Tutorial: Statistical Methods for Evaluating Diagnostic Test Accuracy Using Procalcitonin as an Example
This educational tutorial walks through the statistical toolkit for diagnostic accuracy studies, from sensitivity/specificity and likelihood ratios through ROC curves and bivariate random-effects meta-analysis. Using procalcitonin for sepsis diagnosis as a running example, it demonstrates confidence interval construction, natural frequency communication, and sample size estimation with worked R code. The paper serves as a practical reference for laboratorians and researchers designing or appraising diagnostic studies.
The original study
Statistics in diagnostic medicine.
- Authors
- Schlattmann P
- Journal
- Clinical chemistry and laboratory medicine
- Type
- Journal Article, Review
- PMID
- 35357790
Original abstract
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.