Open AccessValidation of Colorectal Cancer Models on Long-term Outcomes from a Randomized Controlled Trial
Author(s) -
Maria DeYoreo,
Iris LansdorpVogelaar,
Amy B. Knudsen,
Karen M. Kuntz,
Ann G. Zauber,
Carolyn M. Rutter
Publication year - 2020
Publication title -
medical decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 103
eISSN - 1552-681X
pISSN - 0272-989X
DOI - 10.1177/0272989x20961095
Subject(s) - sigmoidoscopy , medicine , colorectal cancer , microsimulation , randomized controlled trial , incidence (geometry) , hazard ratio , quality adjusted life year , cancer , cost effectiveness , colonoscopy , confidence interval , risk analysis (engineering) , physics , optics , transport engineering , engineering
Microsimulation models are often used to predict long-term outcomes and guide policy decisions regarding cancer screening. The United Kingdom Flexible Sigmoidoscopy Screening (UKFSS) Trial examines a one-time intervention of flexible sigmoidoscopy that was implemented before a colorectal cancer (CRC) screening program was established. Long-term study outcomes, now a full 17 y following randomization, have been published. We use the outcomes from this trial to validate 3 microsimulation models for CRC to long-term study outcomes. We find that 2 of 3 models accurately predict the relative effect of screening (the hazard ratios) on CRC-specific incidence 17 y after screening. We find that all 3 models yield predictions of the relative effect of screening on CRC incidence and mortality (i.e., the hazard ratios) that are reasonably close to the UKFSS results. Two of the 3 models accurately predict the relative reduction in CRC incidence 17 y after screening. One model accurately predicted the absolute incidence and mortality rates in the screened group. The models differ in their estimates related to adenoma detection at screening. Although high-quality screening results help to inform models, trials are expensive, last many years, and can be complicated by ethical issues and technological changes across the duration of the trial. Thus, well-calibrated and validated models are necessary to predict outcomes for which data are not available. The results from this validation demonstrate the utility of models in predicting long-term outcomes and in collaborative modeling to account for uncertainty.