Data-Adaptive Identification of Effect Modifiers through Stochastic Shift Interventions and Cross-Validated Targeted Learning
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通过随机位移干预与交叉验证靶向学习自适应识别效应修饰因素
- 研究问题
- 如何针对连续暴露识别政策干预效果存在差异的亚群，并给出可解释的分组规则及不确定性评估，以支持有针对性的公共卫生决策？
- 方法与思路
- 作者结合随机位移干预、数据自适应目标参数与交叉验证 TMLE，允许使用机器学习，并通过模拟和 NHANES 数据考察方法表现。
- 材料陈述的贡献
- 摘要称该方法可识别连续暴露的效应修饰因素并提供置信区间；应用发现年龄修饰部分污染物的影响，且提供 EffectXshift 软件包。
- 阅读关注点
- 应核对位移干预与亚群目标参数的定义、数据自适应选择如何进入推断，以及识别条件、机器学习误差要求和年龄分组的稳定性。
- 阅读边界
- 所给版本为 arXiv 预印本，不能据此认定已同行评审或获期刊接收；摘要未说明具体识别条件、覆盖率结果及污染物分析的混杂控制细节。
原文摘要
In epidemiology, identifying subpopulations that are particularly vulnerable to exposures and those who may benefit differently from exposure-reducing interventions is essential. Factors such as age, gender-specific vulnerabilities, and physiological states such as pregnancy are critical for policymakers when setting regulatory guidelines. However, current semi-parametric methods for estimating heterogeneous treatment effects are often limited to binary exposures and can function as black boxes, lacking clear, interpretable rules for subpopulation-specific policy interventions. This study introduces a novel method that uses cross-validated targeted minimum loss-based estimation (TMLE) paired with a data-adaptive target parameter strategy to identify subpopulations with the most significant differential impact of simulated policy interventions that reduce exposure. Our approach is assumption-lean, allowing for the integration of machine learning while still yielding valid confidence intervals. We demonstrate the robustness of our methodology through simulations and application to data from the National Health and Nutrition Examination Survey. Our analysis of NHANES data on persistent organic pollutants (POPs) and leukocyte telomere length (LTL) identified age as a significant effect modifier. Specifically, we found that exposure to 3,3',4,4',5-pentachlorobiphenyl (PCNB) consistently had a differential impact on LTL, with a one-standard deviation reduction in exposure leading to a more pronounced increase in LTL among younger populations compared to older ones. We offer our method as an open-source software package, EffectXshift, enabling researchers to investigate the effect modification of continuous exposures. The EffectXshift package provides clear and interpretable results, informing targeted public health interventions and policy decisions.