Powered by: Blackwell Publishing

HRET - Health Research & Educational Trust

HSR - Health Services Research

Impacting Health Practice and Policy Through State-of-the-Art Research and Thinking

Our Next Issue

March 2018
Coming Soon! Read

< ABSTRACT LIST

VOLUME 53 | NUMBER 1 | FEBRUARY 2018


Covariate Balancing through Naturally Occurring Strata

Objective: To provide an alternative to propensity scoring (PS) for the common situation where there are interacting covariates.

Setting: We used 1.3 million assessments of residents of the United States Veterans Affairs nursing homes, collected from January 1, 2000, through October 9, 2012.

Design: In stratified covariate balancing (SCB), data are divided into naturally occurring strata, where each stratum is an observed combination of the covariates. Within each stratum, cases with, and controls without, the target event are counted; controls are weighted to be as frequent as cases. This weighting procedure guarantees that covariates, or combination of covariates, are balanced, meaning they occur at the same rate among cases and controls. Finally, impact of the target event is calculated in the weighted data. We compare the performance of SCB, logistic regression (LR), and propensity scoring (PS) in simulated and real data. We examined the calibration of SCB and PS in predicting 6-month mortality from inability to eat, controlling for age, gender, and nine other disabilities for 296,051 residents in Veterans Affairs nursing homes. We also performed a simulation study, where outcomes were randomly generated from treatment, 10 covariates, and increasing number of covariate interactions. The accuracy of SCB, PS, and LR in recovering the simulated treatment effect was reported.

Findings: In simulated environment, as the number of interactions among the covariates increased, SCB and properly specified LR remained accurate but pairwise LR and pairwise PS, the most common applications of these tools, performed poorly. In real data, application of SCB was practical. SCB was better calibrated than linear PS, the most common method of PS.

Conclusions: In environments where covariates interact, SCB is practical and more accurate than common methods of applying LR and PS.

back to top | back to article index | purchase full article

Copyright© 2017, Health Research & Educational Trust. All rights reserved. Content Disclaimer
Health Research & Educational Trust, 155 North Wacker, 4th Floor Chicago, IL 60606 (312) 422.2600