Plausibility of high variability in treatment effects across individuals has been recognized as an important consideration in clinical studies. Surprisingly, little attention has been given to evaluating this variability in design of clinical trials or analyses of resulting data. High variation in a treatment's efficacy or safety across individuals (referred to herein as treatment heterogeneity) may have important consequences because the optimal treatment choice for an individual may be different from that suggested by a study of average effects. We call this an individual qualitative interaction (IQI), borrowing terminology from earlier work—referring to a qualitative interaction (QI) being present when the optimal treatment varies across “groups” of individuals. At least three techniques have been proposed to investigate treatment heterogeneity: techniques to detect a QI, use of measures such as the density overlap of two outcome variables under different treatments, and use of cross-over designs to observe “individual effects.” We elucidate underlying connections among them, their limitations, and some assumptions that may be required. We do so under a potential outcomes framework that can add insights to results from usual data analyses and to study design features that improve the capability to more directly assess treatment heterogeneity.

中文翻译：

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治疗异质性和个体定性相互作用

个体间治疗效果的高度可变性的合理性已被认为是临床研究中的一个重要考虑因素。令人惊讶的是，很少有人关注评估临床试验设计或结果数据分析中的这种可变性。个体间治疗功效或安全性的高度变化（在本文中称为治疗异质性）可能具有重要的后果，因为个体的最佳治疗选择可能与平均效果研究所建议的不同。我们将其称为个体定性相互作用 (IQI)，借用早期工作中的术语——指的是当最佳治疗因个体“群体”而异时存在的定性相互作用 (QI)。至少已经提出了三种技术来研究治疗异质性：检测 QI 的技术、使用不同治疗下两个结果变量的密度重叠等措施，以及使用交叉设计来观察“个体效应”。我们阐明了它们之间的潜在联系、它们的局限性以及可能需要的一些假设。我们在一个潜在的结果框架下这样做，该框架可以为常规数据分析的结果增加洞察力，并研究设计特征，以提高更直接评估治疗异质性的能力。以及一些可能需要的假设。我们在一个潜在的结果框架下这样做，该框架可以为常规数据分析的结果增加洞察力，并研究设计特征，以提高更直接评估治疗异质性的能力。以及一些可能需要的假设。我们在一个潜在的结果框架下这样做，该框架可以为常规数据分析的结果增加洞察力，并研究设计特征，以提高更直接评估治疗异质性的能力。