Methods for Using Data Abstracted from Medical Charts to Impute Longitudinal Missing Data in a Clinical Trial

Abstract

Objective To describe a method for imputing missing follow-up blood pressure data in a clinical hypertension trial using blood pressures abstracted from medical charts. Methods We tested a two-step method. In the first, a longitudinal mixed-effects model was estimated on blood pressures abstracted from medical charts. In the second, the patient-specific fitted values from this model at follow-up were used to impute blood pressures missing at follow-up in the trial. Simulations that imposed alternative missing data mechanisms on observed trial data were used to compare this approach to imputation approaches that do not incorporate data from charts. Results For data that are missing at random, incorporating the fitted values from chart-based longitudinal models leads to estimates of the trial-based blood pressures that are unbiased and have lower mean squared deviation than do blood pressures imputed without the chart-based data. For data that are missing not at random, incorporating fitted values ameliorates but does not eliminate the inherent missing data bias. Conclusions Incorporating chart data into an imputation algorithm via the use of longitudinal mixed-effects model is an efficient way to impute longitudinal data that are missing from a randomized trial.