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 [PubMed Abstract]

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 [PubMed Abstract]

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. [PubMed Abstract] 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

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

(link to code)

Tutorials

Tutorials are available for both Stata and R. The tutorials include data sets, code and an Adobe Acrobat document taking the reader step by step through several different analyses.