ArticleAmerican journal of epidemiology2020
Evaluating the Utility of Coarsened Exact Matching for Pharmacoepidemiology Using Real and Simulated Claims Data.
Article in American journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 96 citations in OpenAlex.
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- Post-matching analysis after coarsened exact matching: implications of coarsening for residual confounding and model dependence.Research square · 2026Article
- Examining the Efficacy of Coarsened Exact Matching as an Alternative to Propensity Score Matching.Statistics in medicine · 2026Article
- Edentulism and risk of all-cause dementia and Alzheimer's disease: A competing risk analysis by cognitive status.Journal of Alzheimer's disease : JAD · 2026Article
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- Hospice care for Medicaid cancer patients in Puerto Rico: implications on healthcare costs and utilization.JNCI cancer spectrum · 2026Article
- Orthopedic surgery after selective dorsal rhizotomy in children with cerebral palsy: A matched cohort study.Developmental medicine and child neurology · 2026Article
- Effectiveness of the 2024-2025 KP.2 COVID-19 vaccines in the United States during long-term follow-up.Nature communications · 2025Article
- Burden of Atopic Dermatitis in Patients Initiating Systemic Therapies in the United States.Advances in therapy · 2025Article
- Revision hip arthroscopy for hip synovial chondromatosis is effective despite inferior postoperative clinical outcomes compared to patients undergoing primary hip arthroscopy: a matched control study with minimum 2-year follow-up.Journal of orthopaedic surgery and research · 2024Article
- A comparison of time-varying propensity score vs sequential stratification approaches to longitudinal matching with a time-varying treatment.BMC medical research methodology · 2024Article
- Secondary Prevention via Case Managers in Stroke Patients: A Cost-Effectiveness Analysis of Claims Data from German Statutory Health Insurance Providers.Healthcare (Basel, Switzerland) · 2024Article
- Traumatic Cervical Spinal Cord Injury and Income and Employment Status.JAMA network open · 2024Article
- Comparison of two propensity score-based methods for balancing covariates: the overlap weighting and fine stratification methods in real-world claims data.BMC medical research methodology · 2024Article
- Lifestyle and risk factors associated with elevated prostate-specific antigen levels in rural men: implications for health counseling.Frontiers in oncology · 2024Article
- Outcomes of Hip Arthroscopy in the Setting of Concomitant Symptomatic Lumbosacral Spine Pathology: A Matched Control Study With Minimum 24-Month Follow-up.The American journal of sports medicine · 2023Article
- An Integrated Patient-Centred Medical Home (PCMH) Care Model Reduces Prospective Healthcare Utilisation for Community-Dwelling Older Adults with Complex Needs: A Matched Observational Study in Singapore.International journal of environmental research and public health · 2023Observational
- Comparison of metaraminol versus no metaraminol on time to resolution of shock in critically ill patients.European journal of hospital pharmacy : science and practice · 2023Observational
- The 'welcomed lockdown' hypothesis? Mental wellbeing and mobility restrictions.The European journal of health economics : HEPAC : health economics in prevention and care · 2023Article
- Design and analysis of outcomes following SARS-CoV-2 infection in veterans.BMC medical research methodology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Coarsened exact matching (CEM) is a matching method proposed as an alternative to other techniques commonly used to control confounding. We compared CEM with 3 techniques that have been used in pharmacoepidemiology: propensity score matching, Mahalanobis distance matching, and fine stratification by propensity score (FS). We evaluated confounding control and effect-estimate precision using insurance claims data from the Pharmaceutical Assistance Contract for the Elderly (1999-2002) and Medicaid Analytic eXtract (2000-2007) databases (United States) and from simulated claims-based cohorts. CEM generally achieved the best covariate balance. However, it often led to high bias and low precision of the risk ratio due to extreme losses in study size and numbers of outcomes (i.e., sparse data bias)-especially with larger covariate sets. FS usually was optimal with respect to bias and precision and always created good covariate balance. Propensity score matching usually performed almost as well as FS, especially with higher index exposure prevalence. The performance of Mahalanobis distance matching was relatively poor. These findings suggest that CEM, although it achieves good covariate balance, might not be optimal for large claims-database studies with rich covariate information; it might be ideal if only a few (<10) strong confounders must be controlled.
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