Semiparametric allocation of subjects to cohort strata

• Published in: Epidemiology (Cambridge, Mass.)
• Main Authors: Walker, Alexander M, Russo, Massimiliano, Schneeweiss, Maria C, Glynn, Robert J
• Format: Journal Article
• Language: English
• Published: United States 01.03.2024
• ISSN: 1531-5487
• DOI: 10.1097/EDE.0000000000001698

We illustrate a method for stratum assignment in small cohort studies that avoids modeling assumptions. Off-the-shelf software (rgenoud) made stratum assignments to minimize a loss function built on within-stratum and population-adjusted Euclidean distances. In 100 trials using simulated data of 300 records with a binary treatment and four dissimilar covariate treatment predictors, minimizing a loss based on Euclidean distance reduced covariate imbalance by a median of 99%. Stratification by propensity score and weighting records by the inverse of their probability of treatment reduced imbalance by 76-89% and 83-94%, respectively. Loss minimization applied to a cohort of 361 children undergoing immunotherapy achieved nearly complete elimination of covariate differences for important treatment predictors. With the availability of semiparametric stratum-assignment algorithms, analysts can tailor loss functions to meet design goals. Here, a loss function that emphasized covariate balance performed well under limited testing.