High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

ABSTRACT:

Background

Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model.

Methods

We developed a multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure pr

SUBMITTER: Schneeweiss S 

PROVIDER: S-EPMC3077219 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature