Decision analysis

Engineering

Philosophy

Psychoanalysis

Aerospace engineering

Biology

Statistics

Confusion

Range (aeronautics)

Computer science

Simple (philosophy)

Paleontology

Decision model

Test (biology)

Psychology

Preference

Mathematics

Epistemology

Econometrics

Artificial intelligence

Decision tree

Machine learning

Background Decision curve analysis is a method to evaluate prediction models and diagnostic tests that was introduced in a 2006 publication. Decision curves are now commonly reported in the literature, but there remains widespread misunderstanding of and confusion about what they mean. Summary of commentary In this paper, we present a didactic, step-by-step introduction to interpreting a decision curve analysis and answer some common questions about the method. We argue that many of the difficulties with interpreting decision curves can be solved by relabeling the y -axis as “benefit” and the x -axis as “preference.” A model or test can be recommended for clinical use if it has the highest level of benefit across a range of clinically reasonable preferences. Conclusion Decision curves are readily interpretable if readers and authors follow a few simple guidelines.

A simple, step-by-step guide to interpreting decision curve analysis