Adaptive designs in clinical trials: why use them, and how to run and report them

Philip Pallmann, Alun W Bedding, Babak Choodari-Oskooei, Munyaradzi Dimairo, Laura Flight, Lisa V Hampson, Jane Holmes, Adrian P Mander, Lang'o Odondi, Matthew R Sydes, Sofía S Villar, James M S Wason, Christopher J Weir, Graham M Wheeler, Christina Yap, Thomas Jaki, Philip Pallmann, Alun W Bedding, Babak Choodari-Oskooei, Munyaradzi Dimairo, Laura Flight, Lisa V Hampson, Jane Holmes, Adrian P Mander, Lang'o Odondi, Matthew R Sydes, Sofía S Villar, James M S Wason, Christopher J Weir, Graham M Wheeler, Christina Yap, Thomas Jaki

Abstract

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice.

Keywords: Adaptive design; Design modification; Flexible design; Interim analysis; Seamless design; Statistical methods.

Conflict of interest statement

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

AWB is an employee of Roche Products Ltd. LVH is an employee of AstraZeneca. All other authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Schematic of a traditional clinical trial design with fixed sample size, and an adaptive design with pre-specified review(s) and adaptation(s)
Fig. 2
Fig. 2
Overview of the troxacitabine trial using a response-adaptive randomisation design. The probabilities shown are those at the time the patient on the x-axis was randomised. Coloured numbers indicate the arms to which the patients were randomised
Fig. 3
Fig. 3
Illustration of bias introduced by early stopping for futility. This is for 20 simulated two-arm trials with no true treatment effect. The trajectories of the test statistics (as a standardised measure of the difference between treatments) are subject to random fluctuation. Two trials (red) are stopped early because their test statistics are below a pre-defined futility boundary (blue cross) at the interim analysis. Allowing trials with random highs at the interim to continue but terminating trials with random lows early will lead to an upward bias of the (average) treatment effect

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Source: PubMed

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