Recovering Incomplete Clustered Binary Data in Longitudinal Electronic Health Records Using Multiple Imputation
This paper introduces and validates the Clustered Logistic Factor with Random Effects (CLF-RE) framework as a computationally scalable multiple imputation method that effectively recovers incomplete, high-dimensional longitudinal binary data in electronic health records, significantly reducing bias in epidemiologic estimates compared to existing approaches.