Propensity Score Analysis with Guaranteed Subgroup Balance

2339 

Contributed Abstract 

Poster 

Yan Li (1), Liang Li (2)

(1) Mayo Clinic, N/A, (2) University of Texas MD Anderson Cancer Center, N/A

Liang Li  
University of Texas MD Anderson Cancer Center

Yan Li  
Mayo Clinic

Yan Li  
Mayo Clinic

Estimating the causal treatment effects by subgroups is important in observational studies when the treatment effect heterogeneity may be present. Existing propensity score methods rely on a correctly specified model. Model misspecification results in biased treatment effect estimation and covariate imbalance. We proposed a new algorithm, the propensity score analysis with guaranteed subgroup balance (G-SBPS), to achieve covariate balance in all subgroups. We further incorporated nonparametric kernel regression for the propensity scores and developed a kernelized G-SBPS (kG-SBPS) to improve the subgroup balance of covariate transformations in a rich functional class. This extension is more robust to propensity score model misspecification. Extensive numerical studies showed that G-SBPS and kG-SBPS improve subgroup covariate balance and subgroup treatment effect estimation (ATE), compared to existing methods. We applied G-SBPS and kG-SBPS to a dataset on right heart catheterization to estimate the subgroup ATEs on the hospital length of stay and a dataset on diabetes self-management training to estimate the subgroup ATEs for the treated on the hospitalization rate.

Causal inference|Subgroup analysis|Nonparametric kernel regression|Covariate balance|Inverse probability weighting|Treatment effect heterogeneity

Section on Statistics in Epidemiology

Causal Inference