Clinical Statistics, GSK, 1250 S. Collegeville Road, Collegeville, PA, 19426-0989, USA.
Data and Statistical Sciences, Abbvie, North Chicago, IL, USA.
Ther Innov Regul Sci. 2020 Jul;54(4):850-860. doi: 10.1007/s43441-019-00014-4. Epub 2019 Dec 10.
Historical data have been used to augment or replace control arms in some rare disease and pediatric clinical trials. With greater availability of historical data and new methodology such as dynamic borrowing, the inclusion of historical data in clinical trials is an increasingly appealing approach for larger disease areas as well, as this can result in increased power and precision and can minimize the burden on patients in clinical trials. However, sponsors must assess whether the potential biases incurred with this approach outweigh the benefits and discuss this trade-off with the regulatory agencies. This paper discusses important points for the appropriate selection of historical controls for inclusion in the analysis of primary and/or key secondary endpoint(s) in clinical trials. The general steps are as follows: (1) Assess whether a trial is a suitable candidate for this approach. (2) If it is, then carefully identify appropriate historical trials to minimize selection bias. (3) Refine the historical control set if appropriate, for example, by selecting subsets of studies or patients. Identification of trial settings that are amenable to historical borrowing and selection of appropriate historical data using the principles discussed in this paper has the potential to lead to more efficient estimation and decision making. Ultimately, this efficiency gain results in lower patient burden and gets effective drugs to patients more quickly.
历史数据已被用于某些罕见病和儿科临床试验中,以补充或替代对照组。随着历史数据的可用性增加,以及动态借用等新方法的出现,这种在临床试验中纳入历史数据的方法对于更大的疾病领域也越来越具有吸引力,因为这可以提高功效和精度,并最大限度地减少临床试验中患者的负担。然而,赞助商必须评估这种方法所带来的潜在偏差是否超过了益处,并与监管机构讨论这种权衡取舍。本文讨论了在临床试验中为纳入主要和/或次要终点分析而适当选择历史对照的重要要点。一般步骤如下:(1)评估试验是否适合采用这种方法。(2)如果适合,则仔细确定适当的历史试验,以最大程度地减少选择偏差。(3)如有必要,进一步完善历史对照集,例如,选择研究或患者的子集。本文讨论的原则可用于确定适合历史借用的试验环境,并选择适当的历史数据,这有可能提高估计和决策的效率。最终,这种效率的提高将降低患者的负担,并使有效的药物更快地惠及患者。