Bayesian mediation analysis methods to explore racial/ethnic disparities in anxiety among cancer survivors

Yu, Qingzhao; Cao, Wentao; Mercante, Donald E.; Wu, Xiao‐Cheng; Bin, Li

doi:10.1007/s41237-022-00185-9

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…We used all three of the Bayesian methods by Yu et al [18] with the study data to explore racial disparity in the diagnostic ages of breast cancer. Each mediator was fit with the predictor race using a Bayesian generalized linear model.…”

Section: Resultsmentioning

confidence: 99%

“…In this section, we briefly review the three Bayesian methods for mediation analysis [18]. The first method is based on the functions of estimated parameters, the second method is an extension of the product of coefficients method, and the third method is a counterfactual resampling method.…”

Section: Methodsmentioning

confidence: 99%

“…The first method is based on the functions of estimated parameters, the second method is an extension of the product of coefficients method, and the third method is a counterfactual resampling method. For simplicity, we assume there is only one exposure variable X, one outcome Y, and one potential third variable M. Detailed algorithms are provided in the Appendix of [18].…”

Section: Methodsmentioning

confidence: 99%

“…In Section 2, we introduce a data set that is used to explore racial disparity in breast cancer diagnostic ages. Then, in Section 3, we review three different Bayesian methods proposed by Yu et al [18] to estimate the mediation effects for continuous/binary exposure variables with multivariate outcomes. We apply these methods to explore the racial disparity in the diagnostic age of breast cancer patients in Louisiana, and the results are presented in Section 4.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer

Cao,

Hagan,

2024

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A mediation effect refers to the effect transmitted by a mediator intervening in the relationship between an exposure variable and a response variable. Mediation analysis is widely used to identify significant mediators and to make inferences on their effects. The Bayesian method allows researchers to incorporate prior information from previous knowledge into the analysis, deal with the hierarchical structure of variables, and estimate the quantities of interest from the posterior distributions. This paper proposes three Bayesian mediation analysis methods to make inferences on mediation effects. Our proposed methods are the following: (1) the function of coefficients method; (2) the product of partial difference method; and (3) the re-sampling method. We apply these three methods to explore racial disparities in the diagnostic age of breast cancer patients in Louisiana. We found that African American (AA) patients are diagnosed at an average of 4.37 years younger compared with Caucasian (CA) patients (57.40 versus 61.77, p< 0.0001). We also found that the racial disparity can be explained by patients’ insurance (12.90%), marital status (17.17%), cancer stage (3.27%), and residential environmental factors, including the percent of the population under age 18 (3.07%) and the environmental factor of intersection density (9.02%).

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer

Cao,

Hagan,

2024

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“…The MI-bootstrap pooled sample ("MI-boot PS") method 40 was used to estimate endpoints of the 95% confidence intervals (CIs) of the mediation effects. For each imputed dataset, 1000 datasets were In a secondary analysis, we used a multiple mediator method proposed by Yu et al 11,51 which estimates an averaged direct effect when controlling the mediators by simulating a random intervention on them according to their marginal distributions. This method uses a different approach to implement the counterfactuals to estimate indirect effects, but still uses hazard ratios from Cox models, as well as the "MI-boot PS" method for confidence intervals and standard errors.…”

Section: Sequential Mediation Analysismentioning

confidence: 99%

Racial disparities in epithelial ovarian cancer survival: An examination of contributing factors in the Ovarian Cancer in Women of African Ancestry consortium

Harris

Guertin

Camacho

et al. 2022

Intl Journal of Cancer

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Black women diagnosed with epithelial ovarian cancer have poorer survival compared to white women. Factors that contribute to this disparity, aside from socioeconomic status and guideline‐adherent treatment, have not yet been clearly identified. We examined data from the Ovarian Cancer in Women of African Ancestry (OCWAA) consortium which harmonized data on 1074 Black women and 3263 white women with ovarian cancer from seven US studies. We selected potential mediators and confounders by examining associations between each variable with race and survival. We then conducted a sequential mediation analysis using an imputation method to estimate total, direct, and indirect effects of race on ovarian cancer survival. Black women had worse survival than white women (HR = 1.30; 95% CI 1.16‐1.47) during study follow‐up; 67.9% of Black women and 69.8% of white women died. In our final model, mediators of this disparity include college education, nulliparity, smoking status, body mass index, diabetes, diabetes/race interaction, postmenopausal hormone (PMH) therapy duration, PMH duration/race interaction, PMH duration/age interaction, histotype, and stage. These mediators explained 48.8% (SE = 12.1%) of the overall disparity; histotype/stage and PMH duration accounted for the largest fraction. In summary, nearly half of the disparity in ovarian cancer survival between Black and white women in the OCWAA consortium is explained by education, lifestyle factors, diabetes, PMH use, and tumor characteristics. Our findings suggest that several potentially modifiable factors play a role. Further research to uncover additional mediators, incorporate data on social determinants of health, and identify potential avenues of intervention to reduce this disparity is urgently needed.

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Introduction to the Vol. 50, No. 1, 2023

Ueno

2023

Behaviormetrika

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Bayesian mediation analysis methods to explore racial/ethnic disparities in anxiety among cancer survivors

Cited by 4 publications

References 24 publications

Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer

Bayesian Mediation Analysis with an Application to Explore Racial Disparities in the Diagnostic Age of Breast Cancer

Racial disparities in epithelial ovarian cancer survival: An examination of contributing factors in the Ovarian Cancer in Women of African Ancestry consortium

Introduction to the Vol. 50, No. 1, 2023

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