Am J Epidemiol. 2020 Dec 1;189(12):1583-1589. doi: 10.1093/aje/kwaa124.
When estimating causal effects, careful handling of missing data is needed to avoid bias. Complete-case analysis is commonly used in epidemiologic analyses. Previous work has shown that covariate-stratified effect estimates from complete-case analysis are unbiased when missingness is independent of the outcome conditional on the exposure and covariates. Here, we assess the bias of complete-case analysis for adjusted marginal effects when confounding is present under various causal structures of missing data. We show that estimation of the marginal risk difference requires an unbiased estimate of the unconditional joint distribution of confounders and any other covariates required for conditional independence of missingness and outcome. The dependence of missing data on these covariates must be considered to obtain a valid estimate of the covariate distribution. If none of these covariates are effect-measure modifiers on the absolute scale, however, the marginal risk difference will equal the stratified risk differences and the complete-case analysis will be unbiased when the stratified effect estimates are unbiased. Estimation of unbiased marginal effects in complete-case analysis therefore requires close consideration of causal structure and effect-measure modification.
在估计因果效应时，需要小心处理缺失数据，以避免偏差。完全案例分析在流行病学分析中被广泛应用。先前的研究表明，在缺失数据与暴露和协变量条件下的结局无关时，完全案例分析得到的协变量分层效应估计是无偏的。在这里，我们评估了在各种缺失数据因果结构下存在混杂时，调整边际效应的完全案例分析的偏倚。我们表明，对于边缘风险差异的估计，需要对混杂因素和缺失和结局条件独立所需的任何其他协变量的无条件联合分布进行无偏估计。必须考虑缺失数据对这些协变量的依赖性，以获得协变量分布的有效估计。然而，如果这些协变量在绝对尺度上都不是效应修正因子，那么边缘风险差异将等于分层风险差异，并且当分层效应估计无偏时，完全案例分析将是无偏的。因此，完全案例分析中无偏边际效应的估计需要仔细考虑因果结构和效应修正。
