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Propensity score weighting analysis with complex survey data for estimating population-level treatment effects on
Lihua Li1,2,3,4, Chen Yang1,2, Wei Zhang1,2
1Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, One Gustave L Levy Place, Box 1077, New York, NY 10029, USA.
Health Services & Outcomes Research Methodology
|December 15, 2025
Summary
Propensity score weighting (PSW) methods that account for complex survey design improve treatment effect estimates for survival outcomes. Incorporating survey design in outcome modeling is most critical for accurate population-level results.
Area of Science:
- Biostatistics
- Epidemiology
- Survey Methodology
Background:
- Propensity score weighting (PSW) is used for treatment effect estimation in observational studies.
- Best practices for applying PSW to complex survey data with survival outcomes are unclear.
- Complex survey data have design features like strata, clusters, and sampling weights.
Purpose of the Study:
- To explore integrating PSW with complex survey data for unbiased population-level survival outcome estimates.
- To evaluate three PSW methods based on their accounting for survey design features.
- To compare performance in estimating absolute and relative treatment effects.
Main Methods:
- Simulated complex survey data with survival outcomes under various scenarios.
- Evaluated three methods: I (no design adjustment), II (outcome model adjustment), III (both models adjusted).
- Compared methods using mean relative bias, mean absolute bias, and coverage probability.
Main Results:
- Survey-weighted Methods II and III outperformed unweighted Method I, especially with true treatment effects.
- Methods II and III showed similar performance across various challenging scenarios (e.g., informative censoring, outliers, non-response).
- Accounting for survey design in the outcome model was most critical.
Conclusions:
- Both modeling stages in PSW should incorporate survey designs for complex survey data.
- Prioritizing survey design in the outcome model is crucial for accurate population-level treatment effect estimation.
- Applied methods to National Health Interview Survey (NHIS) data to study smoking cessation after cancer diagnosis and survival.
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