Unblinded Adaptive Statistical Information Designs: Clinical Endpoint With or Without Biomarker

• Activity Number: 465
• Type: Invited
• Date/Time: Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Statistics in Biopharmaceutical Research Journal
• Author(s): Sue-Jane Wang*+
• Companies: Food and Drug Administration
• Keywords: adaptive weighted (or unweighted) Z-statistic ; biomarker (or surrogate) outcome ; correlation ; heterogeneity (or inconsistency) ; maximum Type I error probability

Abstract

The most frequently seen adaptation of a clinical trial design in regulatory submission is adaptation of statistical information, e.g., sample size or events. Such adaptation can be based solely on the clinical endpoint of interest or early biomarker. In this work, we articulate the technical merits and discuss challenges when statistical information based solely on the clinical endpoint is used as the design aspect for adaptation. We present the interplay between the weighted and unweighted adaptive Z-statistics with versus without additional criteria. We elucidate a way to minimize the potential heterogeneity of the observed treatment effects between stages in a two-stage adaptive design setting. Another framework pertains to base a shorter term biomarker data for adaptation of statistical information, where the final analysis is to test the null hypothesis of no treatment effect based on the ultimate clinical outcome. We present analytically solution for such an adaptation. We conclude by providing a few recommendations.