Complexities with identifying suitable test populations in oncology studies contribute significantly to the 60% attrition rate in Phase III trials. Cyrus Mehta, (President of Cytel) has recently authored a paper on ‘Biomarker Driven Population Enrichment for Adaptive Oncology Trials,’ (forthcoming in Statistics in Medicine) which provides an innovative method for using two-stage adaptive designs for population enrichment.
Mehta, et al., are sensitive to the dilemma faced by Phase III trial designers choosing between open and restricted enrollment. Open enrollment allows for a large number of patients, and ensures that all patients who may benefit from a therapy have an opportunity to be involved. By contrast, limiting enrollment is a superior practice for revealing the efficacy of a trial for a targeted population. The proposed method allows for biomarker driven enrichment at interim analysis, meaning that only those subgroups that appear to benefit from therapy need to progress to the second stage of the trial.
Although motivated by problems in oncology, there is no reason the method cannot be used in other therapeutic areas.
The development of molecularly targeted therapies for certain types of cancers has led to the consideration of population enrichment designs that explicitly factor-in the possibility that the experimental compound might differentially benefit different biomarker subgroups. In such designs, enroll lment would initially be open to a broad patient population with the option to restrict future enrollment, following an interim analysis, to only those biomarker subgroups that appeared to be benefiting from the experimental therapy. While this strategy could greatly improve the chances of success for the trial, it poses several statistical and logistical design challenges. Since late-stage oncology trials are typically event driven, one faces a complex trade-off between power, sample size, number of events and study duration. This trade-off is further compounded by the importance of maintaining statistical independence of the data before and after the interim analysis and of optimizing the timing of the interim analysis. This paper presents statistical methodology that ensures strong control of type-1 error for such population enrichmentdesigns, based on generalizations of the conditional error rate approaches of M¨uller and Sch¨afer and Irle and Sch¨afer . The special difficulties encountered with time-to-event endpoints are addressed by our methods. The crucial role of simulation for guiding the choice of design parameters is emphasized. Although motivated by oncology, the methods are applicable as well to population enrichment designs in other therapeutic areas.
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