Evidence map›Paper›PMID 30255567›Full record

ArticleStatistics in medicine2019

Multiple mediation analysis with survival outcomes: With an application to explore racial disparity in breast cancer survival.

Qingzhao Yu, Xiaocheng Wu, Bin Li, Richard A Scribner

Abstract read
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Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.

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59citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

59 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Qingzhao YuBiostatistics, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana.ORCID 0000-0001-8194-0798
Xiaocheng WuEpidemiology, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana.
Bin LiDepartment of Experimental Statistics, Louisiana State University, Baton Rouge, Louisiana.
Richard A ScribnerEpidemiology, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, Louisiana.

Funding

Trends of disparities in breast cancer progression and health care considering multilevel risk factorsR15MD012387 · NIMHD · LSU HEALTH SCIENCES CENTER · PI YU, QINGZHAO · 2017 to 2023
$1.1M
NIMHD NIH HHS R15 MD012387
6 · The paper itself

Abstract

Mediation analysis allows the examination of effects of a third variable in the pathway between an exposure and an outcome. The general multiple mediation analysis method, proposed by Yu et al, improves traditional methods (eg, estimation of natural and controlled direct effects) to enable consideration of multiple mediators/confounders simultaneously and the use of linear and nonlinear predictive models for estimating mediation/confounding effects. In this paper, we extend the method for time-to-event outcomes and apply the method to explore the racial disparity in breast cancer survivals. Breast cancer is the most common cancer and the second leading cause of cancer death among women of all races. Despite improvement of survival rates of breast cancer in the US, a significant difference between white and black women remains. Previous studies have found that more advanced and aggressive tumors and less than optimal treatment may explain the lower survival rates for black women as compared to white women. Due to limitations of current analytic methods and the lack of comprehensive data sets, researchers have not been able to differentiate the relative effect each factor contributes to the overall racial disparity. We use the CDC-funded Patterns of Care study to examine the determinants of racial disparities in breast cancer survival using a novel multiple mediation analysis. Using the proposed method, we applied the Cox hazard model and multiple additive regression trees as predictive models and found that all racial disparity in survival among Louisiana breast cancer patients were explained by factors included in the study.

Indexed as

Survival AnalysisAlgorithmsBlack or African AmericanBreast NeoplasmsFemaleHealthcare DisparitiesHumansLinear ModelsLouisianaModels, StatisticalNonlinear DynamicsProportional Hazards ModelsRacial GroupsSocioeconomic FactorsTreatment OutcomeUnited Statesmediation analysismultiple additive regression treespattern of careracial disparitysurvival analysis

Identifiers

PMID30255567
PMCPMC6320301

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.