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Comparisons of risk prediction methods using nested case‐control data
Authors:Agus Salim  Bénédicte Delcoigne  Krystyn Villaflores  Woon‐Puay Koh  Jian‐Min Yuan  Rob M van Dam  Marie Reilly
Institution:1. Mathematics and Statistics, La Trobe University, Bundoora, VIC, Australia;2. Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;3. Duke‐NUS Graduate Medical School, Singapore;4. Department of Epidemiology, University of Pittsburgh, Pittsburgh, U.S.A.;5. Saw Swee Hock School of Public Health, National University of Singapore, Singapore
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.
Keywords:absolute risk  cost efficiency  prediction models  prognosis  risk calculator  study design
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