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Estimating country-specific incidence rates of rare cancers: comparative perfomance analysis of modeling approaches using European cancer registry data
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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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SALMERÓN, Diego, et al. Estimating country-specific incidence rates of rare cancers: comparative perfomance analysis of modeling approaches using European cancer registry data. American Journal of Epidemiology. 2022. Vol. 191, num. 3, pags. 487-498. ISSN 0002-9262. [consulted: 10 of October of 2026]. Available at: https://hdl.handle.net/2445/187901