An extension of mediation analysis to the relative survival framework

Mediation analysis can be applied to investigate the role of a third variable (a so-called mediator) on the pathway between an exposure and the outcome of interest through an effect decomposition. For example, it would allow exploring if and how much of the socioeconomic disparities observed in cancer survival can be explained by differences in stage at diagnosis. Here we describe an extension of mediation analysis within the relative survival framework. Relative survival is a commonly used measure in cancer epidemiology that estimates net survival, and its incorporation into mediation analysis allows focusing on disease-related survival differences as opposed to all-cause survival differences. The latter is the result of both cancer-related and other factors that are more challenging to identify. Using mediation analysis in relative survival and counterfactual survival functions, we can partition the difference in marginal relative survival between exposure groups into the difference due to a mediator (such as stage at diagnosis, which has an indirect effect) and the remaining difference (due to the exposure, a direct effect). The proportion mediated can also be obtained together with contrasts of all-cause survival differences as well as the number of “avoidable deaths” under interventions aimed at modifiable risk factors. We illustrate how all of these measures of interest can be estimated and introduce certain assumptions required to do so. To that end, we fit a flexible parametric survival model for the survival outcome using the stpm3 command and a separate multinomial logistic model for the mediator. We then combine these models using a regression standardization approach, implemented within the standsurv command, with uncertainty estimated using a parametric bootstrap procedure. We will illustrate the approach in practice using an example on survival differences in rectal cancer survival by income groups in Sweden. Finally, the methods that we developed could be applied in other disease areas as well, where exploring the underlying mechanisms for disease-specific survival differences is of interest. Mediation analysis in the relative survival framework provides thus a valuable tool that has the potential to improve our understanding of factors driving health disparities and informing policies aimed at modifiable mediators (risk factors).