扩展观察研究推论的方法:考虑因果结构、识别假设和估算器。

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Epidemiology Pub Date : 2024-11-01 Epub Date: 2024-08-09 DOI:10.1097/EDE.0000000000001780
Eleanor Hayes-Larson, Yixuan Zhou, L Paloma Rojas-Saunero, Crystal Shaw, Marissa J Seamans, M Maria Glymour, Audrey R Murchland, Daniel Westreich, Elizabeth Rose Mayeda
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引用次数: 0

摘要

之前关于可推广性和可迁移性定量方法的大部分工作都强调将因果效应估计从随机试验扩展到目标人群。当暴露和暴露-结果中介因素与暴露-结果混杂因素(混杂因素和中介因素都能改变暴露-结果效应)都存在选择时,可识别性假设和估算方法就与随机环境不同。我们认为,这种因果结构在观察性研究中很常见,尤其是在生命过程流行病学领域,例如,当从中晚期入组的队列中扩展早期暴露对晚期结果影响的估计时。我们介绍了在这种情况下使用观察数据进行可识别性假设和识别的方法,强调了与随机试验结果扩展工作的不同之处。我们介绍了统计方法,包括加权、结果建模和双重稳健方法,以估计目标人群中潜在的结果平均值和平均治疗效果,并在模拟研究中说明了这些方法的性能。我们表明,如果研究样本中存在对暴露和混杂因素的选择,估计方法必须能够解决目标人群中的混杂问题。当暴露-结果关系的中介因素也存在选择时,估计方法必须能够使用不同的变量集来解释选择(包括中介因素)和混杂。我们讨论了我们的结果在概念上的影响,并强调了应用工作中尚未解决的实际问题,以便将观察性研究的结果推广到目标人群。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Methods for Extending Inferences From Observational Studies: Considering Causal Structures, Identification Assumptions, and Estimators.

Most prior work in quantitative approaches to generalizability and transportability emphasizes extending causal effect estimates from randomized trials to target populations. Extending findings from observational studies is also of scientific interest, and identifiability assumptions and estimation methods differ from randomized settings when there is selection on both the exposure and exposure-outcome mediators in combination with exposure-outcome confounders (and both confounders and mediators can modify exposure-outcome effects). We argue that this causal structure is common in observational studies, particularly in the field of life course epidemiology, for example, when extending estimates of the effect of an early-life exposure on a later-life outcome from a cohort enrolled in midlife or late life. We describe identifiability assumptions and identification using observed data in such settings, highlighting differences from work extending findings from randomized trials. We describe statistical methods, including weighting, outcome modeling, and doubly robust approaches, to estimate potential outcome means and average treatment effects in the target population and illustrate performance of the methods in a simulation study. We show that in the presence of selection into the study sample on both exposure and confounders, estimators must be able to address confounding in the target population. When there is also selection on mediators of the exposure-outcome relationship, estimators need to be able to use different sets of variables to account for selection (including the mediator), and confounding. We discuss conceptual implications of our results as well as highlight unresolved practical questions for applied work to extend findings from observational studies to target populations.

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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
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