What You Need to Know About Adaptive Trials - Pharmaceutical Executive


What You Need to Know About Adaptive Trials
Enabled by the power of today's computers, a handful of new statistical techniques and clinical-trial designs promise big benefits for pharma, doctors, and patients alike. They'll let you change the way you run your business—and they'll force you to change. Here's your guide to the basics.

Pharmaceutical Executive

There is a price to be paid for these designs, though. They are computationally and logistically complex. One reason that Bayesian statistics were little used for decades was that their number-crunching demands could quickly go beyond anyone's practical capabilities. Modern hardware and software has put them in reach, but the amount of work needed is still substantial. The convolutions of the more powerful designs require a good amount of simulation to check them out before real-world implementation—sometimes tens of thousands of runs. Current computing power is finally able to meet that challenge. But all of this means a certain loss of obviousness, with schemes that are much harder to reduce to the back of an envelope (or a slide presentation laden with bullet points).

The sheer logistics of a high-level adaptive design also require careful thought. Quick and reliable electronic data collection would seem to be mandatory for a trial that is dependent on constant updating. A great deal of that data is going to have to be unblinded while such a trial goes on, in direct proportion to its continuously adaptive nature, and with all the complications that that entails. And adaptive designs present risks of accidental unblinding that are unknown in traditional designs—for example, if patients are to be enrolled at different rates to different treatment arms while the trial is going on. It's easy to envision a situation where people at each study site have access to unblinded results, but that is far too risky, argues Pharsight's Bill Gillespie. A safer alternative would be a central clearinghouse for preparation of study packs and evaluation of data, backed by a reliable inventory and distribution system.


To date, the best example in the open literature of a full-scale, real-world Bayesian trial is Pfizer's ASTIN trial (Acute Stroke Therapy by Inhibition of Neutrophils), published in the journal Stroke in 2003. The trial incorporated, among other features, adaptive allocation of patients to different dose groups and the possibility of a seamless transition to Phase III in the event of strong efficacy readout. As it happened, the trial seems to have been a good one with which to meet the realities of clinical research.

Stroke therapies are difficult to assess for efficacy, with many weeks of treatment and/or follow-up monitoring. This led to the problem of ensuring that data could be meaningfully incorporated back into the trial, which was addressed in this case by modeling outcomes based on early clinical signs. In addition to this anticipated problem, though, several unexpected factors put the design to the test.

The placebo response, for example, was much greater than anticipated, which led the data monitoring committee to stop the trial for futility at the first possible point (which was, to be sure, at the 48-week interval, reflecting the slow progression of any stroke therapy). Another unexpected complication was, paradoxically, the enthusiastic response of the trial centers to the adaptive design. The study's authors later estimated that the pace of recruiting actually outran the adaptive features of the trial, with more patients eventually recruited than would have been statistically required under more measured conditions. The preparation and distribution of clinical supplies seems also to have been a logistical challenge, given that more than 100 study centers worldwide participated.

Still, ASTIN seems to have done what it was designed to do—in this case, killing the drug promptly and decisively. "Pfizer seems to have walked away from it feeling that they'd had a complete answer," says Gillespie. This is an advantage that may have been under-appreciated. Too many negative trials end with one party or another feeling that if something had been done differently (longer, at a higher dose, etc.), the program might have survived. Adaptive designs, built to simultaneously explore clinical data more thoroughly, might leave more researchers satisfied that all the alternatives had been given a fair hearing.

There are other factors that the new designs can help clarify. Consider the two sorts of benefits to be sought with any clinical trial design: You can seek to improve the situation that the clinicians face during the studies, or seek to improve the situation of the eventual patients when the drug reaches the market. These two goals aren't always mutually compatible. For example, it may be more valuable, in the clinical setting, to investigate doses both higher and lower than will likely be used in real-world practice. Similarly, a narrowly focused efficacy trial might give cleaner data from a regulatory standpoint, at the expense of providing more information for the physicians who will eventually prescribe the drug.


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