Phase 3 adaptive trial design options in treatment of complicated urinary tract infection

Adaptive Trials in Cardiology: Some Considerations and Examples

Adaptive trials hold great promise to enhance the evidence base supporting medical interventions. In this review, we will describe the basic principles of an adaptive trial and the different types of adaptive trials, show examples of adaptive trials, and conclude with the advantages and challenges of different types of adaptive trials. While regulatory bodies have expressed a desire to see more adaptive trials, resistance in the community remains. We hope that this review helps to build greater acceptance of the concept of adaptive trial design.

Reducing Patient Burden in Clinical Trials Through the Use of Historical Controls: Appropriate Selection of Historical Data to Minimize Risk of Bias

Historical data have been used to augment or replace control arms in some rare disease and pediatric clinical trials. With greater availability of historical data and new methodology such as dynamic borrowing, the inclusion of historical data in clinical trials is an increasingly appealing approach for larger disease areas as well, as this can result in increased power and precision and can minimize the burden on patients in clinical trials. However, sponsors must assess whether the potential biases incurred with this approach outweigh the benefits and discuss this trade-off with the regulatory agencies. This paper discusses important points for the appropriate selection of historical controls for inclusion in the analysis of primary and/or key secondary endpoint(s) in clinical trials. The general steps are as follows: (1) Assess whether a trial is a suitable candidate for this approach. (2) If it is, then carefully identify appropriate historical trials to minimize selection bias. (3) Refine the historical control set if appropriate, for example, by selecting subsets of studies or patients. Identification of trial settings that are amenable to historical borrowing and selection of appropriate historical data using the principles discussed in this paper has the potential to lead to more efficient estimation and decision making. Ultimately, this efficiency gain results in lower patient burden and gets effective drugs to patients more quickly.