Liu Fangyu, Duchesneau Emilie D, Lund Jennifer L, Jackson John W
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Curr Epidemiol Rep. 2024 Dec;11(4):185-198. doi: 10.1007/s40471-024-00343-5. Epub 2024 Feb 19.
Research questions on exposure change and health outcomes are both relevant to clinical and policy decision making for public health. Causal inference methods can help investigators answer questions about exposure change when the first or incident exposure is unobserved or not well defined. This review aims to help researchers conceive of helpful causal research questions about exposure change and understand various statistical methods for answering these questions to promote wider adoption of causal inference methods in research on exposure change outside the field of pharmacoepidemiology.
Epidemiologic studies examining exposure changes face challenges that can be addressed by causal inference methods, including target trial emulation. However, their application outside the field of pharmacoepidemiology is limited.
In this review, we (a) illustrate considerations in defining an exposure change and defining the total and joint effects of an exposure change, b) provide practical guidance on trial emulation design, data set-up for statistical analysis; (c) demonstrate four statistical methods that can estimate total and/or joint effects (structural conditional mean models, time-dependent matching, inverse probability weighting, and the parametric g-formula); and (d) compare the advantages and limitations of these statistical methods.
关于暴露变化与健康结果的研究问题，对于公共卫生的临床和政策决策均具有相关性。当首次暴露或新发暴露未被观察到或定义不明确时，因果推断方法可帮助研究人员回答有关暴露变化的问题。本综述旨在帮助研究人员构思有关暴露变化的有益因果研究问题，并理解用于回答这些问题的各种统计方法，以促进因果推断方法在药物流行病学领域之外的暴露变化研究中得到更广泛的应用。
研究暴露变化的流行病学研究面临一些可通过因果推断方法（包括目标试验模拟）解决的挑战。然而，它们在药物流行病学领域之外的应用有限。
在本综述中，我们（a）阐述了在定义暴露变化以及定义暴露变化的总体和联合效应时的注意事项，（b）为试验模拟设计、统计分析的数据设置提供实用指导；（c）展示了四种可估计总体和/或联合效应的统计方法（结构条件均值模型、时间依存匹配、逆概率加权和参数化g公式）；（d）比较了这些统计方法的优缺点。
