Simple Statistical Measures for Assessing the Accuracy of a Diagnostic Test in Clinical Medicine

Diagnosis plays a key role in the decision-making process in medicine. At variance that in simple situations in which it may be sufficient to recognize the clinical picture dictated by the experience, in more complex situations it is very important to choose the most accurate diagnostic test. The simplest statistical measures used to assess the performance of a diagnostic test are sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios (LRs). Prevalence of disease does not affect sensitivity and specificity of a test while it influences its pre-dictive values, positive and negative. Finally, the likelihood ratio allows physicians to interpret test results in a clinical perspective, because it expresses how many patients given by the test as affected are true positives and false positives (positive likelihood ratio) as well as how many pa-tients given by the test as unaffected are true negatives and false negatives (negative likelihood ratio).Diagnostic research is carried out in the setting of a the cross-sectional study design. In this paper, by providing a series of examples from the literature, we explain how to calculate and in-terpret the most simple statistical indexes qualifying a diagnostic test