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Optimization-Based Stable Balancing Weights Versus Propensity Score Weighting for Samples With High Covariate

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Stable balancing weights (SBW) improved covariate balance and effective sample size (ESS) compared to propensity score weighting (PSW). SBW offers flexibility in prespecifying covariate balance goals, outperforming PSW in applied healthcare database analyses.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Health Services Research

Background:

  • Propensity score weighting (PSW) is a common method for covariate balance in observational studies.
  • Stable balancing weights (SBW) offer an alternative approach to achieve covariate balance.
  • Comparing the performance of SBW and PSW is crucial for selecting optimal weighting methods.

Purpose of the Study:

  • To compare the performance of stable balancing weights (SBW) against propensity score weighting (PSW).
  • To evaluate covariate balance and effective sample size (ESS) using both methods.
  • To assess performance in cases of extreme covariate imbalance and substantial sample size discrepancy.

Main Methods:

  • Utilized the Premier Healthcare Database for two applied cases involving surgical procedures and neurological procedures.
  • Generated average treatment effects on the treated (ATT) weights.
  • Implemented SBW using grid search and prespecified SMD tolerance techniques.
  • Compared SBW and PSW on postweighting standardized mean differences (SMD), number of imbalanced covariates, and ESS.

Main Results:

  • Both SBW techniques improved covariate balance.
  • SBW methods achieved higher ESS for control groups compared to PSW.
  • Sensitivity analyses with SBW and variable-specific SMD thresholds further increased ESS, outperforming PSW.
  • All methods resulted in postweighting ESS lower than the original unweighted sample size.

Conclusions:

  • Optimization-based SBW provides flexibility in prespecifying covariate balance goals.
  • SBW resulted in superior postweighting covariate balance and larger ESS compared to PSW in applied analyses.
  • SBW is a promising alternative to PSW for enhancing causal inference from observational data.