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Prediction impact curve is a new measure integrating intervention effects in the evaluation of risk models
Emory Univ, Rollins Sch Publ Hlth, Dept Epidemiol, Atlanta, GA 30322 USA..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular epidemiology. Karolinska Inst, Dept Med Epidemiol & Biostat, SE-17177 Stockholm, Swedden..
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Molecular epidemiology.ORCID iD: 0000-0003-2256-6972
Emory Univ, Rollins Sch Publ Hlth, Dept Epidemiol, Atlanta, GA 30322 USA.;Vrije Univ Amsterdam Med Ctr, EMGO Inst Hlth & Care Res, Sect Community Genet, Dept Clin Genet, NL-1007 MB Amsterdam, Netherlands..
2016 (English)In: Journal of Clinical Epidemiology, ISSN 0895-4356, E-ISSN 1878-5921, Vol. 69, 89-95 p.Article in journal (Refereed) Published
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Text
Abstract [en]

Objective: We propose a new measure of assessing the performance of risk models, the area under the prediction impact curve (auPIC), which quantifies the performance of risk models in terms of their average health impact in the population. Study Design and Setting: Using simulated data, we explain how the prediction impact curve (PIC) estimates the percentage of events prevented when a risk model is used to assign high-risk individuals to an intervention. We apply the PIC to the Atherosclerosis Risk in Communities (ARIC) Study to illustrate its application toward prevention of coronary heart disease. Results: We estimated that if the ARIC cohort received statins at baseline, 5% of events would be prevented when the risk model was evaluated at a cutoff threshold of 20% predicted risk compared to 1% when individuals were assigned to the intervention without the use of a model. By calculating the auPIC, we estimated that an average of 15% of events would be prevented when considering performance across the entire interval. Conclusion: We conclude that the PIC is a clinically meaningful measure for quantifying the expected health impact of risk models that supplements existing measures of model performance.

Place, publisher, year, edition, pages
2016. Vol. 69, 89-95 p.
Keyword [en]
Prediction impact curve, AUC, Risk model, Predictive model, Coronary heart disease, Predictive ability
National Category
Environmental Health and Occupational Health
Identifiers
URN: urn:nbn:se:uu:diva-274427DOI: 10.1016/j.jclinepi.2015.06.011ISI: 000367127600012PubMedID: 26119889OAI: oai:DiVA.org:uu-274427DiVA: diva2:896527
Funder
EU, FP7, Seventh Framework Programme, HEALTH-F4-2007-201413Swedish Research Council, 2012-1397Swedish Heart Lung Foundation, 20120197NIH (National Institute of Health), HHSN261201200425PEU, European Research Council, 310884
Available from: 2016-01-21 Created: 2016-01-21 Last updated: 2017-11-30Bibliographically approved

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Ingelsson, Erik

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