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Stat Med. 2013 Aug 30;32(19):3373-87. doi: 10.1002/sim.5786. Epub 2013 Mar 24.

Propensity score weighting with multilevel data.

Author information

1
Department of Statistical Science, Duke University, Durham, NC, 27708, USA. fli@stat.duke.edu

Abstract

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.

KEYWORDS:

balance; multilevel; propensity score; racial disparity; treatment effect; unmeasured confounders; weighting

PMID:
23526267
PMCID:
PMC3710526
DOI:
10.1002/sim.5786
[Indexed for MEDLINE]
Free PMC Article

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