A prediction model for colon cancer surveillance data

Norman Good, K Suresh, Graeme Young, Trevor Lockett, F Macrae, J Taylor

    Research output: Contribution to journalArticle

    2 Citations (Scopus)

    Abstract

    Dynamic prediction models make use of patient-specific longitudinal data to update individualized survival probability predictions based on current and past information. Colonoscopy (COL) and fecal occult blood test (FOBT) results were collected from two Australian surveillance studies on individuals characterized as high-risk based on a personal or family history of colorectal cancer. Motivated by a Poisson process, this paper proposes a generalized nonlinear model with a complementary log-log link as a dynamic prediction tool that produces individualized probabilities for the risk of developing advanced adenoma or colorectal cancer (AAC). This model allows predicted risk to depend on a patient's baseline characteristics and time-dependent covariates. Information on the dates and results of COLs and FOBTs were incorporated using time-dependent covariates that contributed to patient risk of AAC for a specified period following the test result. These covariates serve to update a person's risk as additional COL, and FOBT test information becomes available. Model selection was conducted systematically through the comparison of Akaike information criterion. Goodness-of-fit was assessed with the use of calibration plots to compare the predicted probability of event occurrence with the proportion of events observed. Abnormal COL results were found to significantly increase risk of AAC for 1 year following the test. Positive FOBTs were found to significantly increase the risk of AAC for 3 months following the result. The covariates that incorporated the updated test results were of greater significance and had a larger effect on risk than the baseline variables.

    Original languageEnglish
    Pages (from-to)2662-2675
    Number of pages14
    JournalSTATISTICS IN MEDICINE
    Volume34
    Issue number18
    DOIs
    Publication statusPublished - 15 Aug 2015

    Keywords

    • Adenoma
    • Cancer surveillance
    • Colonoscopy
    • Complementary log-log link
    • Interval censored
    • Poisson process

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  • Cite this

    Good, N., Suresh, K., Young, G., Lockett, T., Macrae, F., & Taylor, J. (2015). A prediction model for colon cancer surveillance data. STATISTICS IN MEDICINE, 34(18), 2662-2675. https://doi.org/10.1002/sim.6500