IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v51y2002i2p151-164.html
   My bibliography  Save this article

Application of Markov chain Monte Carlo methods to projecting cancer incidence and mortality

Author

Listed:
  • Isabelle Bray

Abstract

Summary. Projections based on incidence and mortality data collected by cancer registries are important for estimating current rates in the short term, and public health planning in the longer term. Classical approaches are dependent on questionable parametric assumptions. We implement a Bayesian age–period–cohort model, allowing the inclusion of prior belief concerning the smoothness of the parameters. The model is described by a directed acyclic graph. Computations are carried out by using Markov chain Monte Carlo methods (implemented in BUGS) in which the degree of smoothing is learnt from the data. Results and convergence diagnostics are discussed for an exemplary data set. We then compare the Bayesian projections with other methods in a range of situations to demonstrate its flexibility and robustness.

Suggested Citation

  • Isabelle Bray, 2002. "Application of Markov chain Monte Carlo methods to projecting cancer incidence and mortality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 51(2), pages 151-164, May.
    • Handle: RePEc:bla:jorssc:v:51:y:2002:i:2:p:151-164
    DOI: 10.1111/1467-9876.00260
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9876.00260
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-9876.00260?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
    ---><---

    Citations

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


    Cited by:

    1. Yang, Bowen & Li, Jackie & Balasooriya, Uditha, 2015. "Using bootstrapping to incorporate model error for risk-neutral pricing of longevity risk," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 16-27.
    2. Kyoji Furukawa & Munechika Misumi & John B. Cologne & Harry M. Cullings, 2016. "A Bayesian Semiparametric Model for Radiation Dose‐Response Estimation," Risk Analysis, John Wiley & Sons, vol. 36(6), pages 1211-1223, June.
    3. Irene O L Wong & Benjamin J Cowling & Gabriel M Leung & C Mary Schooling, 2012. "Trends in Mortality from Septicaemia and Pneumonia with Economic Development: An Age-Period-Cohort Analysis," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-7, June.
    4. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," BAFFI CAREFIN Working Papers 1505, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    5. Carl Schmertmann & Emilio Zagheni & Joshua R. Goldstein & Mikko Myrskylä, 2014. "Bayesian Forecasting of Cohort Fertility," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 500-513, June.
    6. Kyoji Furukawa & John B. Cologne & Yukiko Shimizu & N. Phillip Ross, 2009. "Predicting Future Excess Events in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 29(6), pages 885-899, June.
    7. Volker Schmid & Leonhard Held, 2004. "Bayesian Extrapolation of Space–Time Trends in Cancer Registry Data," Biometrics, The International Biometric Society, vol. 60(4), pages 1034-1042, December.
    8. Kogure, Atsuyuki & Kurachi, Yoshiyuki, 2010. "A Bayesian approach to pricing longevity risk based on risk-neutral predictive distributions," Insurance: Mathematics and Economics, Elsevier, vol. 46(1), pages 162-172, February.
    9. Irene O L Wong & Benjamin J Cowling & Gabriel M Leung & C Mary Schooling, 2013. "Age-Period-Cohort Projections of Ischaemic Heart Disease Mortality by Socio-Economic Position in a Rapidly Transitioning Chinese Population," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-8, April.
    10. Giulia Carreras & Giuseppe Gorini, 2013. "Time Trends of Italian Former Smokers 1980–2009 and 2010–2030 Projections Using a Bayesian Age Period Cohort Model," IJERPH, MDPI, vol. 11(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    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:bla:jorssc:v:51:y:2002:i:2:p:151-164. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.