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    • br Methods and setting The

    2018-11-05


    Methods and setting The data from all Norwegian women aged 50–69 who were diagnosed with breast cancer between 1999 and 2008 were collected for this study. Patients were followed until death (from any cause) or latest date of follow-up as of December 31, 2009 (maximum follow-up of 11 years). The death from any cause approach may be regarded as conservative, since this includes deaths that are possibly unrelated to breast cancer (Cuzick, 2008). During the sample period, 15 out of 19 counties in Norway implemented the program; 4 counties had already implemented the program in 1995/1996. The county specific implementation sequence and the Sunitinib of data collection can be seen in Table 1. The introduction was not randomized and took place according to administrative considerations. The patients were analyzed according to the county in which they lived in the year when diagnosed with breast cancer. Information from the cancer registry, which is 99.95% complete for female breast cancer patients in Norway (Larsen et al., 2009), was linked with information on SES as well as time and cause of death from Statistics Norway. All analyses were adjusted for age at diagnosis, year of diagnosis, civil status, and parity. Civil status and parity were included because both variables reflect additional resources within the household, and parity has been found to be related to breast cancer incidence, survival, and SES (Lappegård et al., 2005; Menvielle et al., 2011). Year of diagnosis was included to capture secular trends in the mortality rates over the data collection period. The continuous variables age at diagnosis and year of diagnosis were entered linearly. Nulliparity was entered as a binary variable. Civil status was entered as a categorical variable (unmarried, married, widowed, separated/divorced). The introduction of the NBCSP (time period) was entered as a binary variable to measure any immediate change as well as a continuous variable (counting the time since county-specific introduction) to capture linear trends in time after introduction reflecting the structure of an interrupted time series analysis (Penfold & Zhang, 2013). In the current study mediation was estimated by turning to natural effect models for direct and indirect effects using a counterfactual framework and marginal structural models, as implemented by Lange, Vansteelandt, and Bekaert (2012). As defined by Nordahl et al. (2014), and taking education as an example of the exposure variable, the natural direct effect considers the difference in mortality between the levels of an exposure (level of education, say) where the mediator (cancer stage) is that of the reference education level (primary school). The natural indirect effect of education on mortality is then represented by the mortality rate had education been fixed at a given level and had cancer stage been changed to whatever value cancer stage would take at that level of education. To implement this definition of direct and indirect effects in the same analysis, a multinomial logit model for cancer stage was estimated conditioning on all variables to preserve the model structure in a given final analysis (Lange et al., 2012). Subsequently, a new dataset was constructed that included an auxiliary variable for the indirect effect of education on cancer stage, replicating the data one time for each of the three levels of education. Based on the multinomial logit model, predicted probabilities for each cancer stage level were obtained, first using the original education variable to represent the direct effect and thereafter using the new auxiliary education variable to represent the indirect effect. These sets of predicted probabilities gave the denominator and numerator, respectively, for the weights of the marginal structural model. While the above text exemplified the implementation of the analysis with education as the exposure (i.e. the between-education group analysis), a similar interpretation can be given to the analysis that considers time period as the exposure. However, for this within-education group analysis the auxiliary variable for the indirect effects were the replications for each level of time period, i.e. before and after screening introduction. Across all the multinomial logit models no weight for any single observation was exceedingly large, and none of the estimated weights were beyond 2.5 times larger or smaller than 1. The details of these estimated models are given in the Appendix separately for the two main aims of the study (Appendices A and B).