Dimensionality Reduction Techniques for Improving Propensity Score Specification: An Application to a Cohort Study Using Claims Data

Purpose To apply various dimensionality reduction techniques for propensity score (PS) estimation in high‐dimensional data and evaluate their performance against more conventional approaches in achieving covariate balance and confounding control in pharmacoepidemiological studies using claims data. Methods We undertook a cohort study investigating the association between dialysis and mortality in older patients with heart failure and advanced chronic kidney disease using Optum's de‐identified Clinformatics Data Mart Database. We compared PS estimated using investigator‐specified covariates, high‐dimensional propensity score (hdPS) algorithm, and three dimensionality reduction techniques: principal component analysis (PCA), logistic PCA, and autoencoders. Covariate balance was assessed using standardized mean differences (SMD) and propensity score overlap plots. Hazard ratios for in‐hospital mortality were estimated using Cox proportional hazards models. Results The analysis included 485 dialysis‐exposed and 1455 unexposed individuals after matching. Autoencoder‐based PS achieved the best covariate balance (8 covariates with SMD > 0.1), followed by PCA (20 covariates), logistic PCA (25 covariates), hdPS (37 covariates), and investigator‐specified (83 covariates). Hazard ratios for in‐hospital mortality were similar across PS methods. Conclusions Dimensionality reduction techniques, particularly autoencoders, outperformed traditional methods in achieving covariate balance when estimating PS in high‐dimensional claims data. These methods may offer improved covariate balance in pharmacoepidemiological studies PS‐matched designs in large healthcare databases.