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A framework for quantifying net benefits of alternative prognostic models.

TitleA framework for quantifying net benefits of alternative prognostic models.
Publication TypeJournal Article
Year of Publication2012
AuthorsRapsomaniki E, White IR, Wood AM
Secondary AuthorsThompson SG
Corporate AuthorsEmerging Risk Factors Collaboration
JournalStat Med
Volume31
Issue2
Pagination114-30
Date Published2012 Jan 30
ISSN1097-0258
KeywordsCardiovascular Diseases, Cost-Benefit Analysis, Discriminant Analysis, Epidemiologic Research Design, Humans, Kaplan-Meier Estimate, Meta-Analysis as Topic, Prognosis, Proportional Hazards Models, Risk Assessment
Abstract

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.

DOI10.1002/sim.4362
Alternate JournalStat Med
PubMed ID21905066
PubMed Central IDPMC3496857
Grant ListU.1052.00.001 / / Medical Research Council / United Kingdom
U.1052.00.006 / / Medical Research Council / United Kingdom
G19/35 / / Medical Research Council / United Kingdom
G0100222 / / Medical Research Council / United Kingdom
G8802774 / / Medical Research Council / United Kingdom
RG/08/013/25942 / / British Heart Foundation / United Kingdom
G0902037 / / Medical Research Council / United Kingdom
UL1 TR000062 / TR / NCATS NIH HHS / United States
G0701619 / / Medical Research Council / United Kingdom
MC_U105260792 / / Medical Research Council / United Kingdom
G0700463 / / Medical Research Council / United Kingdom
RG/08/014/24067 / / British Heart Foundation / United Kingdom
MC_U105260558 / / Medical Research Council / United Kingdom
RG/07/008/23674 / / British Heart Foundation / United Kingdom