Adjusting Expected Mortality Rates Using Information from a Control Population: An Example Using Socioeconomic Status

Expected or reference mortality rates are commonly used in the calculation of measures such as relative survival in population-based cancer survival studies and standardized mortality ratios. These expected rates are usually presented by age, sex and calendar year. In certain situations, stratification of expected rates by other factors is required to avoid potential bias if interest lies in quantifying measures by such factors, for example, socioeconomic status. If data on a population level are not available, information from a control population could be used to adjust expected rates. We present two approaches for adjusting expected mortality rates using information from a control population; a Poisson generalised linear model, and a flexible parametric survival model. We used a control group from BCBaSe, a Swedish register-based matched breast cancer cohort with diagnoses between 1992 and 2012, to illustrate the two methods using socioeconomic status as a risk factor of interest. Results showed that Poisson and flexible parametric survival approaches estimate similar adjusted mortality rates by socioeconomic status. Additional uncertainty involved in the methods to estimate stratified expected mortality rates described in this study can be accounted for using a parametric bootstrap, but may make little difference if using a large control population.