title: Bridging data across studies using frequentist and Bayesian estimation.
abstract: In drug development programs, an experimental treatment is evaluated across different populations and/or disease types using multiple studies conducted in countries around the world. In order to show the efficacy and safety in a specific population, a bridging study may be required. There are therapeutic areas for which enrolling patients to a trial is very challenging. Therefore, it is of interest to utilize the available historical information from previous studies. However, treatment effect may vary across different subpopulations/disease types; therefore, directly utilizing outcomes from historical studies may result in a biased estimation of treatment effect under investigation in the target trial. In this article, we propose novel approaches using both frequentist and Bayesian frameworks that allow borrowing information from historical studies while accounting for relevant patient's covariates via a propensity-based weighting. We evaluate the operating characteristics of the proposed methods in a simulation study and demonstrate that under certain conditions these methods may lead to improved estimation of a treatment effect.
authors.affiliation: a Department of Statistics , North Carolina State University , Raleigh , North Carolina , USA.
authors.forename: Teng
authors.initials: T
authors.lastname: Zhang
authors.affiliation: b QuintilesIMS , Durham , North Carolina , USA.
authors.forename: Ilya
authors.initials: I
authors.lastname: Lipkovich
authors.affiliation: b QuintilesIMS , Durham , North Carolina , USA.
authors.forename: Olga
authors.initials: O
authors.lastname: Marchenko
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D002986:Clinical Trials as Topic; D003627:Data Interpretation, Statistical; D015195:Drug Design; D006801:Humans; D012107:Research Design
keywords: Bayesian analysis; bridging; clinical trials; power prior; propensity