IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v40y2020i8p1034-1040.html
   My bibliography  Save this article

Validation of Colorectal Cancer Models on Long-term Outcomes from a Randomized Controlled Trial

Author

Listed:
  • Maria DeYoreo

    (RAND Corporation, Santa Monica, CA, USA)

  • Iris Lansdorp-Vogelaar

    (Department of Public Health, Erasmus MC, Rotterdam, Zuid-Holland, the Netherlands)

  • Amy B. Knudsen

    (Institute for Technology Assessment and Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA)

  • Karen M. Kuntz

    (Department of Health Policy and Management, University of Minnesota, School of Public Health, Minneapolis, MN, USA)

  • Ann G. Zauber

    (Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, NY, USA)

  • Carolyn M. Rutter

    (RAND Corporation, Santa Monica, CA, USA)

Abstract

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.

Suggested Citation

  • Maria DeYoreo & Iris Lansdorp-Vogelaar & Amy B. Knudsen & Karen M. Kuntz & Ann G. Zauber & Carolyn M. Rutter, 2020. "Validation of Colorectal Cancer Models on Long-term Outcomes from a Randomized Controlled Trial," Medical Decision Making, , vol. 40(8), pages 1034-1040, November.
    • Handle: RePEc:sae:medema:v:40:y:2020:i:8:p:1034-1040
    DOI: 10.1177/0272989X20961095
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X20961095
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X20961095?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rutter, Carolyn M. & Miglioretti, Diana L. & Savarino, James E., 2009. "Bayesian Calibration of Microsimulation Models," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1338-1350.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vahab Vahdat & Oguzhan Alagoz & Jing Voon Chen & Leila Saoud & Bijan J. Borah & Paul J. Limburg, 2023. "Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks," Medical Decision Making, , vol. 43(6), pages 719-736, August.
    2. Stavroula A Chrysanthopoulou, 2017. "MILC: A Microsimulation Model of the Natural History of Lung Cancer," International Journal of Microsimulation, International Microsimulation Association, vol. 10(3), pages 5-26.
    3. Jing Voon Chen & Julia L. Higle & Michael Hintlian, 2018. "A systematic approach for examining the impact of calibration uncertainty in disease modeling," Computational Management Science, Springer, vol. 15(3), pages 541-561, October.
    4. Douglas Taylor & Vivek Pawar & Denise Kruzikas & Kristen Gilmore & Myrlene Sanon & Milton Weinstein, 2012. "Incorporating Calibrated Model Parameters into Sensitivity Analyses," PharmacoEconomics, Springer, vol. 30(2), pages 119-126, February.
    5. Sophie Whyte & Cathal Walsh & Jim Chilcott, 2011. "Bayesian Calibration of a Natural History Model with Application to a Population Model for Colorectal Cancer," Medical Decision Making, , vol. 31(4), pages 625-641, July.
    6. Alex van der Steen & Joost van Rosmalen & Sonja Kroep & Frank van Hees & Ewout W. Steyerberg & Harry J. de Koning & Marjolein van Ballegooijen & Iris Lansdorp-Vogelaar, 2016. "Calibrating Parameters for Microsimulation Disease Models," Medical Decision Making, , vol. 36(5), pages 652-665, July.
    7. Eleanor J. Murray & James M. Robins & George R. Seage III & Sara Lodi & Emily P. Hyle & Krishna P. Reddy & Kenneth A. Freedberg & Miguel A. Hernán, 2018. "Using Observational Data to Calibrate Simulation Models," Medical Decision Making, , vol. 38(2), pages 212-224, February.
    8. Arias Chao, Manuel & Kulkarni, Chetan & Goebel, Kai & Fink, Olga, 2022. "Fusing physics-based and deep learning models for prognostics," Reliability Engineering and System Safety, Elsevier, vol. 217(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:40:y:2020:i:8:p:1034-1040. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.