Office of Biostatistics, Center for Drug Evaluation and Research, FDA, Silver Spring, Maryland, USA.
Analytics Data Science, Research & Development, Biogen Inc, Cambridge, Massachusetts, USA.
Pharm Stat. 2023 Jul-Aug;22(4):650-670. doi: 10.1002/pst.2299. Epub 2023 Mar 27.
The International Council for Harmonization (ICH) E9(R1) addendum recommends choosing an appropriate estimand based on the study objectives in advance of trial design. One defining attribute of an estimand is the intercurrent event, specifically what is considered an intercurrent event and how it should be handled. The primary objective of a clinical study is usually to assess a product's effectiveness and safety based on the planned treatment regimen instead of the actual treatment received. The estimand using the treatment policy strategy, which collects and analyzes data regardless of the occurrence of intercurrent events, is usually utilized. In this article, we explain how missing data can be handled using the treatment policy strategy from the authors' viewpoint in connection with antihyperglycemic product development programs. The article discusses five statistical methods to impute missing data occurring after intercurrent events. All five methods are applied within the framework of the treatment policy strategy. The article compares the five methods via Markov Chain Monte Carlo simulations and showcases how three of these five methods have been applied to estimate the treatment effects published in the labels for three antihyperglycemic agents currently on the market.
国际协调理事会（ICH）E9（R1）附录建议在试验设计之前根据研究目标选择合适的评估指标。评估指标的一个定义属性是伴随事件，特别是什么是伴随事件以及如何处理它。临床研究的主要目的通常是根据计划的治疗方案评估产品的有效性和安全性，而不是实际接受的治疗。通常使用采用治疗策略的评估指标，该策略无论伴随事件是否发生都收集和分析数据。在本文中，我们将从作者的角度解释如何使用治疗策略来处理缺失数据，这与抗高血糖产品开发计划有关。文章讨论了五种用于处理伴随事件后缺失数据的统计方法。所有五种方法都在治疗策略框架内应用。本文通过马尔可夫链蒙特卡罗模拟比较了这五种方法，并展示了这五种方法中的三种如何应用于估计目前市场上三种抗高血糖药物标签中公布的治疗效果。
