IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v31y2011i4p625-641.html
   My bibliography  Save this article

Bayesian Calibration of a Natural History Model with Application to a Population Model for Colorectal Cancer

Author

Listed:
  • Sophie Whyte
  • Cathal Walsh
  • Jim Chilcott

Abstract

Background . Cancer natural history models are essential when evaluating screening/preventative interventions or changes to diagnostic pathways. Natural history models commonly use a state transition structure, but it is often not possible to observe the state transition probabilities required for parameterization. Aim . The work aimed to accurately represent the uncertainty in the parameters of a state transition model for the natural history of colorectal cancer by embedding the problem in the framework of Bayesian inference. Methods . The Metropolis-Hastings algorithm was used to estimate natural history parameters and screening test characteristics by generating multiple sets of parameters from the posterior distribution, which is the probability distribution that is compatible with the observed data. Observed data included colorectal cancer incidence categorized by age and stage, autopsy data on polyp prevalence, and cancer and polyp detection rates from the first round of screening with the fecal occult blood test in England. The approach was implemented using Visual Basic. Results . The results were subsequently examined for convergence using the package CODA in R 2.8.0. Outputs from fitting were samples from the joint posterior distribution of the natural history parameters given the epidemiological data. The parameter sets obtained are shown to have a good fit to all the observed data sets. These parameter sets are used when running probabilistic sensitivity analysis. Conclusion . The advantages of this strategy are that it draws efficiently from a high-dimensional correlated parameter space. The algorithm is simple to code and runs overnight on a standard desktop PC. Using this method, the parameter sets are drawn according to their posterior probability given calibration data, and thus they correctly summarize the residual uncertainty in the parameter space.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:medema:v:31:y:2011:i:4:p:625-641
    DOI: 10.1177/0272989X10384738
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X10384738
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X10384738?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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miqdad Asaria & Susan Griffin & Richard Cookson & Sophie Whyte & Paul Tappenden, 2013. "Distributional Cost-Effectiveness Analysis of Health Care Programmes," Working Papers 091cherp, Centre for Health Economics, University of York.
    2. Miqdad Asaria & Susan Griffin & Richard Cookson & Sophie Whyte & Paul Tappenden, 2015. "Distributional Cost‐Effectiveness Analysis of Health Care Programmes – A Methodological Case Study of the UK Bowel Cancer Screening Programme," Health Economics, John Wiley & Sons, Ltd., vol. 24(6), pages 742-754, June.
    3. Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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:31:y:2011:i:4:p:625-641. 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.