This tutorial gives an introduction into statistical methods for diagnostic medicine. The validity of a diagnostic test can be assessed using sensitivity and specificity which are defined for a binary diagnostic test with known reference or gold standard. As an example we use Procalcitonin with a cut off value ≥ 0.5 g/L as a test and Sepsis-2 criteria as a reference standard for the diagnosis of sepsis. Next likelihood ratios are introduced which combine the information given by sensitivity and specificity. For these measures the construction of confidence intervals is demonstrated. Then, we introduce predictive values using Bayes' theorem. Predictive values are sometimes difficult to communicate. This can be improved using natural frequencies which are applied to our example. Procalcitonin is actually a continuous biomarker, hence we introduce the use of receiver operator curves (ROC) and the area under the curve (AUC). Finally we discuss sample size estimation for diagnostic studies. In order to show how to apply these concepts in practice we explain how to use the freely available software R.
Keywords: likelihood ratio; predictive values; receiver operator curve; sample size estimation; sensitivity; software R; specificity.
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