Publication: Estimating Country-Specific Incidence Rates of Rare Cancers: Comparative Performance Analysis of Modeling Approaches Using European Cancer Registry Data
Authors
Salmerón, D ; Botta, L ; Martínez, JM ; Trama, A ; Gatta, G ; Borràs, J ; Capocaccia, R ; Clèries, R
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Publisher
Oxford University Press Inc
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DOI
https://doi.org/10.1093/aje/kwab262
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info:eu-repo/semantics/article
Description
© 2021. The authors. This document is made available under the CC-BY-NC 4.0 license http://creativecommons.org/licenses/by-nc /4.0/
This document is the submitted version of a published work that appeared in final form in
American Journal of Epidemiology.
Abstract
Estimating incidence of rare cancers is challenging for exceptionally rare entities and in small populations. In
a previous study, investigators in the Information Network on Rare Cancers (RARECARENet) provided Bayesian
estimates of expected numbers of rare cancers and 95% credible intervals for 27 European countries, using data
collected by population-based cancer registries. In that study, slightly different results were found by implementing
a Poisson model in integrated nested Laplace approximation/WinBUGS platforms. In this study, we assessed
the performance of a Poisson modeling approach for estimating rare cancer incidence rates, oscillating around
an overall European average and using small-count data in different scenarios/computational platforms. First,
we compared the performance of frequentist, empirical Bayes, and Bayesian approaches for providing 95%
confidence/credible intervals for the expected rates in each country. Second, we carried out an empirical study
using 190 rare cancers to assess different lower/upper bounds of a uniform prior distribution for the standard
deviation of the random effects. For obtaining a reliable measure of variability for country-specific incidence
rates, our results suggest the suitability of using 1 as the lower bound for that prior distribution and selecting
the random-effects model through an averaged indicator derived from 2 Bayesian model selection criteria: the
deviance information criterion and the Watanabe-Akaike information criterion.
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Citation
American Journal of Epidemiology, Volume 191, Issue 3, March 2022, Pages 487–498
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