Accuracy of Diagnostic Tests

Abstract

Clinical trials of disease management require accurate tests for making a diagnosis/patient follow-up. Whatever test, screening, laboratory or physical, investigators involved need to know how good it is. The goodness of a diagnostic test is a complex question that is usually estimated according to three criteria: (1) its reproducibility, (2) precision, and (3) accuracy. Reproducibility is synonymous to reliability, and is, generally, assessed by the size of differences between duplicate measures. Precision of a test is synonymous to the spread in the test results, and can be estimated, e.g., by standard deviations/standard errors. Accuracy is synonymous to validity, and can be defined as a test’s ability to show which individuals have the disease in question and which do not. Unlike the first two criteria, the third is hard to quantify, first, because it is generally assessed by two estimators rather than one, namely sensitivity and specificity defined as the chance of a true positive and true negative test respectively. A second problem is, that these two estimators are severely dependent on one another. If one is high, the other is, as a rule, low, vice versa. Due to this mechanism it is difficult to find the most accurate diagnostic test for a given disease. In this chapter we review the current dual approach to accuracy and propose that it be replaced with a new method, called the overall accuracy level. The main advantage of this new method is that it tells you exactly how much information is given by the test under assessment. It, thus, enables you to determine the most accurate qualitative tests for making a diagnosis, and can also be used to determine the most accurate threshold for positive quantitative tests.