Designing one-sided group sequential clinical trials to detect a mixture alternative. Issue 2 (3rd April 2018)

• Record Type: Journal Article
• Authors: Peng, Hua; Jeske, Daniel R.; SenGupta, Ashis; Yao, Weixin
• Is Part Of: Sequential analysis. Volume 37:Issue 2(2018)
• Page Start: 268
• Page End: 291
• Publication Date: 2018-04-03
• Subjects: Error spending approach -- group sequential design -- mixture model -- power -- sample size -- treatment effect; 62L05 -- 62H10 -- 62F10; Sequential analysis -- Periodicals; 519.54
• DOI: 10.1080/07474946.2018.1466545
• Languages: English
• ISSNs: 0747-4946

Abstract

Abstract: We consider the construction of one-sided group sequential designs where the stopping rule includes boundaries for early stopping to accept for futility and to reject for efficacy. The traditional assumption that all patients have the same likelihood of benefiting from the treatment is sometimes unrealistic and can underestimate the required sample size. This motivates us to power the design for an alternative where the treatment group observations come from a mixture of normal distributions. For the proposed setting, we use standardized test statistics based on sample means, and the test turns out to be an L-optimal similar test. Stopping boundaries and arm size for the design are determined by Type I and Type II error spending equations. We demonstrate the need for larger arm sizes when trying to detect a mixture alternative compared to trying to detect a pure shift alternative. The unknown variance case is discussed. With the mixture model, we discuss a more general definition of treatment effect. The maximum likelihood estimator for the treatment effect is discussed.