Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies.
Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies.
复制标题
在空气污染研究中使用随机推理发现异质暴露效应。
DOI：
10.1080/01621459.2020.1870476
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发表时间：
2021
影响因子：
3.7
通讯作者：
Dominici,Francesca
中科院分区：
文献类型：
--
作者：
Lee,Kwonsang;Small,DylanS;Dominici,Francesca
一些研究提供了强有力的证据，表明长期暴露在空气污染中，即使是低水平的空气污染，也会增加死亡风险。随着监管行动变得昂贵得令人望而却步，需要强有力的证据来指导制定有针对性的干预措施，以保护最脆弱的群体。在这篇文章中，我们介绍了一种新的统计方法，它(I)发现其影响与总体平均水平有很大不同的子组，(Ii)使用基于随机化的测试来评估发现的异质性影响。此外，我们还开发了一种敏感性分析方法来评估结论对不可测量的混杂偏差的稳健性。通过模拟研究和理论论证，我们证明了聚焦于发现的子组的假设检验可以显著提高检测暴露效应的异质性的统计能力。我们将所提出的从头开始的方法应用于2000-2006年间美国新英格兰地区1,612,414名医疗保险受益人的数据。我们发现，与人口平均水平相比，81-85岁的低收入老年人和85岁及以上的老年人长期暴露于PM2.5对5年死亡率的因果影响在统计学上显著更大。
Several studies have provided strong evidence that long-term exposure to air pollution, even at low levels, increases risk of mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to protect the most vulnerable is needed. In this article, we introduce a novel statistical method that (i) discovers subgroups whose effects substantially differ from the population mean, and (ii) uses randomization-based tests to assess discovered heterogeneous effects. Also, we develop a sensitivity analysis method to assess the robustness of the conclusions to unmeasured confounding bias. Via simulation studies and theoretical arguments, we demonstrate that hypothesis testing focusing on the discovered subgroups can substantially increase statistical power to detect heterogeneity of the exposure effects. We apply the proposed de novo method to the data of 1,612,414 Medicare beneficiaries in the New England region in the United States for the period 2000–2006. We find that seniors aged between 81 and 85 with low income and seniors aged 85 and above have statistically significant greater causal effects of long-term exposure to PM2.5on 5-year mortality rate compared to the population mean.