title: Bayesian approach to the design and analysis of non-inferiority trials for anti-infective products.
abstract: In the absence of placebo-controlled trials, determining the non-inferiority (NI) margin for comparing an experimental treatment with an active comparator is based on carefully selected well-controlled historical clinical trials. With this approach, information on the effect of the active comparator from other sources including observational studies and early phase trials is usually ignored because of the need to maintain active comparator effect across trials. This may lead to conservative estimates of the margin that translate into larger sample-size requirements for the design and subsequent frequentist analysis, longer trial durations, and higher drug development costs. In this article, we provide methodological approaches to determine NI margins that can utilize all relevant historical data through a novel power adjusted Bayesian meta-analysis, with Dirichlet process priors, that puts ordered weights on the amount of information a set of data contributes. We also provide a Bayesian decision rule for the non-inferiority analysis that is based on a broader use of available prior information and a sample-size determination that is based on this Bayesian decision rule. Finally, the methodology is illustrated through several examples.
authors.affiliation: Office of Biostatistics, Food and Drug Administration, Silver Spring, MD, 20993-0002, USA.
authors.forename: Meg A
authors.initials: MA
authors.lastname: Gamalo
authors.forename: Ram C
authors.initials: RC
authors.lastname: Tiwari
authors.forename: Lisa M
authors.initials: LM
authors.lastname: LaVange
mesh_terms: D000890:Anti-Infective Agents / Q000627:therapeutic use*; D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D006801:Humans; D015201:Meta-Analysis as Topic; D012107:Research Design; D018401:Sample Size
chemical_list: D000890:Anti-Infective Agents
keywords: Dirichlet process prior; meta-analysis; non-inferiority margin; power prior; sample-size estimation