Related Experiment Video
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
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Multiple imputation for propensity score analysis with covariates missing at random: some clarity on "within" and
Trang Quynh Nguyen1, Elizabeth A Stuart1,2,3
1Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States.
American Journal of Epidemiology
|June 12, 2024
Summary
This study clarifies using multiple imputation with propensity score analysis for observational data. The "within" method is valid for estimating treatment effects, while most "across" methods are inconsistent.
Area of Science:
- Epidemiology
- Social Sciences
- Biostatistics
Background:
- Propensity score methods are widely used in observational studies to estimate treatment effects.
- Multiple imputation is a common technique for handling missing covariate data.
- The integration of multiple imputation with propensity score analysis lacks clear methodological guidance.
Purpose of the Study:
- To clarify the consistency of different multiple imputation approaches for propensity score analysis.
- To evaluate the validity of "within" and "across" imputation methods.
- To provide practical recommendations for applied researchers.
Main Methods:
- Comparison of "within" and "across" multiple imputation strategies for propensity score analysis.
- Theoretical analysis of consistency for different methods.
- Evaluation of methods imputing functions of covariates, such as propensity scores.
Main Results:
- The "within" imputation method is statistically valid and broadly applicable.
- Existing "across" imputation methods for propensity scores are inconsistent.
- A modified "across" method averaging inverse probability weights is consistent for propensity score weighting.
Conclusions:
- The standard "within" imputation method is recommended for its validity and flexibility.
- Applied researchers should carefully consider the chosen imputation strategy in propensity score analysis.
- Further research may explore alternative "across" methods for specific applications.
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