The role of body mass index at diagnosis of colorectal cancer on Black–White disparities in survival: a density regression mediation approach. (24th September 2020)

• Record Type: Journal Article
• Authors: Devick, Katrina L; Valeri, Linda; Chen, Jarvis; Jara, Alejandro; Bind, Marie-Abèle; Coull, Brent A
• Is Part Of: Biostatistics. Volume 23:Number 2(2022)
• Page Start: 449
• Page End: 466
• Publication Date: 2020-09-24
• Subjects: Accelerated failure time model -- Cancer health disparities -- Causal inference -- Dependent Dirichlet process -- Nonparametric Bayesian -- Stochastic intervention; Medical statistics -- Periodicals; Biometry -- Periodicals; Health risk assessment -- Periodicals; Medicine -- Research -- Statistical methods -- Periodicals; 610.727
• DOI: 10.1093/biostatistics/kxaa034
• Languages: English
• ISSNs: 1465-4644

Abstract

Summary: 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.