通过将观察性研究嵌入随机实验，将观察性研究与随机实验联系起来
Bridging observational studies and randomized experiments by embedding the former in the latter
摘要：
环境暴露对健康的影响已经研究了几十年，通常使用标准回归模型来评估观察性非实验数据中发现的风险-结果关联。我们提出并说明了一种不同的方法来检查因果关系的影响，环境暴露对健康结果的观测数据。我们的策略试图将观察数据结构化，以近似来自假设但现实的随机实验的数据。该方法基于经典试验设计，包括四个阶段，并在其中两个阶段依靠现代计算技术来实现，具体而言，我们的策略包括：1）概念阶段，涉及精确表述因果问题，即假设的随机实验，其中暴露被分配到单位; 2）设计阶段，试图重建（或近似）在观察到任何结果数据之前的随机化实验，3）比较假设随机化实验的暴露和非暴露单元中的感兴趣结果的统计分析，以及4）总结阶段，提供关于暴露对结果的可能因果影响的大小的统计证据的结论。我们说明我们的方法使用一个例子，检查父母吸烟对儿童的肺功能收集在家庭生活在东波士顿在20世纪70年代的影响。为了补充传统的纯粹基于模型的方法，我们的策略，其中包括结果自由匹配抽样，提供了可行的工具，以量化可能对人类健康结果的有害暴露影响，特别是因为它还包括透明的诊断，以评估假设的四阶段统计方法正在应用。
英文摘要：
The health effects of environmental exposures have been studied for decades, typically using standard regression models to assess exposure-outcome associations found in observational non-experimental data. We propose and illustrate a different approach to examine causal effects of environmental exposures on health outcomes from observational data. Our strategy attempts to structure the observational data to approximate data from a hypothetical, but realistic, randomized experiment. This approach, based on insights from classical experimental design, involves four stages, and relies on modern computing to implement the effort in two of the four stages.More specifically, our strategy involves: 1) a conceptual stage that involves the precise formulation of the causal question in terms of a hypothetical randomized experiment where the exposure is assigned to units; 2) a design stage that attempts to reconstruct (or approximate) a randomized experiment before any outcome data are observed, 3) a statistical analysis comparing the outcomes of interest in the exposed and non-exposed units of the hypothetical randomized experiment, and 4) a summary stage providing conclusions about statistical evidence for the sizes of possible causal effects of the exposure on outcomes. We illustrate our approach using an example examining the effect of parental smoking on children's lung function collected in families living in East Boston in the 1970's. To complement the traditional purely model-based approaches, our strategy, which includes outcome free matched-sampling, provides workable tools to quantify possible detrimental exposure effects on human health outcomes especially because it also includes transparent diagnostics to assess the assumptions of the four-stage statistical approach being applied.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
