Journal article

Comparisons of risk prediction methods using nested case-control data

Agus Salim, Benedicte Delcoigne, Krystyn Villaflores, Woon-Puay Koh, Jian-Min Yuan, Rob M van Dam, Marie Reilly

Statistics in Medicine | WILEY | Published : 2017


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 designin..

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Awarded by National Health and Medical Research Council (NHMRC) Australia

Awarded by National Medical Research Council (NMRC) Singapore

Awarded by Cancerfonden (Swedish Cancer Society)


Funding Acknowledgements

This research was funded by the National Health and Medical Research Council (NHMRC) Australia Grant No. 1108967 awarded to Agus Salim, and theNational Medical Research Council (NMRC) Singapore Grant No. 1270/2010 awarded to Rob van Dam. Krystyn Villaflores received a vacation scholarship from the Australian Mathematical Sciences Institute (AMSI) that enabled her to work on this project, and Benedicte Delcoigne was partially funded by a grant from Cancerfonden (The Swedish Cancer Society), Contract No. 11 0343. The authors would like to thank Myeongjee Lee, who prepared the data set for the real data application, and Nasheen Naidoo and Ye Sun for their help with data extractions.