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A framework for quantifying net benefits of alternative prognostic models
Author(s) -
Rapsomaniki Eleni,
White Ian R.,
Wood Angela M.,
Thompson Simon G.
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4362
Subject(s) - context (archaeology) , predictive modelling , psychological intervention , computer science , estimation , actuarial science , set (abstract data type) , risk assessment , medicine , risk analysis (engineering) , econometrics , machine learning , economics , paleontology , computer security , management , psychiatry , biology , programming language
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.