Fig. 1: Data extraction and analysis pipeline.
From: Imputation of missing values for electronic health record laboratory data
1a Inspired by a stepwise imputation by observation blocks in longitudinal data from EHR, we extracted the last observation before an event (e.g., stroke or heart failure), and the first observation after the event. Two types of holdouts, random holdout values (HV) and random holdout complete cases (HC), represent two missingness scenarios, MAR and monotone with NMAR, respectively. 1b Outline the tested imputation models and algorithms evaluated by error metrics and the results after repeated multiple imputation (MI). Abbreviations: PMM predictive mean matching, 2lpan Implements the Gibbs sampler for the linear two-level model with homogeneous within-group (patient ID) variances, LMM linear mixed-effects model, FCS fully conditional specification, nRMSE Root Mean Square Error normalized by standard deviation, CR Coverage rate, the proportion of confidence intervals that contain the true value.
