Individual and population level inference about risk and burden of diabetes is often made using diagnostic tests that are imperfect and prone to misclassification error (i.e. false positives and negatives). These errors or biases are rarely accounted for and could lead to inappropriate clinical decisions, inefficient allocation of scarce resources, and poor planning of disease prevention and treatment interventions. The objective of this article is to describe how misclassification error due to imperfect diagnostic tests affects individual and population level inference, particularly involving the role of hemoglobin HbA1c in diagnosing Type 2 diabetes mellitus. An illustration of how disease prevalence, test sensitivity and specificity could be used by healthcare providers to inform individual level inference is also provided.
|Original language||English (US)|
|Journal||Current Diabetes Reviews|
|State||E-pub ahead of print - Nov 29 2016|
- Journal Article