In biomedical research, repeated measurements within each subject are often processed to remove artifacts and unwanted sources of variation. The resulting data are used to construct derived outcomes that act as proxies for scientific outcomes that are not directly observable. Although intra-subject processing is widely used, its impact on inter-subject statistical inference has not been systematically studied, and a principled framework for causal analysis in this setting is lacking. In this article, we propose a semiparametric framework for causal inference with derived outcomes obtained after intra-subject processing. This framework applies to settings with a modular structure, where intra-subject analyses are conducted independently across subjects and are followed by inter-subject analyses based on parameters from the intra-subject stage. We develop multiply robust estimators of causal parameters under rate conditions on both intra-subject and inter-subject models, which allows the use of flexible machine learning. We specialize the framework to a mediation setting and focus on the natural direct effect. For high dimensional inference, we employ a step-down procedure that controls the exceedance rate of the false discovery proportion. Simulation studies demonstrate the superior performance of the proposed approach. We apply our method to estimate the impact of stimulant medication on brain connectivity in children with autism spectrum disorder.

翻译：在生物医学研究中，每个受试者内部的重复测量值通常需经过处理以消除伪影和不需要的变异来源。由此产生的数据被用于构建衍生结果变量，这些变量作为无法直接观测的科学结果的代理指标。尽管受试者内部处理被广泛使用，但其对受试者间统计推断的影响尚未得到系统研究，且缺乏该情境下因果分析的原理性框架。本文提出一个半参数框架，用于对经过受试者内部处理后获得的衍生结果进行因果推断。该框架适用于具有模块化结构的场景，其中受试者内部分析在各受试者间独立进行，随后基于受试者内部阶段获得的参数进行受试者间分析。我们在受试者内部模型和受试者间模型的速率条件下开发了因果参数的多重稳健估计量，从而允许使用灵活的机器学习方法。我们将该框架具体应用于中介分析场景，并聚焦于自然直接效应。针对高维推断，我们采用逐步降维程序以控制错误发现比例的超额率。模拟研究证明了所提方法的优越性能。我们应用该方法评估了兴奋剂药物对自闭症谱系障碍儿童大脑连接性的影响。