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.

中文翻译：

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了解癌症预后的差异：中介分析延伸到相对生存框架


中介分析可用于研究第三个变量对暴露和结果之间路径的影响。此类应用包括研究导致不同亚组癌症生存差异的决定因素。然而，癌症差异可能是复杂机制的结果，涉及癌症相关死亡率和其他原因死亡率的差异，因此很难确定导致因素。相对生存率是癌症流行病学中常用的衡量标准，可用于关注与癌症相关的差异。我们将中介分析扩展到相对生存框架，以探索癌症不平等。在拟合相对生存模型后，使用回归标准化获得边际效应。利益对比包括暴露组之间的边际相对生存率和边际全因生存率差异。这种对比包括由于在某些假设下可识别的中介而产生的间接影响。为中介者拟合了一个单独的模型，并使用参数自举法估计了不确定性。还可以估计干预措施下可避免的死亡，以量化消除差异的影响。使用诊断为结肠癌的个体的数据来说明这些方法。相对生存率内的中介分析可以重点关注解释癌症相关差异的因素，而不是全因差异，并有助于提高我们对癌症不平等的理解。