Multilevel mediation analysis on time-to-event outcomes: Exploring racial/ethnic disparities in breast cancer survival in California.

Qingzhao Yu, Mandi Yu, Joe Zou, Xiaocheng Wu, Scarlett L Gomez, Bin Li
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Abstract

Background: Third-variable effect refers to the effect from a third-variable that explains an observed relationship between an exposure and an outcome. Depending on whether there is a causal relationship from the exposure to the third variable, the third-variable is called a mediator or a confounder. The multilevel mediation analysis is used to differentiate third-variable effects from data of hierarchical structures.

Data collection and analysis: We developed a multilevel mediation analysis method to deal with time-to-event outcomes and implemented the method in the mlma R package. With the method, third-variable effects from different levels of data can be estimated. The method uses multilevel additive models that allow for transformations of variables to take into account potential nonlinear relationships among variables in the mediation analysis. We apply the proposed method to explore the racial/ethnic disparities in survival among patients diagnosed with breast cancer in California between 2006 and 2017, using both individual risk factors and census tract level environmental factors. The individual risk factors are collected by cancer registries and the census tract level factors are collected by the Public Health Alliance of Southern California in partnership with the Virginia Commonwealth University's Center on Society and Health. The National Cancer Institute work group linked variables at the census tract level with each patient and performed the analysis for this study.

Results: We found that the racial disparity in survival were mostly explained at the census tract level and partially explained at the individual level. The associations among variables were depicted. Conclusion: The multilevel mediation analysis method can be used to differentiate mediation/confounding effects for factors originated from different levels. The method is implemented in the R package mlma.

Abstract Image

Abstract Image

时间到事件结果的多层次中介分析:探索加州乳腺癌生存的种族/民族差异
背景:第三变量效应是指解释观察到的暴露与结果之间关系的第三变量效应。根据暴露于第三个变量之间是否存在因果关系,第三个变量被称为中介或混杂因素。多层次中介分析用于区分层次结构数据中的第三变量效应。数据收集和分析:我们开发了一种多层中介分析方法来处理时间到事件的结果,并在mlma R包中实现了该方法。利用该方法,可以估计来自不同数据水平的第三变量效应。该方法使用多层加性模型,允许变量转换,以考虑中介分析中变量之间潜在的非线性关系。我们应用所提出的方法,利用个体风险因素和人口普查区水平的环境因素,探讨2006年至2017年间加利福尼亚州乳腺癌患者的生存种族/民族差异。个人风险因素由癌症登记处收集,人口普查区水平的因素由南加州公共卫生联盟与弗吉尼亚联邦大学社会与健康中心合作收集。国家癌症研究所工作小组将人口普查区水平的变量与每位患者联系起来,并对这项研究进行了分析。结果:我们发现,种族生存差异主要在人口普查区水平上得到解释,部分在个体水平上得到解释。描述了变量之间的关联。结论:多层次中介分析方法可以区分不同层次因素的中介/混杂效应。该方法在R包mlma中实现。
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