Literature DB >> 32968805

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

Katrina L Devick1, Linda Valeri2, Jarvis Chen3, Alejandro Jara4, Marie-Abèle Bind5, Brent A Coull6.   

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.
© The Author 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Accelerated failure time model; Cancer health disparities; Causal inference; Dependent Dirichlet process; Nonparametric Bayesian; Stochastic intervention

Mesh:

Year:  2022        PMID: 32968805      PMCID: PMC9016785          DOI: 10.1093/biostatistics/kxaa034

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.279


  36 in total

1.  Causal Isotonic Regression.

Authors:  Ted Westling; Peter Gilbert; Marco Carone
Journal:  J R Stat Soc Series B Stat Methodol       Date:  2020-05-13       Impact factor: 4.488

2.  Interventional Effects for Mediation Analysis with Multiple Mediators.

Authors:  Stijn Vansteelandt; Rhian M Daniel
Journal:  Epidemiology       Date:  2017-03       Impact factor: 4.822

3.  Colorectal cancer statistics, 2014.

Authors:  Rebecca Siegel; Carol Desantis; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2014-03-17       Impact factor: 508.702

4.  Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible.

Authors:  Judith J Lok
Journal:  Stat Med       Date:  2016-05-26       Impact factor: 2.373

5.  Illustrating a "consequential" shift in the study of health inequalities: a decomposition of racial differences in the distribution of body mass.

Authors:  Arjumand Siddiqi; Faraz Vahid Shahidi; Vincent Hildebrand; Anthony Hong; Sanjay Basu
Journal:  Ann Epidemiol       Date:  2018-02-15       Impact factor: 3.797

6.  Causal mediation analysis with survival data.

Authors:  Tyler J VanderWeele
Journal:  Epidemiology       Date:  2011-07       Impact factor: 4.822

7.  Prevalence of overweight and obesity in the United States, 1999-2004.

Authors:  Cynthia L Ogden; Margaret D Carroll; Lester R Curtin; Margaret A McDowell; Carolyn J Tabak; Katherine M Flegal
Journal:  JAMA       Date:  2006-04-05       Impact factor: 56.272

8.  Relationship of prediagnostic body mass index with survival after colorectal cancer: Stage-specific associations.

Authors:  Jonathan M Kocarnik; Andrew T Chan; Martha L Slattery; John D Potter; Jeffrey Meyerhardt; Amanda Phipps; Hongmei Nan; Tabitha Harrison; Thomas E Rohan; Lihong Qi; Lifang Hou; Bette Caan; Candyce H Kroenke; Howard Strickler; Richard B Hayes; Robert E Schoen; Dawn Q Chong; Emily White; Sonja I Berndt; Ulrike Peters; Polly A Newcomb
Journal:  Int J Cancer       Date:  2016-05-14       Impact factor: 7.396

9.  Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes.

Authors:  Wenjing Zheng; Mark van der Laan
Journal:  J Causal Inference       Date:  2017-06-23

Review 10.  The obesity epidemic in the United States--gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis.

Authors:  Youfa Wang; May A Beydoun
Journal:  Epidemiol Rev       Date:  2007-05-17       Impact factor: 6.222

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