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

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  • 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.

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. Asmik Nalmpatian & Christian Heumann & Stefan Pilz, 2024. "Forecasting Mortality Trends: Advanced Techniques and the Impact of COVID-19," Stats, MDPI, vol. 7(4), pages 1-17, October.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    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. Katrien Antonio & Anastasios Bardoutsos & Wilbert Ouburg, 2015. "Bayesian Poisson log-bilinear models for mortality projections with multiple populations," Working Papers Department of Accountancy, Finance and Insurance (AFI), Leuven 485564, KU Leuven, Faculty of Economics and Business (FEB), Department of Accountancy, Finance and Insurance (AFI), Leuven.
    11. 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.

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