A Statistical Simulation to Evaluate the Robustness of Hb A1c Measurement in the Presence of Quantitative Error

Abstract

Abstract Background The performance requirements for hemoglobin (Hb) A1c analysis have been questioned as analytic methods have improved. We developed a statistical simulation that relates error to the clinical utility of an oft-used laboratory test, as a means of assessing test performance expectations. Methods Finite mixture modeling of the Centers for Disease Control and Prevention—National Health and Nutrition Examination Survey (NHANES) 2017–2020 Hb A1c data in conjunction with Monte Carlo sampling were used to model and simulate a population prior to the introduction of error into the results. The impact of error on clinical utility was assessed by categorizing the results using the American Diabetes Association (ADA) diagnostic criteria and assessing the sensitivity and specificity of Hb A1c under various degrees of error (bias and imprecision). Results With the current allowable total error threshold of 6% for Hb A1c measurement, the simulation estimated a worst case between 50% and 60% for both test sensitivity and specificity for the non-diabetic category. Similarly, sensitivity and specificity estimates for the pre-diabetic category were 30% to 40% and 60% to 70%, respectively. Finally, estimates for the diabetic category yielded values of 80% to 90% for sensitivity and >90% for specificity. Conclusions Bias and imprecision greatly affect the clinical utility of Hb A1c for all patient groups. The simulated error demonstrated in this modeling impacts 3 critical applications of the Hb A1c in diabetes management: the capacity to reliably screen, diagnostic accuracy, and utility in diabetes monitoring.