Zhang Teng, Lipkovich Ilya, Marchenko Olga
a Department of Statistics , North Carolina State University , Raleigh , North Carolina , USA.
b QuintilesIMS , Durham , North Carolina , USA.
J Biopharm Stat. 2017;27(3):426-441. doi: 10.1080/10543406.2017.1289948. Epub 2017 Feb 7.
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.
在药物研发项目中，一种实验性治疗方法会通过在全球多个国家开展的多项研究，针对不同人群和/或疾病类型进行评估。为了证明在特定人群中的疗效和安全性，可能需要进行桥接研究。在某些治疗领域，招募患者参加试验极具挑战性。因此，利用先前研究中可用的历史信息很有意义。然而，治疗效果可能因不同亚组/疾病类型而异；因此，直接使用历史研究的结果可能会导致对目标试验中正在研究的治疗效果的估计产生偏差。在本文中，我们提出了使用频率学派和贝叶斯框架的新方法，这些方法允许从历史研究中借鉴信息，同时通过基于倾向的加权考虑相关患者的协变量。我们在模拟研究中评估了所提出方法的操作特性，并证明在某些条件下，这些方法可能会改进对治疗效果的估计。
