title: A hybrid Bayesian-frequentist approach to evaluate clinical trial designs for tests of superiority and non-inferiority.
abstract: Specification of the study objective of superiority or non-inferiority at the design stage of a phase III clinical trial can sometimes be very difficult due to the uncertainty that surrounds the efficacy level of the experimental treatment. This uncertainty makes it tempting for investigators to design a trial that would allow testing of both superiority and non-inferiority hypotheses. However, when a conventional single-stage design is used to test both hypotheses, the sample size is based on the chosen primary objective of either superiority or non-inferiority. In this situation, the power of the test for the secondary objective can be low, which may lead to a large loss of resources. Potentially low reproducibility is another major concern for the single-stage design in phase III trials, because significant findings of confirmatory trials are required to be reproducible. In this paper, we propose a hybrid Bayesian-frequentist approach to evaluate reproducibility and power in single-stage designs for phase III trials to test both superiority and non-inferiority. The essence of the proposed approach is to express the uncertainty that surrounds the efficacy of the experimental treatment as a probability distribution. Then one can use Bayes formula with simple graphical techniques to evaluate reproducibility and power adequacy.
authors.affiliation: Division of Biostatistics, NYU School of Medicine, 650 First Ave., New York, NY 10016, USA.
authors.forename: Yongzhao
authors.initials: Y
authors.lastname: Shao
authors.forename: Vandana
authors.initials: V
authors.lastname: Mukhi
authors.forename: Judith D
authors.initials: JD
authors.lastname: Goldberg
mesh_terms: D001499:Bayes Theorem; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D004341:Drug Evaluation / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D012107:Research Design / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity