Adaptive designs are studies that “include a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study,”1 with the aim to provide opportunity to gather more complete information about the therapeutic intervention from interim analyses. This lessens the unpredictability and reduces the risk of failure of Phase III confirmatory trials due to safety and efficacy concerns. In a traditional fixed-trial approach, the trial is designed and conducted as prescribed by the design until the end of the study when the final data are analyzed. Phases of clinical research are conducted as sequential, stand-alone studies from first-in-human Phase I dose escalation, to Phase II dose-finding, until final Phase III confirmatory trials leading to marketing approval. In adaptive trials, scheduled interim analyses are performed to learn from the accumulating data and make necessary modifications while the trial is ongoing, maintaining the study’s integrity and the validity of final conclusions. The most frequently appearing types of adaptations are seamless Phase II/III (57%), group sequential (21%), biomarker adaptive (20%), and adaptive dose-finding designs (16%).
In a recent DIA-Singapore conference presentation, Cytel biostatistician Charles Warne further elaborated on the three types:
In the last segment of the presentation, the “promising zone design” for sample size re-estimation (SSR) was discussed. The SSR methods investigate the validity of the initial design assumptions based on accruing data and increase the sample size, if needed. This Promising Zone concept, developed by Cytel co-founder Dr. Mehta and colleagues, focuses on the interim analysis test statistic and its corresponding conditional power. Mr. Warne shared a case study, which demonstrated how the promising zone design is an attractive way for sponsors to gate their investment based on the accumulating data, so that the resources to increase sample size (and probability of success) are only committed when interim results are promising.
Overall, while there are important operational aspects to consider for adaptive designs, such designs can save time and cost by making what is an inherently complex drug development process more flexible to adapt to new information as it arises. Therefore, this design can be an innovative solution for drug developers in terms of resources and for patients in terms of health (i.e., getting improved and required treatment sooner and avoiding unnecessary exposure to the ineffective treatment).
1. U.S. Food and Drug Administration. (2010). Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics.