Another consideration is the overlap between the statistical power of a trial and its scientific and clinical power. A large, expensive trial can, after all, prove what it set out to prove in terms of statistical significance, but end up confirming a too-small difference from the null hypothesis—one that won't be enough to convince physicians and patients that the new therapy is of any real value. Disturbingly, the larger and more powerful the trial, the greater the chances of this outcome if the issue is not addressed early in its design.

These same concerns apply to adaptive designs, whose flexibility actually makes these tradeoffs more explicit. The balance between them varies depending on the clinical stage. In Phase I, the benefits sought are almost exclusively for the clinical investigators, such as dose-finding. Only in oncology trials, where the Phase I patients are often not healthy volunteers, is there a greater chance for patient/physician benefits to be a consideration. Phase II is probably the stage when these two endpoints are most evenly matched.

From the outside perspective, the main point of a Phase II trial is to determine whether or not the drug benefits patients with the targeted disease. From the inside, though, an equally pressing reason for Phase II is to position things for the best chance of success in the far more costly Phase III. A design that allows for a seamless Phase II/III transition has the potential to allow statistically robust efficacy determinations, while allowing patients to directly benefit from their own participation in the trial, a benefit which is arguably impossible to realize with many standard designs. (This was probably one reason for the high enrollment rates seen in Pfizer's ASTIN trial).

Adaptive designs have the potential to change the way clinical research is conducted. But any such power has to be used wisely. As with every other stage of drug development, no magic is on offer here—a bad design cannot be made good by making it adaptive. Careful thought about the purpose and execution of an adaptive trial is needed to keep it from becoming an exercise in self-deception, something the industry is already well stocked with. Still, given the number of times that dosages, toxicities, and efficacies have been wrongly estimated preclinically, there are clear advantages to methods that can use fewer patients when an effect is greater than expected—and give greater statistical power when it turns out to be less. The next step is getting more drug candidates worthy of their trial designs.

Derek Lowe is the author of In the Pipeline, an industry blog. He can be reached at derek-lowe@sbcglobal.net