Understanding disparities in cancer prognosis: An extension of mediation analysis to the relative survival framework. Issue 2 (14th December 2020)

• Record Type: Journal Article
• Authors: Syriopoulou, Elisavet; Rutherford, Mark J.; Lambert, Paul C.
• Is Part Of: Biometrical journal. Volume 63:Issue 2(2021)
• Page Start: 341
• Page End: 353
• Publication Date: 2020-12-14
• Subjects: cancer inequalities -- mediation analysis -- natural indirect effect -- regression standardization -- relative survival; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201900355
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract: Mediation analysis can be applied to investigate the effect of a third variable on the pathway between an exposure and the outcome. Such applications include investigating the determinants that drive differences in cancer survival across subgroups. However, cancer disparities may be the result of complex mechanisms that involve both cancer‐related and other‐cause mortality differences making it difficult to identify the causing factors. Relative survival, a commonly used measure in cancer epidemiology, can be used to focus on cancer‐related differences. We extended mediation analysis to the relative survival framework for exploring cancer inequalities. The marginal effects were obtained using regression standardization, after fitting a relative survival model. Contrasts of interests included both marginal relative survival and marginal all‐cause survival differences between exposure groups. Such contrasts include the indirect effect due to a mediator that is identifiable under certain assumptions. A separate model was fitted for the mediator and uncertainty was estimated using parametric bootstrapping. The avoidable deaths under interventions can also be estimated to quantify the impact of eliminating differences. The methods are illustrated using data for individuals diagnosed with colon cancer. Mediation analysis within relative survival allows focus on factors that account for cancer‐related differences instead of all‐cause differences and helps improve our understanding on cancer inequalities.