Decision Curve Analysis for Model Evaluation

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 may be cumbersome to apply to models that yield a continuous result. Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. See the following references for details on the methods: Vickers (2006) , Vickers (2008) , and Pfeiffer (2020) .


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("dcurves")

0.2.0 by Daniel D. Sjoberg, 13 days ago


https://github.com/ddsjoberg/dcurves, http://www.danieldsjoberg.com/dcurves/


Report a bug at https://github.com/ddsjoberg/dcurves/issues


Browse source code at https://github.com/cran/dcurves


Authors: Daniel D. Sjoberg [aut, cre, cph]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports broom, dplyr, ggplot2, glue, purrr, rlang, scales, survival, tibble

Suggests covr, gtsummary, knitr, rmarkdown, spelling, testthat, tidyr


See at CRAN