Decision curve analysis is a simple method for evaluating prediction models, diagnostic tests, and molecular markers.
The method was first published as:
A subsequent discussion paper gives some further details about the method:
A paper describing various extensions to decision curve analysis, such as application to survival time data, is:
Vickers AJ, Cronin AM, Elkin EB, Gonen M. BMC Medical Informatics and Decision Making. 2008 Nov 26;8(1):53. Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers.
For more on the theoretical background of decision curve analysis, see:
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010 Jan;21(1):128-38.
Vickers AJ, Cronin AM. Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework. Semin Oncol. 2010 Feb;37(1):31-8.
Two papers that extend decision curve analysis to issues of treatment response are:
Statistical Code for Running Decision Curve Analysis
A tutorial for running decision curve analysis using Stata, R and SAS takes the reader step by step through doing a basic decision curve analysis, including formatting the graph, interpreting the results and saving the output (download PDF ). Also included are instructions on: showing net reduction in interventions; evaluation of joint or conditional tests; evaluation of published models; application to censored data, including competing risk.
The zip file contains the data sets, Stata code, and R code used in the tutorial (download Decision Curve Analysis tutorial.zip). The example data sets used are provided as text, CSV, Stata, and R format.