• Shaun Porwal ([email protected])
• Rohan Singh ([email protected])

Diagnostic and prognostic models are typically evaluated with measures of accuracy that do not address clinical consequences. Decision-analytic techniques allow assessment of clinical outcomes, but often require collection of additional information that may be cumbersome to apply to models that yield continuous results. Decision Curve Analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requiring only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The dca function performs decision curve analysis for binary and survival outcomes. Review the DCA tutorial (towards the bottom) for a detailed walk-through of various applications. Also, see www.decisioncurveanalysis.org for more information.

The documentation follows the best practice for project documentation as described by Daniele Procida in the Diátaxis documentation framework and consists of four separate parts:

1. Tutorials
2. How-To Guides
3. Reference
4. Explanation

Quickly find what you're looking for depending on your use case by looking at the different pages.

::: dcurves

Apache 2.0