) [68,69,70,71,72,73].
Investigating multiple research objectives that are traditionally examined in distinct trial phases, in one trial under a single protocol [74,75,76]. For instance, addressing learning (selecting promising treatments for further testing) and confirmatory objectives in one trial.Operationally or inferentially seamless AD [63,64,65, 77,78,79].
Adjusting the trial population or selecting patients with certain characteristics that are most likely to benefit from investigative treatments [80,81,82,83]. This may involve incorporating statistical information from or adapting on a biomarker.Population or patient enrichment or biomarker AD [84,85,86,87,88].
Changing the primary research hypotheses or objectives or primary endpoints [78, 89]. For example, switching from non-inferiority to superiority.Adaptive hypotheses [58, 90].
Switching the allocated treatment of patients to an alternative treatment influenced by ethical considerations, for instance, due to lack of benefit or safety issues.Adaptive treatment-switching [91, 92].
Combination of at least two types of adaptations [24, 36, 89, 93,94,95,96,97,98].Multiple ADs such as GSD or drop-the-loser with SSR [99]; inferentially seamless phase 2/3 AD with hypotheses selection [77] or population enrichment [100]; biomarker-stratified with RAR [101]; adaptive platform trials where arms can be added or stopped early [19, 24, 102].