Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, QC H3A 1G1, Canada.
Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Am J Epidemiol. 2024 Aug 5;193(8):1176-1181. doi: 10.1093/aje/kwae048.
External validity is an important part of epidemiologic research. To validly estimate effects in specific external target populations using a chosen effect measure (ie, "transport"), some methods require that one account for all effect measure modifiers (EMMs). However, little is known about how including other variables that are not EMMs (ie, non-EMMs) in adjustment sets affects estimates. Using simulations, we evaluated how inclusion of non-EMMs affected estimation of the transported risk difference (RD) by assessing the impacts of covariates that (1) differ (or not) between the trial and the target, (2) are associated with the outcome (or not), and (3) modify the RD (or not). We assessed variation and bias when covariates with each possible combination of these factors were used to transport RDs using outcome modeling or inverse odds weighting. Inclusion of variables that differed in distribution between the populations but were non-EMMs reduced precision, regardless of whether they were associated with the outcome. However, non-EMMs associated with selection did not amplify bias resulting from omission of necessary EMMs. Including all variables associated with the outcome may result in unnecessarily imprecise estimates when estimating treatment effects in external target populations.
外部有效性是流行病学研究的重要组成部分。为了使用选定的效应度量（即“传输”）在特定的外部目标人群中有效估计效应，一些方法要求考虑所有效应修正因素（EMMs）。然而，对于在调整集中包含不属于 EMMs（即非 EMMs）的其他变量如何影响估计值，人们知之甚少。我们使用模拟评估了非 EMMs 的包含如何通过评估以下因素的协变量对传输风险差异（RD）估计的影响来影响估计：（1）试验和目标之间是否存在差异（或不存在差异），（2）与结果是否相关（或不相关），以及（3）是否改变 RD（或不改变）。我们使用结果建模或逆概率加权来评估当使用这些因素的每种可能组合的协变量传输 RD 时，变量的变化和偏差。无论与结果是否相关，在人群之间分布不同但不属于 EMMs 的变量的包含都会降低精度。然而，与选择相关的非 EMMs 不会放大由于省略必要的 EMMs 而导致的偏差。当在外部目标人群中估计治疗效果时，包含与结果相关的所有变量可能会导致不必要的不精确估计。
