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Propensity score weighting for a continuous exposure with multilevel data.

Megan S Schuler1, Wanghuan Chu2, Donna Coffman3

  • 1Department of Health Care Policy, Harvard Medical School, Boston, MA 02215.

Health Services & Outcomes Research Methodology
|December 20, 2016
PubMed
Summary
This summary is machine-generated.

Propensity score weighting for continuous exposures in multilevel data can reduce bias. Accounting for the data's hierarchical structure, especially with cluster-mean weights, improves results over single-level models.

Keywords:
continuous exposuremultilevel dataobservational studypropensity score

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Propensity score methods are used to balance exposure groups on covariates.
  • Traditional methods assume no hierarchical data structure.
  • Clustered data are common in health research (e.g., patients within providers).

Purpose of the Study:

  • To extend propensity score weighting to continuous exposures in multilevel settings.
  • To compare different propensity score model specifications (random effects, fixed effects, single-level).
  • To evaluate marginal versus cluster-mean stabilized propensity score weights.

Main Methods:

  • Simulations were used to compare various propensity score weighting strategies.
  • The study focused on continuous exposure variables within a multilevel data framework.
  • Different regression models were specified to estimate propensity scores.

Main Results:

  • Multilevel regression models for propensity scores reduced bias compared to single-level models, especially when cluster-level confounders were missing.
  • Cluster-mean stabilized propensity score weights demonstrated superior performance over marginal weights.
  • Accounting for the hierarchical data structure is crucial for accurate estimation.

Conclusions:

  • Propensity score weighting in multilevel settings requires careful model specification.
  • Cluster-mean stabilized weights are recommended for continuous exposures in clustered data.
  • These methods enhance causal inference in complex hierarchical health data.