title: Comparisons of risk prediction methods using nested case-control data.
abstract: Using both simulated and real datasets, we compared two approaches for estimating absolute risk from nested case-control (NCC) data and demonstrated the feasibility of using the NCC design for estimating absolute risk. In contrast to previously published results, we successfully demonstrated not only that data from a matched NCC study can be used to unbiasedly estimate absolute risk but also that matched studies give better statistical efficiency and classify subjects into more appropriate risk categories. Our result has implications for studies that aim to develop or validate risk prediction models. In addition to the traditional full cohort study and case-cohort study, researchers designing these studies now have the option of performing a NCC study with huge potential savings in cost and resources. Detailed explanations on how to obtain the absolute risk estimates under the proposed approach are given. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Mathematics and Statistics, La Trobe University, Bundoora, 3086, VIC, Australia.
authors.forename: Agus
authors.initials: A
authors.lastname: Salim
authors.affiliation: Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Bénédicte
authors.initials: B
authors.lastname: Delcoigne
authors.affiliation: Mathematics and Statistics, La Trobe University, Bundoora, 3086, VIC, Australia.
authors.forename: Krystyn
authors.initials: K
authors.lastname: Villaflores
authors.affiliation: Duke-NUS Graduate Medical School, Singapore.
authors.forename: Woon-Puay
authors.initials: WP
authors.lastname: Koh
authors.affiliation: Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, U.S.A.
authors.forename: Jian-Min
authors.initials: JM
authors.lastname: Yuan
authors.affiliation: Saw Swee Hock School of Public Health, National University of Singapore, Singapore.
authors.forename: Rob M
authors.initials: RM
authors.lastname: van Dam
authors.affiliation: Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Marie
authors.initials: M
authors.lastname: Reilly
mesh_terms: D000367:Age Factors; D002318:Cardiovascular Diseases / Q000209:etiology; D016022:Case-Control Studies; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012737:Sex Factors; D013223:Statistics as Topic / Q000379:methods*
keywords: absolute risk; cost efficiency; prediction models; prognosis; risk calculator; study design