Review article

https://doi.org/10.11613/BM.2018.010101

Bayes’ theorem, the ROC diagram and reference values: Definition and use in clinical diagnosis

Anders Kallner ; Department of clinical chemistry, Karolinska University Laboratory, Stockholm, Sweden

Abstract

Medicine is diagnosis, treatment and care. To diagnose is to consider the probability of the cause of discomfort experienced by the patient. The
physician may face many options and all decisions are liable to uncertainty to some extent. The rational action is to perform selected tests and thereby increase the pre-test probability to reach a superior post-test probability of a particular option. To draw the right conclusions from a test, certain background information about the performance of the test is necessary. We set up a partially artificial dataset with measured results obtained from the laboratory information system and simulated diagnosis attached. The dataset is used to explore the use of contingency tables with a unique graphic design and software to establish and compare ROC graphs. The loss of information in the ROC curve is compensated by a cumulative data analysis (CDA) plot linked to a display of the efficiency and predictive values. A standard for the contingency table is suggested and the use of dynamic reference intervals discussed.

Keywords

likelihood ratio; cumulative data analysis; odds; sensitivity; specificity

Hrčak ID:

189590

URI

https://hrcak.srce.hr/189590

Publication date:

15.2.2018.

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