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Box 14 Exemplars on reporting item 8b elements

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

Example 1. Pre-planned changes to allocation ratios as a consequence of treatment selection or/and sample size increase
“All new patients recruited after the conclusions of the interim analysis are made, will be randomised in a (2:) 2: 1 ratio to the selected regimen(s) of propranolol or placebo until a total of (100:)100: 50 patients (or more in the case where a sample size increase is recommended) have been randomised over the two stages of the study.” [94] Extracted from supplementary material. (2:) and (100:) are only applicable if the second best regimen is selected at stage 1.
Example 2. Bayesian RAR; pre-planned algorithm to update allocation ratios; frequency of updates (after every participant);no burn-in period; period of a fixed control allocation ratio; information that informed adaptation; decision-making criteria for dropping treatments (part of item 7b)
See Additional file 3 as extracted from Giles et al. [67]
Example 3. Bayesian RAR; burn-in period; fixed control allocation ratio; details of adaptive randomisation including additional adaptations and decision-making criteria (part of item 7b); derivation of statistical quantities; details of Bayesian models and prior distribution with rationale
“…eligible patients were randomized on day 1 to treatment with placebo or neublastin 50, 150, 400, 800, or 1200 mg/kg, administered by intravenous injection on days 1, 3, and 5. The first 35 patients were randomized in a 2:1:1:1:1:1 ratio to placebo and each of the 5 active doses (randomisation method required) (i.e., 10 patients in the placebo group and 5 for each dose of active treatment). Subsequently, 2 of every 7 enrolled patients were assigned to placebo. Interim data evaluations of pain (AGPI) and pruritus questionnaire data (proportion of patients who reported ‘the itch is severe enough to cause major problems for me’ on an Itch Impact Questionnaire) were used to update the allocation probability according to a Bayesian algorithm for adaptive allocation and to assess efficacy and futility criteria for early stopping of enrolment (Fig. 1 [not shown here]). Interim evaluations and updates to the allocation probabilities were performed weekly. Enrolment was to be stopped early after ≥50 patients had been followed for 4 weeks if either the efficacy criterion (> 80% probability that the maximum utility dose reduces the pain score by ≥1.5 points more than the placebo) or the futility criterion (< 45% probability that the maximum utility dose reduces pain more than the placebo) was met.” [140] Details of statistical models used—including computation of posterior quantities; prior distribution with rationale; generation of the utility function; and weighting of randomisation probabilities—are accessible via a weblink provided (https://links.lww.com/PAIN/A433).