Unobserved effect modifiers can induce bias when generalizing causal effect estimates to target populations. In this work, we extend a sensitivity analysis framework assessing the robustness of study results to unobserved effect modification that adapts to various generalizability scenarios, including multiple (conditionally) randomized trials, observational studies, or combinations thereof. This framework is interpretable and does not rely on distributional or functional assumptions about unknown parameters. We demonstrate how to leverage the multi-study setting to detect violation of the generalizability assumption through hypothesis testing, showing with simulations that the proposed test achieves high power under real-world sample sizes. Finally, we apply our sensitivity analysis framework to analyze the generalized effect estimate of secondhand smoke exposure on birth weight using cohort sites from the Environmental influences on Child Health Outcomes (ECHO) study.

翻译：当将因果效应估计外推至目标人群时，未观测的效应修饰因子可能导致偏差。本研究扩展了一种敏感性分析框架，用于评估研究结果对未观测效应修饰的稳健性，该框架可适应多种外推场景，包括多重（条件性）随机试验、观察性研究或其组合。该框架具有可解释性，且不依赖于对未知参数的分布或函数假设。我们展示了如何利用多研究设置，通过假设检验检测外推假设的违反情况，并通过模拟证明所提出的检验在现实样本量下具有较高的统计功效。最后，我们应用该敏感性分析框架，利用环境对儿童健康结果（ECHO）研究中的队列站点，分析了二手烟暴露对出生体重的外推效应估计。