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Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure.

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  • 1Department of Educational Psychology, The University of Texas at Austin.

Multivariate Behavioral Research
|February 21, 2024
PubMed
Summary
This summary is machine-generated.

Propensity score (PS) analyses with missing and clustered data can be improved. Multilevel imputation and fixed-effects PS models reduce bias in treatment effect estimation.

Keywords:
Propensity score weightingcausal inferencemissing datamultilevel datamultiple imputation

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Area of Science:

  • Behavioral Sciences
  • Biostatistics
  • Epidemiology

Background:

  • Propensity score (PS) analyses are vital in behavioral sciences.
  • Missing covariate data and clustered data structures complicate PS analyses.
  • Previous research addressed these issues separately, not concurrently.

Purpose of the Study:

  • Evaluate methods for PS weighting analysis with both missing and clustered data.
  • Compare different missing data handling and PS weighting strategies.
  • Identify optimal approaches for complex PS analysis scenarios.

Main Methods:

  • Conducted a simulation study evaluating various missing data methods (complete-case, single-level imputation, multilevel imputation).
  • Assessed different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting, clustered weighting).
  • Examined weighted single-level or multilevel outcome models.

Main Results:

  • Multilevel imputation effectively handled missing data in clustered settings.
  • Fixed-effects PS models, clustered weighting, and weighted multilevel outcome models reduced bias.
  • Combining appropriate missing data handling and PS weighting strategies is crucial.

Conclusions:

  • Accounting for clustering in both missing data handling (multilevel imputation) and PS analysis (fixed-effects PS models, clustered weighting) minimizes bias.
  • The findings offer practical guidance for complex observational studies.
  • A real-data example illustrates the recommended methods.