Covariate Balancing through Naturally Occurring Strata
针对协变量存在交互作用的常见情况，提出分层协变量平衡（SCB）方法作为倾向性评分的替代方案，通过将数据按自然发生的协变量组合分层并加权控制组，保证协变量平衡，在模拟和真实数据中比常用方法更准确。
OBJECTIVE: To provide an alternative to propensity scoring (PS) for the common situation where there are interacting covariates. SETTING: We used 1.3 million assessments of residents of the United States Veterans Affairs nursing homes, collected from January 1, 2000, through October 9, 2012. DESIGN: In stratified covariate balancing (SCB), data are divided into naturally occurring strata, where each stratum is an observed combination of the covariates. Within each stratum, cases with, and controls without, the target event are counted; controls are weighted to be as frequent as cases. This weighting procedure guarantees that covariates, or combination of covariates, are balanced, meaning they occur at the same rate among cases and controls. Finally, impact of the target event is calculated in the weighted data. We compare the performance of SCB, logistic regression (LR), and propensity scoring (PS) in simulated and real data. We examined the calibration of SCB and PS in predicting 6-month mortality from inability to eat, controlling for age, gender, and nine other disabilities for 296,051 residents in Veterans Affairs nursing homes. We also performed a simulation study, where outcomes were randomly generated from treatment, 10 covariates, and increasing number of covariate interactions. The accuracy of SCB, PS, and LR in recovering the simulated treatment effect was reported. FINDINGS: In simulated environment, as the number of interactions among the covariates increased, SCB and properly specified LR remained accurate but pairwise LR and pairwise PS, the most common applications of these tools, performed poorly. In real data, application of SCB was practical. SCB was better calibrated than linear PS, the most common method of PS. CONCLUSIONS: In environments where covariates interact, SCB is practical and more accurate than common methods of applying LR and PS.