Heterogeneous treatment effects are driven by treatment effect modifiers, pre-treatment covariates that modify the effect of a treatment on an outcome. Current approaches for uncovering these variables are limited to low-dimensional data, data with weakly correlated covariates, or data generated according to parametric processes. We resolve these issues by developing a framework for defining model-agnostic treatment effect modifier variable importance parameters applicable to high-dimensional data with arbitrary correlation structure, deriving one-step, estimating equation and targeted maximum likelihood estimators of these parameters, and establishing these estimators' asymptotic properties. This framework is showcased by defining variable importance parameters for data-generating processes with continuous, binary, and time-to-event outcomes with binary treatments, and deriving accompanying multiply-robust and asymptotically linear estimators. Simulation experiments demonstrate that these estimators' asymptotic guarantees are approximately achieved in realistic sample sizes for observational and randomized studies alike. This framework is applied to gene expression data collected for a clinical trial assessing the effect of a monoclonal antibody therapy on disease-free survival in breast cancer patients. Genes predicted to have the greatest potential for treatment effect modification have previously been linked to breast cancer. An open-source R package implementing this methodology, unihtee, is made available on GitHub at https://github.com/insightsengineering/unihtee.

翻译：异质性治疗效应由治疗效应修饰因子驱动，这些因子是干预前协变量，能够改变治疗对结局的影响。当前识别这些变量的方法局限于低维数据、协变量弱相关数据或基于参数化过程生成的数据。我们通过开发一个框架解决了这些问题：该框架定义了适用于任意相关结构高维数据的模型无关治疗效应修饰因子变量重要性参数，推导了这些参数的一步估计量、估计方程估计量和目标最大似然估计量，并建立了这些估计量的渐近性质。通过为连续型、二分类及时间至事件结局（伴二分类治疗）的数据生成过程定义变量重要性参数，并推导相应的多重稳健且渐近线性估计量，充分展示了该框架的应用价值。模拟实验表明，在观察性研究和随机对照研究的实际样本量下，这些估计量的渐近保障可近似实现。该框架被应用于评估单克隆抗体疗法对乳腺癌患者无病生存期影响的临床试验基因表达数据，预测具有最大治疗效应修饰潜力的基因此前已被证实与乳腺癌相关。实现该方法的开源R包unihtee已发布于GitHub（https://github.com/insightsengineering/unihtee）。