Robustness of Multiple Imputation Methods for Missing Risk Factor Data from Electronic Medical Records for Observational Studies

Evaluating appropriate methodologies for imputation of missing outcome data from electronic medical records (EMRs) is crucial but lacking for observational studies. Using US EMR in people with type 2 diabetes treated over 12 and 24 months with dipeptidyl peptidase 4 inhibitors (DPP-4i, n = 38,483) and glucagon-like peptide 1 receptor agonists (GLP-1RA, n = 8,977), predictors of missingness of disease biomarker (HbA1c) were explored. Robustness of multiple imputation (MI) by chained equations, two-fold MI (MI-2F) and MI with Monte Carlo Markov Chain were compared to complete case analyses for drawing inferences. Compared to younger people (age quartile Q1), those in age quartile Q3 and Q4 were less likely to have missing HbA1c by 25–32% (range of OR CI: 0.55–0.88) at 6-month follow-up and by 26–39% (range of OR CI: 0.50–0.80) at 12-month follow-up. People with HbA1c ≥ 7.5% at baseline were 12% (OR CI: 0.83, 0.93) and 14% (OR CI: 0.77, 0.97) less likely to have missing data at 6-month follow-up in the DPP-4i and GLP-1RA groups, respectively. All imputation methods provided similar HbA1c distributions during follow-up as observed with complete case analyses. The clinical inferences based on absolute change in HbA1c and by proportion of people reducing HbA1c to a clinically acceptable level (≤ 7%) were also similar between imputed data and complete case analyses. MI-2F method provided marginally smaller mean difference between observed and imputed data with relatively smaller standard error of difference, compared to other methods, while evaluating for consistency through artificial within-sample analyses. The established MI techniques can be reliably employed for missing outcome data imputations in large EMR-based relational databases, leading to efficiently designing and drawing robust clinical inferences in pharmaco-epidemiological studies.