Supporting information may be found in the online version of this article.
A framework for quantifying net benefits of alternative prognostic models†
Article first published online: 9 SEP 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 31, Issue 2, pages 114–130, 30 January 2012
How to Cite
Rapsomaniki, E., White, I. R., Wood, A. M., Thompson, S. G. and Emerging Risk Factors Collaboration (2012), A framework for quantifying net benefits of alternative prognostic models. Statist. Med., 31: 114–130. doi: 10.1002/sim.4362
- Issue published online: 28 DEC 2011
- Article first published online: 9 SEP 2011
- Manuscript Accepted: 13 JUL 2011
- Manuscript Received: 4 DEC 2010
- UK Medical Research Council (MRC). Grant Numbers: G0700463, U.1052.00.006, U.1052.00.001
- net benefit;
- cardiovascular disease;
- competing risks;
- screening strategies
New prognostic models are traditionally evaluated using measures of discrimination and risk reclassification, but these do not take full account of the clinical and health economic context. We propose a framework for comparing prognostic models by quantifying the public health impact (net benefit) of the treatment decisions they support, assuming a set of predetermined clinical treatment guidelines. The change in net benefit is more clinically interpretable than changes in traditional measures and can be used in full health economic evaluations of prognostic models used for screening and allocating risk reduction interventions. We extend previous work in this area by quantifying net benefits in life years, thus linking prognostic performance to health economic measures; by taking full account of the occurrence of events over time; and by considering estimation and cross-validation in a multiple-study setting. The method is illustrated in the context of cardiovascular disease risk prediction using an individual participant data meta-analysis. We estimate the number of cardiovascular-disease-free life years gained when statin treatment is allocated based on a risk prediction model with five established risk factors instead of a model with just age, gender and region. We explore methodological issues associated with the multistudy design and show that cost-effectiveness comparisons based on the proposed methodology are robust against a range of modelling assumptions, including adjusting for competing risks. Copyright © 2011 John Wiley & Sons, Ltd.