LAUSR

Estimating incidence for rare cancers (RC) is challenging for exceptionally rare entities and in small populations. In a previous study, the RARECAREnet project provided Bayesian estimates of the expected number of RCs and their 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 INLA/WinBUGS platforms. This paper has assessed the performance of a Poisson modelling approach for estimating RC incidence rates, oscillating around an overall European average and using small counts 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 RCs for assessing different lower/upper bounds of a uniform prior distribution for the standard deviation of the random effects. To obtain a reliable measure of variability for country-specific incidence rates, our results suggest the suitability of using 1 as lower bound for that prior distribution and selecting the random effects model through an averaged indicator derived from two Bayesian model selection criterions, the Deviance and Watanabe-Akaike Information Criterions (DIC and WAIC, respectively).