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Updated: May 24, 2026
06:55
Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Variance estimation for stratified propensity score estimators.
E J Williamson1, R Morley, A Lucas
1Centre for MEGA Epidemiology, School of Population Health, University of Melbourne, Melbourne, Australia. ewi@unimelb.edu.au
Statistics in Medicine
|February 25, 2012
Summary
Propensity score methods like stratification and inverse-probability weighting are common in non-randomized studies. Standard variance estimators for these methods can yield overly conservative confidence intervals, suggesting bootstrap variance estimation for more accurate results.
Area of Science:
- Epidemiology
- Biostatistics
- Statistical Modeling
Background:
- Propensity score methods are vital for estimating treatment effects in observational studies.
- Common methods include stratification and inverse-probability weighting (IPW).
- Accurate variance estimation is crucial for reliable confidence intervals.
Purpose of the Study:
- To evaluate the accuracy of standard variance estimators used with propensity score stratification and IPW.
- To derive correct asymptotic marginal variances for these methods.
- To provide practical recommendations for variance estimation in propensity score analysis.
Main Methods:
- Derivation of asymptotic marginal variance for propensity score stratification.
- Comparison with the asymptotic marginal variance for IPW.
- Analysis of routinely used variance estimators in practice.
Main Results:
- Routinely used variance estimators for propensity score stratification can be overly conservative, especially when outcome predictors weakly influence treatment.
- Variance estimators for IPW are consistently too conservative.
- Accurate calculation of asymptotic marginal variance can be complex.
Conclusions:
- Standard variance estimators in propensity score analysis may lead to misleadingly wide confidence intervals.
- Bootstrap variance estimation is recommended for practical application due to complexity and potential inaccuracy of analytical methods.
- Improved variance estimation enhances the reliability of treatment effect estimates from observational studies.
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