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Devick KL, Valeri L, Chen J, Jara A, Bind MA, Coull BA. The role of body mass index at diagnosis of colorectal cancer on Black-White disparities in survival: a density regression mediation approach. Biostatistics 2020; 23:449-466. [PMID: 32968805 PMCID: PMC9016785 DOI: 10.1093/biostatistics/kxaa034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 08/07/2020] [Accepted: 08/11/2020] [Indexed: 11/14/2022] Open
Abstract
The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether differences in BMI are partially responsible for observed racial/ethnic disparities in survival for CRC patients. This article presents new methodology to quantify the impact of the hypothetical intervention that matches the BMI distribution in the Black population to a potentially complex distributional form observed in the White population on racial/ethnic disparities in survival. Our density mediation approach can be utilized to estimate natural direct and indirect effects in the general causal mediation setting under stronger assumptions. We perform a simulation study that shows our proposed Bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing BMI leads to large biases when BMI is a mediator variable. When applied to motivating data from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low-income Black patients, yet harmful for young or high-income Black populations.
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Affiliation(s)
- Katrina L Devick
- Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 13400 E. Shea Blvd., Scottsdale, AZ 85259, USA
- To whom correspondence should be addressed.
| | - Linda Valeri
- Department of Biostatistics, Columbia Mailman School of Public Health, 722 W. 168th Street, Room 612, New York, NY 10032, USA
| | - Jarvis Chen
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Landmark Center, Room 403-N, West Wing, Boston, MA 02215, USA
| | - Alejandro Jara
- Department of Statistics, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile and Millennium Nucleus Center for the Discovery of Structures in Complex Data, Faculty of Mathematics UC, Campus San Joaquin, Vicuña Mackenna 4860, Macul, Chile
| | - Marie-Abèle Bind
- Department of Statistics, Harvard University, One Oxford Street, Suite 400, Cambridge, MA, USA
| | - Brent A Coull
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA 02115, USA
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