A framework for quantifying net benefits of alternative prognostic models

Eleni Rapsomaniki; Ian R White; Angela M Wood; Simon G Thompson; Emerging Risk Factors Collaboration

doi:10.1002/sim.4362

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A framework for quantifying net benefits of alternative prognostic models

Journal article

Open access

Peer reviewed

A framework for quantifying net benefits of alternative prognostic models

Eleni Rapsomaniki, Ian R White, Angela M Wood, Simon G Thompson and Emerging Risk Factors Collaboration

Statistics in medicine, Vol.31(2), pp.114-130

01/30/2012

DOI: 10.1002/sim.4362

PMCID: PMC3496857

PMID: 21905066

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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.

Cardiovascular Diseases - diagnosis

Cardiovascular Diseases - economics

Cardiovascular Diseases - prevention & control

Cost-Benefit Analysis

Discriminant Analysis

Epidemiologic Research Design

Humans

Kaplan-Meier Estimate

Meta-Analysis as Topic

Prognosis

Proportional Hazards Models

Risk Assessment - methods

Risk Assessment - statistics & numerical data

Details

Title: Subtitle: A framework for quantifying net benefits of alternative prognostic models
Creators: Eleni Rapsomaniki - University of Cambridge
Ian R White - MRC Biostatistics Unit
Angela M Wood - University of Cambridge
Simon G Thompson - University of Cambridge
Emerging Risk Factors Collaboration
Contributors: R Wallace (Contributor) - University of Iowa, Internal Medicine
Resource Type: Journal article
Publication Details: Statistics in medicine, Vol.31(2), pp.114-130
DOI: 10.1002/sim.4362
PMID: 21905066
PMCID: PMC3496857
NLM abbreviation: Stat Med
ISSN: 0277-6715
eISSN: 1097-0258
Grant note: G19/35 / Medical Research Council G0700463 / Medical Research Council MC_U105260558 / Medical Research Council G0902037 / Medical Research Council RG/08/013/25942 / British Heart Foundation G8802774 / Medical Research Council UL1 TR000062 / NCATS NIH HHS U.1052.00.006 / Medical Research Council G0100222 / Medical Research Council U.1052.00.001 / Medical Research Council RG/08/014/24067 / British Heart Foundation MC_U105260792 / Medical Research Council RG/07/008/23674 / British Heart Foundation G0701619 / Medical Research Council
Language: English
Date published: 01/30/2012
Academic Unit: Epidemiology; Injury Prevention Research Center; Internal Medicine
Record Identifier: 9984364448702771

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