These rising costs can be considered not as the price of success, but the price of failure—an insight credited to Robert Hemmings.2 If clinical trial designs could detect failure sooner, in phase I or even phase II, then trials would not proceed to the later phases, where costs have been increasing most dramatically.

There are many ways for a trial to fail, said Michael Parides, Mount Sinai School of Medicine. Perhaps the compound simply does not work, or not at the dose being tested, or not in the expected patient population. Sometimes the study design is not optimal or the entire drug development plan is flawed. Many phase III trials that fail have problems that can be traced to phase I and II trials that did not produce the quality of information needed for the confirmatory trial to be designed appropriately.

Improved clinical trial designs hold great promise for making the clinical trial enterprise more efficient, primarily by earlier detection of inadequate benefit. At the same time, treatments that do offer benefit need to be accurately recognized, so that they are not prematurely abandoned, he said. Reliably discarding compounds that do not work and keeping those that do increases the overall trial success rate.

A promising approach to improving trial design is “adaptive design.” Adaptive design is not a new idea, but it is becoming increasingly interesting to researchers. In general, adaptive designs use interim data to modify an ongoing trial without undermining its validity and integrity or introducing bias. Modifications might include correcting inaccurate assumptions or reestimating the sample size. The adaptations are carefully planned in advance and are prespecified, such that, while the trial design is flexible, it is not completely open-ended. There are numerous variations on the adaptive design theme, some more accepted than others.

Recent developments have made adaptive trial designs more feasible. Perhaps most important is the increased use of Bayesian statistical methods, made feasible by desktop computing power. Bayesian approaches allow continual reassessment of trial findings with respect to, for example, maximum tolerable dose. Rather than assigning patients to trial doses according to an algorithm that does not make dose-limiting toxicity explicit, in the Bayesian approach, the researcher makes an assumption about the relationship between dose and toxicity; data are collected; the relationship is reassessed; and the process repeats through some number of cycles. The key element, Parides said, is the notion of continuous learning: Each new patient has the benefit of what was learned from each previous patient. Most such applications require simulations, an approach


2 Dr. Hemmings is Statistics Unit Manager of the U.K. Medicines and Healthcare Products Regulatory Agency.

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