부산대학교 도서관

Clinical trials can be enriched on subpopulations that may be more responsive to treatments to improve the chance of trial success. In 2012 FDA issued a draft guidance to facilitate enrichment design, where it pointed out the uncertainty on the subpopulation classification and on the treatment effect outside of the identified subpopulation. We consider a novel design strategy where the identified subpopulation (biomarker-positive) is augmented by some biomarker-negative patients. Specifically, after sufficiently powering biomarker-positive subpopulation we propose to enroll biomarker-negative patients, enough to assess the overall treatment benefit. We derive a weighted statistic for this assessment, correcting for the disproportionality of biomarker-positive and biomarker-negative subpopulations under enriched trial setting. Screening information is utilized for weight determination. This statistic is an unbiased estimate of the overall treatment effect as that in all-comer trials, and is the basis to power for the overall treatment effect. For analysis, testing will be first performed on biomarker-positive subpopulation; only if treatment benefit is established in this subpopulation will overall treatment effect be tested using the weighted statistic. This design approach differs from typical enrichment design or stratified all-comer design in that the former enrolls only biomarker-positive patients and the latter enrolls a regular all-comer population. It also differs from adaptive enrichment by maintaining the trial design and analysis priority on biomarker-positive subpopulation. Therefore the proposed approach not only warrants a high probability of trial success on biomarker-positive subpopulation, but also efficiently assesses the overall treatment effect in the presence of an uncertain treatment benefit among biomarker-negative patients. [ABSTRACT FROM AUTHOR]