Taking the long view: how to design a series of Phase III trials to maximize cumulative therapeutic benefit.

BACKGROUND: Traditional clinical trial designs strive to definitively establish the superiority of an experimental treatment, which results in risk-adverse criteria and large sample sizes. Increasingly, common cancers are recognized as consisting of small subsets with specific aberrations for targeted therapy, making large trials infeasible.
PURPOSE: To compare the performance of different trial design strategies over a long-term research horizon.
METHODS: We simulated a series of two-treatment superiority trials over 15 years using different design parameters. Trial parameters examined included the number of positive trials to establish superiority (one-trial vs. two-trial rule), α level (2.5%-50%), and the number of trials in the 15-year period, K (thus, trial sample size). The design parameters were evaluated for different disease scenarios, accrual rates, and distributions of treatment effect. Metrics used included the overall survival gain at a 15-year horizon measured by the hazard ratio (HR), year 15 versus year 0. We also computed the expected total survival benefit and the risk of selecting as new standard of care at year 15 a treatment inferior to the initial control treatment, P(detrimental effect).
RESULTS: Expected survival benefits over the 15-year horizon were maximized when more (smaller) trials were conducted than recommended under traditional criteria, using the criterion of one positive trial (vs. two), and relaxing the α value from 2.5% to 20%. Reducing the sample size and relaxing the α value also increased the likelihood of selecting an inferior treatment at the end. The impact of α and K on the survival benefit depended on the specific disease scenario and accrual rate: greater gains for relaxing α in diseases with good outcome and/or low accrual rates and greater gains for increasing K for diseases with poor outcomes. Trials with smaller sample size did not perform well when using stringent (standard) level of evidence. For each disease scenario and accrual rate studied, the optimal design, defined as the design that the maximized expected total survival benefit while constraining P(detrimental effect) < 2.5%, specified α = 20% or 10%, and a sample size considerably smaller than that recommended by the traditional designs. The results were consistent under different assumed distributions for treatment effect. LIMITATIONS: The simulations assumed no toxicity issues and did not consider interim analyses.
CONCLUSIONS: It is worthwhile to consider a design paradigm that seeks to maximize the expected survival benefit across a series of trials, over a longer research horizon. In today's environment of constrained, biomarker-selected populations, our results indicate that smaller sample sizes and larger α values lead to greater long-term survival gains compared to traditional large trials designed to meet stringent criteria with a low efficacy bar.