Propensity score weighting with multilevel data.
Propensity score methods are being increasingly used as a less parametric alternative to traditional regression to balance observed differences across groups in both descriptive and causal comparisons. Data collected in many disciplines often have analytically relevant multilevel or clustered structure. The propensity score, however, was developed and has been used primarily with unstructured data. We present and compare several propensity-score-weighted estimators for clustered data, including marginal, cluster-weighted, and doubly robust estimators. Using both analytical derivations and Monte Carlo simulations, we illustrate bias arising when the usual assumptions of propensity score analysis do not hold for multilevel data. We show that exploiting the multilevel structure, either parametrically or nonparametrically, in at least one stage of the propensity score analysis can greatly reduce these biases. We applied these methods to a study of racial disparities in breast cancer screening among beneficiaries of Medicare health plans.
Duke Scholars
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- White People
- United States
- Statistics & Probability
- Propensity Score
- Monte Carlo Method
- Medicare
- Humans
- Healthcare Disparities
- Female
- Early Detection of Cancer
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- White People
- United States
- Statistics & Probability
- Propensity Score
- Monte Carlo Method
- Medicare
- Humans
- Healthcare Disparities
- Female
- Early Detection of Cancer