Ruberg Stephen J, Akacha Mouna
Global Statistical Sciences and Advanced Analytics, Eli Lilly & Company, Lilly Corporate Center, Indianapolis, Indiana, USA.
Statistical Consulting and Methodology, Novartis Pharma AG, Basel, Switzerland.
Clin Pharmacol Ther. 2017 Dec;102(6):917-923. doi: 10.1002/cpt.869. Epub 2017 Oct 9.
This article focuses on the choice of treatment effect measures in randomized clinical trials (RCTs). Traditionally, an intention-to-treat (ITT) analysis is conducted with an implicit understanding that a treatment-policy effect is of greatest interest. In this article we contend that this approach may not always provide accurate information about clinically meaningful treatment effects, and we present an argument that for any RCT it is desirable to require an explicit definition of what treatment effect is of primary interest, known as the "estimand." We will discuss the limitations of the traditional ITT effect measures as well as the state-of-the art thinking with regard to estimands. Furthermore, we will offer alternate choices that acknowledge that treatments have multiple effects.
本文聚焦于随机临床试验（RCT）中治疗效果测量指标的选择。传统上，意向性分析（ITT）是在隐含地认为治疗策略效果最为重要的情况下进行的。在本文中，我们认为这种方法可能并不总是能提供有关具有临床意义的治疗效果的准确信息，并且我们提出一个观点，即对于任何RCT而言，都需要明确界定首要关注的治疗效果是什么，这被称为“估计量”。我们将讨论传统ITT效果测量指标的局限性以及关于估计量的前沿思考。此外，我们将提供其他选择，这些选择承认治疗具有多种效果。
