Information fraction estimation based on the number of events within the standard treatment regimen. Issue 8 (6th July 2020)

• Record Type: Journal Article
• Authors: Dang, Ha M.; Alonzo, Todd; Franklin, Meredith; Mack, Wendy J.; Krailo, Mark D.; Eckel, Sandrah P.
• Is Part Of: Biometrical journal. Volume 62:Issue 8(2020:Dec.)
• Page Start: 1960
• Page End: 1972
• Publication Date: 2020-07-06
• Subjects: group sequential analysis -- information fraction -- interim monitoring analysis -- maximum duration clinical trials -- pediatric oncology -- survival outcome trials; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201900236
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: For a Phase III randomized trial that compares survival outcomes between an experimental treatment versus a standard therapy, interim monitoring analysis is used to potentially terminate the study early based on efficacy. To preserve the nominal Type I error rate, alpha spending methods and information fractions are used to compute appropriate rejection boundaries in studies with planned interim analyses. For a one‐sided trial design applied to a scenario in which the experimental therapy is superior to the standard therapy, interim monitoring should provide the opportunity to stop the trial prior to full follow‐up and conclude that the experimental therapy is superior. This paper proposes a method called total control only (TCO) for estimating the information fraction based on the number of events within the standard treatment regimen. Based on theoretical derivations and simulation studies, for a maximum duration superiority design, the TCO method is not influenced by departure from the designed hazard ratio, is sensitive to detecting treatment differences, and preserves the Type I error rate compared to information fraction estimation methods that are based on total observed events. The TCO method is simple to apply, provides unbiased estimates of the information fraction, and does not rely on statistical assumptions that are impossible to verify at the design stage. For these reasons, the TCO method is a good approach when designing a maximum duration superiority trial with planned interim monitoring analyses.