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Related Experiment Video

Updated: May 20, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Exploring robust methods for evaluating treatment and comparison groups in chronic care management programs.

Aaron R Wells1, Brent Hamar, Chastity Bradley

  • 1Center for Health Research, Healthways, Inc., Franklin, Tennessee 37067, USA. aaron.wells@healthways.com

Population Health Management
|July 14, 2012
PubMed
Summary
This summary is machine-generated.

Coarsened Exact Matching (CEM) offers a more accurate and less biased method for evaluating chronic care management (CCM) programs compared to Propensity Score Matching (PSM). CEM resulted in greater cost savings and better group balance in a recent health plan study.

Related Experiment Videos

Last Updated: May 20, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Health Services Research
  • Biostatistics
  • Health Economics

Background:

  • Evaluating chronic care management (CCM) programs requires robust methods to assess behavioral, clinical, and financial impacts.
  • Quasi-experimental designs comparing intervention (treatment) and non-intervention (comparison) groups are common but prone to bias due to non-random assignment.
  • Propensity Score Matching (PSM) and Coarsened Exact Matching (CEM) are techniques to balance groups in such studies.

Purpose of the Study:

  • To comprehensively compare the effectiveness of PSM and CEM in evaluating a CCM program.
  • To assess which matching method yields less bias and variance in causal effect estimates.
  • To determine the financial outcomes associated with CCM program evaluation using both methods.

Main Methods:

  • A case study evaluating a CCM program over two years in a large health plan.
  • Descriptive and statistical methods were used to assess pre-matching balance between treatment and comparison groups.
  • Comparison of PSM and CEM in terms of member retention, group balance, and statistical significance of group aggregation.

Main Results:

  • CEM retained more members and achieved better balance between matched groups compared to PSM.
  • CEM resulted in a statistically insignificant Wald test statistic for group aggregation, indicating superior balance.
  • CCM program members matched using CEM showed higher medical cost savings (-$25.57) than those matched using PSM (-$19.78).

Conclusions:

  • Coarsened Exact Matching (CEM) is a statistically superior method for balancing groups in CCM program evaluations.
  • CEM offers a viable, potentially more appropriate, alternative to PSM for reducing bias and variance in causal effect estimation.
  • The findings support the use of CEM for more accurate assessments of CCM program financial performance.