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Updated: Jun 1, 2025
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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
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Bayesian Mediation Analysis for Time-to-Event Outcome: Investigating Racial Disparity in Breast Cancer Survival
Qingzhao Yu1, Wentao Cao1, Donald Mercante1
1Biostatistics, LSU Health-New Orleans.
Communications in Statistics: Theory and Methods
|January 20, 2025
Summary
Bayesian mediation analysis (BMA) offers new methods for understanding survival outcomes. These techniques successfully explained racial disparities in breast cancer survival rates using SEER data.
Area of Science:
- Biostatistics
- Epidemiology
- Medical Statistics
Background:
- Mediation analysis infers mediator effects between exposure and outcome.
- Bayesian mediation analysis (BMA) models hierarchical exposure-mediator-outcome relationships.
- Survival outcomes present unique challenges for mediation analysis.
Purpose of the Study:
- To propose three novel BMA methods for survival outcomes.
- To measure mediation effects using hazard rate, survival time, or log survival time.
- To incorporate limited survival time in time-to-event analyses.
Main Methods:
- Development of three Bayesian mediation analysis methods for survival data.
- Validation through simulation studies assessing estimation precision.
- Application to Surveillance, Epidemiology, and End Results (SEER) Program data.
Main Results:
- The proposed BMA methods provide effective estimates for survival outcomes.
- Simulations demonstrate good estimation precision across various scenarios.
- Analysis of SEER data revealed a variable that fully explained racial disparities in breast cancer survival.
Conclusions:
- The developed BMA methods are effective for analyzing survival outcomes.
- These methods can elucidate complex relationships, including health disparities.
- Visual aids enhance the interpretation of mediation analysis results for survival data.
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