Post‐test diagnostic accuracy measures under tree ordering of disease classes

Abstract

The medical field commonly employs post‐test measures such as predictive values and likelihood ratios to assess diagnostic accuracy. Predictive values, including positive and negative values (PPV and NPV), indicate the probability that individuals have a target health condition based on test results. On the other hand, likelihood ratios, including positive and negative ratios (LR+ and LR− respectively), compare the probability of a particular test result between the diseased and non‐diseased groups. While predictive values are useful in evaluating diagnostic test accuracy in populations with varying disease prevalence, likelihood ratios provide a direct link between pre‐test and post‐test probabilities in specific patients. In this study, we introduce and analyze a new approach called generalized predictive values and likelihood ratios, using a tree ordering of disease classes. We evaluate the effectiveness of these methods through simulation studies and illustrate their use with real data on lung cancer.