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

Author:

Yu Qingzhao1,Yu Mandi2,Zou Joe3,Wu Xiaocheng4,Gomez Scarlett L5,Li Bin6ORCID

Affiliation:

1. Biostatistics, Louisiana State University Health Sciences Center, New Orleans, LA, USA

2. Mathematical Statistician, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA

3. Information Management Services, Inc, Rockville, MD, USA

4. Epidemiology, Louisiana Tumor Registry, New Orleans, LA, USA

5. Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA

6. Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA, USA

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.

Funder

National Institute on Minority Health and Health Disparities

National Institute of Environmental Health Sciences

Publisher

SAGE Publications

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