[Statistical models and their application in descriptive epidemiology of cancer]

There are some limitations or disadvantages of statistical methods traditionally used in descriptive epidemiology of cancer. It can not handle the true relationships of several variables under srudy, and the effectiveness of a variable may often be confounded by other variables. This paper describes two kinds of multivariable regression models frequently used in descriptive epidemiology of cancer, such as the age-period-cohort (APC) model for the analysis of cancer incidence or mortality rate and the relative survival (RSR) model for the analysis of cancer survival rate. Detailed statistical methods, model fitting, parameter estimation, etc., are presented and two examples are used for illustration using data sets of oesophageal and stomach cancers diagnosed in urban Shanghai. The advantages of multivariable regression models are able to adjust effectiveness of confounder factors, and give estimations and evaluations of adjusted relative risks for the population.