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Updated: May 22, 2026
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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
One-to-many propensity score matching in cohort studies
Jeremy A Rassen1, Abhi A Shelat, Jessica Myers
1Division of Pharmacoepidemiology and Pharmacoeconomics; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. jrassen@post.harvard.edu
Pharmacoepidemiology and Drug Safety
|May 4, 2012
Summary
Increasing the matching ratio in cohort studies beyond 1:1 can enhance precision. A variable ratio, parallel, balanced 1:n nearest neighbor approach offers the best balance of precision and bias reduction.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Propensity score matching (PSM) is widely used in cohort studies, commonly employing a 1:1 matching ratio.
- Increasing the matching ratio (1:n) may improve study precision but can potentially introduce bias.
Purpose of the Study:
- To evaluate various propensity score matching methods in cohort studies.
- To assess the impact of different matching ratios and schemes on bias and precision through simulation and real-world data.
Main Methods:
- Simulated 20,000-patient cohorts with varying exposure prevalence and confounder types.
- Employed greedy, nearest neighbor, and balanced nearest neighbor matching techniques.
- Investigated fixed vs. variable ratios and sequential vs. parallel matching schemes, validated with administrative claims data.
Main Results:
- Higher matching ratios (beyond 1:1) generally increased bias.
- Variable ratio matching reduced variance compared to fixed ratios.
- Parallel matching schemes showed lower bias but higher mean squared error than sequential schemes.
- Variable ratio, parallel, balanced nearest neighbor matching demonstrated the lowest bias and mean squared error.
Conclusions:
- One-to-n (1:n) matching can enhance precision in cohort studies.
- A variable ratio, parallel, balanced 1:n nearest neighbor matching strategy is recommended.
- This approach improves precision over 1:1 matching with minimal increase in bias.
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