The role of body mass index at diagnosis of colorectal cancer on Black–White disparities in survival: a density regression mediation approach

Author:

Devick Katrina L1,Valeri Linda2,Chen Jarvis3,Jara Alejandro4,Bind Marie-Abèle5,Coull Brent A6

Affiliation:

1. Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 13400 E. Shea Blvd., Scottsdale, AZ 85259, USA

2. Department of Biostatistics, Columbia Mailman School of Public Health, 722 W. 168th Street, Room 612, New York, NY 10032, USA

3. 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

4. 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

5. Department of Statistics, Harvard University, One Oxford Street, Suite 400, Cambridge, MA, USA

6. Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Building 2, 4th Floor, Boston, MA 02115, USA

Abstract

SummaryThe 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.

Funder

National Institutes of Health

John Harvard Distinguished Science Fellow Program within in the FAS Division of Science of Harvard University

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Reference46 articles.

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Causal Mediation Analysis for an Ordinal Outcome with Multiple Mediators;Structural Equation Modeling: A Multidisciplinary Journal;2023-09-15

2. On the causal interpretation of randomised interventional indirect effects;Journal of the Royal Statistical Society Series B: Statistical Methodology;2023-06-28

3. A multistate approach for the study of interventions on an intermediate time-to-event in health disparities research;Statistical Methods in Medical Research;2023-04-20

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