A hierarchical Bayesian approach to age-specific back-calculation of cancer incidence rates.

Published

Journal Article

We propose a Bayesian hierarchical model to estimate age-specific cancer incidence per year from age-specific cancer mortality. The model is based upon the empirical Bayesian approach of Liao and Brookmeyer (1995) and extends that model by consideration of the dependence on age. The incident cases per year are considered as observations from a discrete-time stochastic process following an autoregressive structure within a Poisson regression model. The model assumes that the survival probability among those with cancer is known. We have investigated the sensitivity of the model to the choice of this distribution and have found that this is the most sensitive part of the model. By comparison the predictions of the model are relatively robust to changes in other key areas, such as the number of years an incident case contributes before death, assumptions about parameter equality for identification and the initial prior distributions. The proposed methodology has been investigated using lung cancer mortality data from Scotland. Parameter estimates were obtained through Markov chain Monte Carlo methods, implemented using BUGS.

Full Text

Cited Authors

  • Mezzetti, M; Robertson, C

Published Date

  • April 1999

Published In

Volume / Issue

  • 18 / 8

Start / End Page

  • 919 - 933

PubMed ID

  • 10363331

Pubmed Central ID

  • 10363331

Electronic International Standard Serial Number (EISSN)

  • 1097-0258

International Standard Serial Number (ISSN)

  • 0277-6715

Digital Object Identifier (DOI)

  • 10.1002/(sici)1097-0258(19990430)18:8<919::aid-sim89>3.0.co;2-7

Language

  • eng