Comparing Entropy Balancing & IPTW for Cohort Balancing: Case Studies from Real-World Claims Data

Music City Center 

In retrospective studies, inverse probability treatment weighting (IPTW) and entropy balancing (EB) help achieve covariate balance and reduce confounding. This study compared these two methods using Merative claims data (2006-2024). Three patient cohort groups were balanced on age, sex, insurance type, region and Elixhauser Comorbidity Index (ECI): two with binary treatments using average treatment effect on treated (ATT) and one multinomial treatment using average treatment effect (ATE). Balance was assessed via effective sample size (ESS), weight distribution and absolute standardized mean difference (ASMD). In the first binary group (48 vs. 4,800 patients), both methods achieved balance: IPTW (ASMD <0.01; ESS: 1,545; weights: 0.01-0.1) and EB (ASMD <0.001; ESS: 1,353; weights: 0.01-11.69). In the second binary group (24,423 vs. 16,406 patients), only EB balanced all covariates (ASMD <0.0001; ESS: 5,913; weights: 0.01-24). In the multinomial group (350 vs. 53 vs. 82 patients), only EB balanced all covariates (ASMD <0.001; ESS: 338, 39, 48; weights: 0.01-4.8). Findings suggest EB, especially with second-moment constraints, provides better covariate balance in real-world studies.

Entropy Balancing,

Inverse Probability Treatment Weighting (IPTW)

Real World Data

Observational studies

Multinomial

Second moments 

Section on Statistical Computing