Lin Li-An, Yuan Shuai Sammy, Li Lie, Ball Greg
Clinical Safety Statistics, Merck & Co., Inc., Rahway, NJ, USA.
Biometrics Team, Kite Pharma, a Gilead Company, Santa Monica, CA, USA.
Contemp Clin Trials. 2020 Aug;95:106068. doi: 10.1016/j.cct.2020.106068. Epub 2020 Jun 27.
During the course of clinical development, ongoing aggregate safety monitoring and evaluation are needed to understand the evolving safety profile and to ensure effective risk-management strategies for medicinal products. CIOMS reports and global regulatory guidance (including from ICH, US FDA, and EMA) compel sponsors for assessment of safety based on aggregate data. To identify and characterize the risks of medicinal products at a program level in a more timely and informed manner, aggregate safety evaluations should combine all available information, including from ongoing blinded trials, completed unblinded trials, and other data sources. In this article, we propose two Bayesian meta-analytic approaches for synthesizing blinded and unblinded studies in order to characterize the evolving safety profile of medicinal products at the program level. With the proposed approaches, sponsors can dynamically update knowledge of their product safety profiles as data accrue. Application of the procedures to a real and a hypothetical clinical trial program are provided to illustrate how the proposed approaches can be used to analyze a pre-specified event of interest and to screen for risk-elevated events.
在临床开发过程中,需要持续进行总体安全性监测和评估,以了解不断演变的安全性概况,并确保针对药品的有效风险管理策略。国际医学科学组织理事会(CIOMS)报告和全球监管指南(包括来自国际人用药品注册技术协调会(ICH)、美国食品药品监督管理局(US FDA)和欧洲药品管理局(EMA)的指南)促使申办者基于总体数据进行安全性评估。为了更及时、更全面地在项目层面识别和描述药品风险,总体安全性评估应整合所有可用信息,包括来自正在进行的盲法试验、已完成的非盲法试验以及其他数据来源的信息。在本文中,我们提出了两种贝叶斯荟萃分析方法,用于综合盲法和非盲法研究,以便在项目层面描述药品不断演变的安全性概况。通过所提出的方法,申办者可以随着数据的积累动态更新其产品安全性概况的知识。本文还将所提出的程序应用于一个真实的和一个假设的临床试验项目,以说明如何使用这些方法来分析预先指定的感兴趣事件并筛查风险升高的事件。