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Adaptive designs in clinical trials: why use them, and how to run and report them
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Adaptive clinical trial designs in oncology - Zang - Chinese Clinical Oncology
Click here. George 1 Soumi Lahiri 1. Barker Editor Sandeep M. Menon Editor Data and Safety Monitoring Statistical Methods for George Editor Xiaofei Wang Editor Benefit-Risk Assessment Medical Product Safety Economic Evaluation of Cancer Statistical Topics in Health Cappelleri, PhD Editor Clinical Trial Optimization Platform Trial Designs in Beckman Editor Methods in Comparative Morton Editor Self-Controlled Case Series Clinical Trial Data Analysis Peace Author Prospective planning of an AD is important for credibility and regulatory considerations [ 41 ].
However, as in any other non-AD trial, some events not envisaged during the course of the trial may call for changes to the design that are outside the scope of a priori planned adaptations, or there may be a failure to implement planned adaptations. Questions may be raised regarding the implications of such unplanned ad hoc modifications.
Is the planned statistical framework still valid? Were the changes driven by potential bias? Are the results still interpretable in relation to the original research question? Thus, any unplanned modifications must be stated clearly, with an explanation as to why they were implemented and how they may impact the interpretation of trial results.
herscutdoking.ml As highlighted earlier, adaptations should be motivated by the need to address specific research objectives. In the context of the trial conducted and its observed results, triallists should discuss the interpretability of results in relation to the original research question s. In particular, who the study results apply to should be considered. For instance, subgroup selection, enrichment and biomarker ADs are motivated by the need to characterise patients who are most likely to benefit from investigative treatments. Thus, the final results may apply only to patients with specific characteristics and not to the general or enrolled population.
What worked well? What went wrong? What could have been done differently? We encourage the discussion of all positive, negative and perhaps surprising lessons learned over the course of an AD trial. Sharing practical experiences with AD methods will help inform the design, planning and conduct of future trials and is, thus, a key element in ensuring researchers are competent and confident enough to apply ADs in their own trials [ 27 ].
For novel cutting-edge designs especially, we recommend writing up and publishing these experiences as a statistician-led stand-alone paper. Otherwise, retrieving and identifying AD trials in the literature and clinical trial registers will be a major challenge for researchers and systematic reviewers [ 28 ]. We wrote this paper to encourage the wider use of ADs with pre-planned opportunities to make design changes in clinical trials. Although there are a few practical stumbling blocks on the way to a good AD trial, they can almost always be overcome with careful planning.
We have highlighted some pivotal issues around funding, communication and implementation that occur in many AD trials. When in doubt about a particular design aspect, we recommend looking up and learning from examples of trials that have used similar designs. As AD methods are beginning to find their way into clinical research, more case studies will become available for a wider range of applications. Practitioners clearly need to publish more of their examples.
Over the last two decades, we have seen and been involved with dozens of trials where ADs have sped up, shortened or otherwise improved trials. That is, however, not to say that all trials should be adaptive. Under some circumstances, an AD would be nonsensical, e. Moreover, it is important to realise that pre-planned adaptations are a safeguard against shaky assumptions at the planning stage, not a means to rescue an otherwise poorly designed trial. ADs indeed carry a risk of introducing bias into a trial. That being said, avoiding ADs for fear of biased results is uncalled for.
The magnitude of the statistical bias is practically negligible in many cases, and there are methods to counteract it. The best way to minimise operational bias which is by no means unique to ADs is by rigorous planning and transparency. Measures such as establishing well-trained and well-informed IDMCs and keeping triallists blind to changes wherever possible, as well as clear and comprehensive reporting, will help build trust in the findings of an AD trial.
The importance of accurately reporting all design specifics, as well as the adaptations made and the trial results, cannot be overemphasised, especially since clear and comprehensive reports facilitate the learning for future AD or non-AD trials. Working through our list of recommendations should be a good starting point. These reporting items are currently being formalised, with additional input from a wide range of stakeholders, as an AD extension to the CONSORT reporting guidance and check list.
The authors would like to thank all reviewers for some very helpful comments and suggestions. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Department of Health or the University of Sheffield. SSV was supported by a research fellowship from Biometrika Trust. PP generated the figures. All authors provided comments on previous versions of the manuscript.
All authors read and approved the final manuscript. LVH is an employee of AstraZeneca. All other authors declare that they have no competing interests.
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