Health Outcomes: Decision Curve Analysis

Decision curve analysis graph

Decision curve analysis is a simple method for evaluating prediction models, diagnostic tests, and molecular markers.

The method was first published as:

Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Medical Decision Making. 2006 Nov-Dec;26(6):565-74.

A subsequent discussion paper gives some further details about the method:

Steyerberg EW, Vickers AJ. Decision curve analysis: a discussion. Medical Decision Making. 2008 Jan-Feb;28(1):146-9.

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:

Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat. 2008;62(4):314-320.

Baker SG, Cook NR, Vickers A, Kramer BS. Using relative utility curves to evaluate risk prediction. J R Stat Soc Ser A Stat Soc. 2009 Oct 1;172(4):729-748.

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:

Vickers AJ, Kattan MW, Daniel S. Method for evaluating prediction models that apply the results of randomized trials to individual patients. Trials. 2007;8:14

Vickers AJ, Kramer BS, Baker SG. Selecting patients for randomized trials: a systematic approach based on risk group. Trials. 2006;7:30.

Statistical Code for Running Decision Curve Analysis

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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 The example data sets used are provided as text, CSV, Stata, and R format.

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