Assessing treatment effect heterogeneity (TEH) in clinical trials is crucial, as it provides insights into the variability of treatment responses among patients, influencing important decisions related to drug development. Furthermore, it can lead to personalized medicine by tailoring treatments to individual patient characteristics. This paper introduces novel methodologies for assessing treatment effects using the individual treatment effect as a basis. To estimate this effect, we use a Double Robust (DR) learner to infer a pseudo-outcome that reflects the causal contrast. This pseudo-outcome is then used to perform three objectives: (1) a global test for heterogeneity, (2) ranking covariates based on their influence on effect modification, and (3) providing estimates of the individualized treatment effect. We compare our DR-learner with various alternatives and competing methods in a simulation study, and also use it to assess heterogeneity in a pooled analysis of five Phase III trials in psoriatic arthritis. By integrating these methods with the recently proposed WATCH workflow (Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors), we provide a robust framework for analyzing TEH, offering insights that enable more informed decision-making in this challenging area.

翻译：评估临床试验中的治疗效应异质性至关重要，因为它能揭示患者间治疗反应的变异性，从而影响药物研发相关的重要决策。此外，通过根据患者个体特征定制治疗方案，它还能推动个体化医疗的发展。本文介绍了一系列以个体治疗效应为基础评估治疗效果的新方法。为了估计该效应，我们采用双重稳健学习器来推断反映因果对比的伪结局。该伪结局随后被用于实现三个目标：(1) 进行异质性的全局检验，(2) 根据协变量对效应修饰的影响程度对其进行排序，以及 (3) 提供个体化治疗效应的估计值。我们通过模拟研究将我们的DR学习器与多种替代方法和竞争方法进行比较，并将其用于评估五项银屑病关节炎III期试验汇总分析中的异质性。通过将这些方法与近期提出的WATCH工作流程（面向临床试验申办方的药物研发中治疗效应异质性评估工作流程）相结合，我们为分析治疗效应异质性提供了一个稳健的框架，为该复杂领域的决策提供了更深入的见解。