Accuracy of Diagnostic Tests

Following the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, design, development, validation, verification and implementation of diagnostic tests were actively addressed by a large number of diagnostic test manufacturers. This paper deals with the biases and sources of variation which influence the accuracy of diagnostic tests, including calculating and interpreting test characteristics, defining what is meant by test accuracy, understanding the basic study design for evaluating test accuracy, understanding the meaning of Sensitivity, Specificity, Positive Predictive Value and Negative Predictive Value, and evaluating them numerically, and the ROC curve (or Receiver Operating Characteristic) and the Area under the Curve (AUC).

Automated summary

This paper addresses the biases and sources of variation which influence the accuracy of diagnostic tests, including calculating and interpreting test characteristics, defining test accuracy, understanding the basic study design for evaluating test accuracy, and evaluating Sensitivity, Specificity, Positive Predictive Value and Negative Predictive Value, as well as the ROC curve and AUC.