Modelling the progression towards duodenal cancer among patients with familial polyposis on the basis of two different score profiles

The objective was to design a method that considers, on clinical arguments, the likely existence of patient subgroups with different evolution profiles. The method is applied in familial adenomatous polyposis to predict the proportion of patients that would develop duodenal cancer. A subject-specific linear mixed-effects model was elaborated to explicitly model heterogeneity in regression parameters. The estimates of the parameters were obtained by Bayesian inference using Gibbs sampling. The application concerned two potential polyposis subgroups: stable-state and progressive. Each patient's score was expressed in function of his putative subgroup, the reference subgroup mean score (intercept), the rate of change (slope), and time. The estimated proportion of stable-state patients was 35%. In progressive-state patients, the estimated annual score increase was 0.38 (95% CI: 0.27-0.48). The regression model predicted that the proportion of patients with a score > or = 9 is near 43% at age 60 (36-50%) and 50% at 70 (43-57%). The method indicates the evolution profile of each subject, which facilitates therapeutic decisions. The modelling may be extended to other more complex situations with several subgroups, with different change rates, or with various genetic or therapeutic profiles.