Bayesian modelling of imperfect ascertainment methods in cancer studies.

Tumour registry linkage, chart review and patient self-report are all commonly used ascertainment methods in cancer epidemiology. These methods are used for estimating the incidence or prevalence of different cancer types in a population, and for investigating the effects of possible risk factors for cancer. Tumour registry linkage is often treated as a gold standard, but in fact none of these methods is error free, and failure to adjust for imperfect ascertainment can lead to biased estimates. This is true both if the goal of the study is to estimate the properties of each ascertainment type, or if it is to estimate cancer incidence or prevalence from one or more of these methods. Although rarely applied in the literature to date, when cancer is ascertained by three or more methods, standard latent class models can be used to estimate cancer incidence or prevalence while adjusting for the estimated imperfect sensitivities and specificities of each ascertainment method. These models, however, do not account for variations in these properties across different cancer sites. To address this problem, we extend latent class methodology to include a hierarchical component, which accommodates different ascertainment properties across cancer sites. We apply our model to a data set of 169 lupus patients with three ascertainment methods and eight cancer types. This allows us to estimate the properties of each ascertainment method without assuming any to be a gold standard, and to calculate a standardized incidence ratio for cancer for lupus patients compared to the general population. As our data set is small, we also illustrate the effects as more data become available. We show that our model produces parameter estimates that are substantially different from the currently most popular method of ascertainment, which uses tumour registry data alone.