Author: Felix Fischer  


Using Bayes theorem to estimate positive and negative predictive values for continuously and ordinally scaled diagnostic tests

Methods: Extending the rationale for the calculation of PPV and NPV, Bayesian theorem is used to calculate the probability of disease given the outcome of a continuously or ordinally scaled test. Probabilities of test results conditional on disease status are modeled in a Bayesian framework and subsequently transformed to probabilities of disease status conditional on test result.  

Results: Using publicly available data, probability of a clinical depression diagnosis given PROMIS Depression scores was estimated. Comparison with PPV and NPV based on dichotomized scores shows that a more fine-grained interpretation of test scores is possible.  

Conclusions: The proposed method bears the chance to facilitate accurate and meaningful interpretation of test results in clinical settings by avoiding unnecessary dichotomization of test scores.  

Keywords: depression; methodology; psychometrics; statistics.