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How Do Propensity Score Methods Measure Up in the Presence of Measurement Error? A Monte Carlo Study
Patricia Rodríguez De Gil1, Aarti P Bellara1, Rheta E Lanehart1
1a University of South Florida.
Multivariate Behavioral Research
|November 27, 2015
Summary
Measurement error in covariates significantly biases propensity score (PS) analysis, a method for reducing selection bias in observational studies. This bias impacts treatment effect estimates and confidence interval coverage, even with low error levels.
Area of Science:
- Behavioral and Social Sciences
- Statistical Methodology
Background:
- Measurement error is a common issue in observational research.
- Propensity score (PS) analysis is frequently used to address selection bias.
Purpose of the Study:
- To investigate the impact of measurement error on PS analysis.
- To evaluate effects across various PS conditioning methods.
Main Methods:
- A Monte Carlo simulation study was employed.
- Examined seven different PS conditioning techniques.
Main Results:
- Measurement error in covariates substantially biases treatment effect estimates.
- Reduced confidence interval coverage was observed across all PS methods.
- Even low levels of measurement error yielded significant bias.
Conclusions:
- Propensity score analysis is sensitive to measurement error in covariates.
- Researchers must consider and mitigate measurement error for valid causal inference.
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