A systematic review of the “promising zone” design

Abstract

Introduction Sample size calculations require assumptions regarding treatment response and variability. Incorrect assumptions can result in under- or overpowered trials, posing ethical concerns. Sample size re-estimation (SSR) methods investigate the validity of these assumptions and increase the sample size if necessary. The “promising zone” (Mehta and Pocock, Stat Med 30:3267–3284, 2011) concept is appealing to researchers for its design simplicity. However, it is still relatively new in the application and has been a source of controversy. Objectives This research aims to synthesise current approaches and practical implementation of the promising zone design. Methods This systematic review comprehensively identifies the reporting of methodological research and of clinical trials using promising zone. Databases were searched according to a pre-specified search strategy, and pearl growing techniques implemented. Results The combined search methods resulted in 270 unique records identified; 171 were included in the review, of which 30 were trials. The median time to the interim analysis was 60% of the original target sample size (IQR 41–73%). Of the 15 completed trials, 7 increased their sample size. Only 21 studies reported the maximum sample size that would be considered, for which the median increase was 50% (IQR 35–100%). Conclusions Promising zone is being implemented in a range of trials worldwide, albeit in low numbers. Identifying trials using promising zone was difficult due to the lack of reporting of SSR methodology. Even when SSR methodology was reported, some had key interim analysis details missing, and only eight papers provided promising zone ranges.

A retrospective analysis of conditional power assumptions in clinical trials with continuous or binary endpoints

“…The most commonly used CP assumptions are (1) current trend ( ; assuming the data observed so far is likely to continue for the duration of the trial), sometimes referred to as ‘observed conditional power’ [ 8 ] and (2) the hypothesised treatment effect ( ; assuming the hypothesised treatment effect used in the original sample size calculation), sometimes referred to as ‘assumed conditional power’ [ 8 ]. There is criticism in the literature regarding the current trend assumption, due to high variability early in the trial duration and potentially yielding an unstable estimate of conditional power values [ 6 ]. An alternative recommendation from the literature [ 7 , 9 ] is based on optimistic confidence limits of the observed treatment effect, being the single optimistic value of the two confidence limits defined by , where represents the percentage point of a standard normal distribution.…”

“…A futility boundary of 10% conditional power value is considered for CP assumption comparison in this re-analysis, although other values may be chosen, with boundaries between 10 and 40% being observed in the literature [ 2 , 9 ]. The smaller value has been chosen for this re-analysis to accommodate sample size re-estimation rules based on CP decisions, with alterations to sample size often occurring below 40% CP [ 3 , 6 ]. CP is calculated after every patient for illustrative purposes of CP during the trial progression.…”

“…There has been some debate in statistical literature over the choice of future treatment effect assumption [ 4 – 6 ], and each can yield very different results. Therefore, it is critical that these assumptions are understood by trialists wishing to base key decision making on designs using conditional power.…”

A conservative approach to leveraging external evidence for effective clinical trial design

“…The design approach described here may also be applied to settings allowing for a more nuanced classification of the study outcome. 25 Here P H 0 ð Þ and P H 1 ð Þ represent the prior probabilities of the design hypotheses about the trial primary endpoint. Their ratio…”

“…When Bayesian inference is used, the study outcome is positive when the posterior probability of the treatment effect exceeding a pre‐specified clinically relevant threshold is much greater than the prior, with typical thresholds exceeding 95%. The design approach described here may also be applied to settings allowing for a more nuanced classification of the study outcome 25 …”