Methods Used in Developing the Adaptive Design Extension of the DELTA² Checklist for Enhancing Sample Size Reporting in Trial Grant Applications and Protocols
This paper describes the systematic development of the AD-DELTA2 checklist, an expert-endorsed extension of the DELTA2 guidance created through a multi-stage process of evidence review, Delphi surveys, and consensus meetings to improve the transparency and reproducibility of sample size reporting in adaptive design trial grant applications and protocols.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
Clinical trials are the rigorous experiments that determine whether a new medicine works and is safe for people. For decades, the standard way to run these tests has been a fixed plan: researchers decide exactly how many people to recruit, how long to study them, and what the rules are for stopping, before the first patient is ever enrolled. This approach is reliable, but it can be rigid. Sometimes, a new type of trial design, known as an adaptive design, offers a way to be more flexible. In these trials, researchers can use information gathered from patients as the study progresses to make pre-planned adjustments. They might stop a treatment arm early if it is clearly not working, or they might change the number of people needed in the study if the data suggests the treatment is more or less effective than expected. This flexibility can make trials faster, cheaper, and more ethical, but it introduces a significant challenge: figuring out how many people to plan for in the first place. Because the path of the trial can change, the final number of participants is no longer a single, fixed number but a range of possibilities.
When researchers apply for funding or write the official plan for such a trial, they must explain how they calculated the number of people needed. If this explanation is unclear, funders cannot judge if the budget is realistic, and regulators cannot be sure the results will be trustworthy. Until now, there has been no standard, clear guide on exactly what information needs to be included in these documents for flexible trials. A team of researchers at the University of Sheffield set out to solve this problem. They wanted to create a practical checklist that would help scientists report their sample size calculations clearly and completely, ensuring that these complex trials are planned with the same transparency as traditional ones.
The researchers began by gathering everything they could find about how these trials are currently reported. They looked at existing rules from major health agencies, reviewed past trial documents to see where information was missing, and spoke with the people who actually run these studies, from statisticians to funders. They found that while the rules for standard trials were clear, the guidance for flexible trials was scattered and often insufficient. In many cases, the documents submitted for funding did not explain enough about how the researchers planned to handle the changes that might happen during the trial. This lack of detail made it difficult to understand the risks and costs involved. To fix this, the team decided to build a new tool based on an existing checklist for standard trials, but expanded specifically to cover the unique needs of adaptive designs.
To make sure their new checklist was actually useful, the researchers did not just write it themselves; they tested it with a large group of experts. They invited 122 specialists from around the world, including statisticians, trial managers, and regulators, to review a draft version of their list. These experts rated every item on the list to decide if it was important enough to keep. This process happened in two rounds. In the first round, the experts pointed out confusing wording and suggested new items. The researchers then refined the list and asked the same experts to review it again. By the end of this second round, the group had reached a strong agreement on what should be included. The final step was a one-day meeting where the remaining experts discussed the few items they still disagreed on and voted to finalize the list.
The result of this careful process is a new checklist called the AD-DELTA2. It contains 14 main points that researchers must address when writing their trial plans. These points cover everything from the basic design of the study to the specific rules for how and when the trial might change. For example, the checklist requires researchers to clearly state what the minimum and maximum number of participants could be, how they decided on those numbers, and what the trial would look like if certain changes were triggered. It asks for details on the statistical methods used to predict these outcomes and how the researchers will handle the uncertainty that comes with a flexible design. The team found that this checklist provides a clear, expert-approved way to report these complex calculations, making it easier for funders to understand the plan and for regulators to verify that the science is sound.
The researchers acknowledge that this tool is not a perfect solution for every possible situation. Adaptive trials can be incredibly complex, and the checklist focuses on the most common types of changes rather than every rare scenario. They also note that the checklist is designed to be flexible itself, allowing researchers to adapt how they present the information depending on whether they are writing a grant application or a formal protocol. However, the core goal remains the same: to ensure that the planning behind these modern, flexible trials is as transparent and reproducible as the trials themselves. By providing a standard way to report these details, the AD-DELTA2 checklist aims to remove the guesswork from the planning phase, helping to ensure that the next generation of clinical trials is both efficient and trustworthy.
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