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    External validation of risk prediction models for incident colorectal cancer using UK Biobank

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    Author
    Usher-Smith, JA; Harshfield, A; Saunders, CL; Sharp, SJ; Emery, J; Walter, FM; Muir, K; Griffin, SJ
    Date
    2018-03-06
    Source Title
    British Journal of Cancer
    Publisher
    NATURE PUBLISHING GROUP
    University of Melbourne Author/s
    Emery, Jonathan; Walter, Fiona
    Affiliation
    General Practice
    Metadata
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    Document Type
    Journal Article
    Citations
    Usher-Smith, J. A., Harshfield, A., Saunders, C. L., Sharp, S. J., Emery, J., Walter, F. M., Muir, K. & Griffin, S. J. (2018). External validation of risk prediction models for incident colorectal cancer using UK Biobank. BRITISH JOURNAL OF CANCER, 118 (5), pp.750-759. https://doi.org/10.1038/bjc.2017.463.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/253108
    DOI
    10.1038/bjc.2017.463
    Abstract
    BACKGROUND: This study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire. METHODS: External validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. RESULTS: There were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals. CONCLUSIONS: Several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.

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