title: A prediction model for colon cancer surveillance data.
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.
authors: CSIRO Mathematical and Information Sciences/Australian e-Health Research Centre, Royal Brisbane and Women's Hospital, Herston, QLD, 4029, Australia.
authors: Norm M
authors: NM
authors: Good
authors: Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.
authors: Krithika
authors: K
authors: Suresh
authors: Flinders Centre for Innovation in Cancer, Flinders University, Bedford Park, SA, 5042, Australia.
authors: Graeme P
authors: GP
authors: Young
authors: CSIRO Preventative Health Flagship and Animal, Food and Health Sciences, Riverside Corporate Park, North Ryde, NSW, 2113, Australia.
authors: Trevor J
authors: TJ
authors: Lockett
authors: Colorectal Medicine and Genetics, The Royal Melbourne Hospital, VIC, 3050, Australia.
authors: Finlay A
authors: FA
authors: Macrae
authors: Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.
authors: Jeremy M G
authors: JM
authors: Taylor
mesh_terms: D000236:Adenoma / Q000473:pathology; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001315:Australia; D001699:Biometry / Q000379:methods*; D003110:Colonic Neoplasms / Q000175:diagnosis* / Q000473:pathology; D003113:Colonoscopy; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D009780:Occult Blood; D016012:Poisson Distribution; D011159:Population Surveillance; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D017678:Sex Distribution; D013021:South Australia; D014739:Victoria
keywords: Poisson process; adenoma; cancer surveillance; colonoscopy; complementary log-log link; interval censored