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. 2019 Feb 10;38(3):398-412.
doi: 10.1002/sim.7977. Epub 2018 Sep 25.

Multiple mediation analysis with survival outcomes: With an application to explore racial disparity in breast cancer survival

Qingzhao Yu  1 , Xiaocheng Wu  2 , Bin Li  3 , Richard A Scribner  2
Affiliations

Multiple mediation analysis with survival outcomes: With an application to explore racial disparity in breast cancer survival

Qingzhao Yu et al. Stat Med. .

Abstract

Mediation analysis allows the examination of effects of a third variable in the pathway between an exposure and an outcome. The general multiple mediation analysis method, proposed by Yu et al, improves traditional methods (eg, estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. In this paper, we extend the method for time-to-event outcomes and apply the method to explore the racial disparity in breast cancer survivals. Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races. Despite improvement of survival rates of breast cancer in the US, a significant difference between white and black women remains. Previous studies have found that more advanced and aggressive tumors and less than optimal treatment may explain the lower survival rates for black women as compared to white women. Due to limitations of current analytic methods and the lack of comprehensive data sets, researchers have not been able to differentiate the relative effect each factor contributes to the overall racial disparity. We use the CDC-funded Patterns of Care study to examine the determinants of racial disparities in breast cancer survival using a novel multiple mediation analysis. Using the proposed method, we applied the Cox hazard model and multiple additive regression trees as predictive models and found that all racial disparity in survival among Louisiana breast cancer patients were explained by factors included in the study.

Keywords: mediation analysis; multiple additive regression trees; pattern of care; racial disparity; survival analysis.

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Figures

FIGURE 1
FIGURE 1
The conceptual model to explore the racial disparity in breast cancer survival
FIGURE 2
FIGURE 2
Relative effects from nonlinear models [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 3
FIGURE 3
Indirect effect of age on breast cancer hazard
FIGURE 4
FIGURE 4
Indirect effect of tumor size on breast cancer hazard
FIGURE 5
FIGURE 5
Indirect effect of cancer type on breast cancer hazard
FIGURE 6
FIGURE 6
Indirect effect of M on the predictor-outcome relationship
See this image and copyright information in PMC

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