Assessing treatment effects in observational data with missing confounders: A comparative study of practical doubly-robust and traditional missing data methods
混杂变量缺失时的处理效应评估:双重稳健与传统缺失数据方法比较
In pharmacoepidemiology, safety and effectiveness are frequently evaluated using readily available administrative and electronic health records data. In these settings, detailed confounder data are often not available in all data sources and therefore missing on a subset of individuals. Multiple imputation (MI) and inverse-probability weighting (IPW) are go-to analytical methods to handle missing data and are dominant in the biomedical literature. Doubly-robust methods,…