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An Algorithm for Optimal Tapered Matching, With Application to Disparities in Survival
Shoshana R Daniel1, Katrina Armstrong1, Jeffrey H Silber1
1University of Pennsylvania, Philadelphia.
Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|January 13, 2015
Summary
This study introduces optimally tapered matching, a novel method for comparing groups with increasing resemblance. It addresses disparities in health outcomes, such as black-white survival differences in endometrial cancer.
Area of Science:
- Biostatistics
- Epidemiology
- Health Disparities Research
Background:
- Tapered matched comparisons are valuable for analyzing group differences.
- Existing methods may not optimally divide comparison populations or pair individuals.
- Understanding mechanisms behind health disparities requires robust comparative methods.
Purpose of the Study:
- To introduce and implement an optimally tapered matching method.
- To address the dual problem of optimally dividing a comparison population and optimally pairing individuals.
- To facilitate the study of health disparities, such as racial differences in cancer survival.
Main Methods:
- Utilized the optimal assignment algorithm in a novel application.
- Developed an implementation of optimally tapered matching in R.
- Applied the method to Medicare and SEER Program data for endometrial cancer survival analysis.
Main Results:
- Demonstrated a new approach to optimally tapered matching.
- Provided an R implementation for practical use.
- Facilitated the analysis of black-white survival disparities in endometrial cancer among women.
Conclusions:
- Optimally tapered matching offers a powerful tool for comparative studies.
- This method can enhance the understanding of mechanisms driving health disparities.
- The approach is applicable to diverse demographic groups and health outcomes.
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