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Propensity score weighting with survey weighted data when outcomes are binary: a simulation study.

Chen Yang1,2, Meaghan S Cuerden3, Wei Zhang4

  • 1Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Health Services & Outcomes Research Methodology
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

This study compared propensity score weighting methods for complex survey data. Two-stage methods best estimate treatment effects on the treated, while optimal methods for average treatment effects vary with model specification and data overlap.

Keywords:
Population average treatment effectPopulation average treatment effect on the treatedPropensity score weightingSmoking cessationSurvey data

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Survey Methodology

Background:

  • Propensity score methods are common in observational studies.
  • Limited research exists on propensity score-based weighting (PSW) for complex survey data, especially with binary outcomes.

Purpose of the Study:

  • To compare eight propensity score weighting approaches for estimating treatment effects in complex survey data.
  • To evaluate methods for estimating the population average treatment effect (PATE) and the population average treatment effect on the treated (PATT).

Main Methods:

  • A simulation study was conducted using survey-weighted data.
  • Eight PSW methods were applied to estimate PATE and PATT under varying scenarios.
  • Evaluated bias and coverage probability based on treatment effect, model misspecification, and propensity score overlap.

Main Results:

  • Two-stage PSW methods consistently outperformed others for estimating PATT.
  • The best PSW method for PATE varied with model misspecification and propensity score overlap.
  • Four two-stage methods showed improved PATE estimates when the outcome model was correctly specified.

Conclusions:

  • Two-stage propensity score weighting methods are recommended for estimating PATT in complex survey data.
  • Method selection for PATE estimation requires careful consideration of model specification and data characteristics.
  • The study applied these methods to analyze smoking cessation discussions using the 2015 National Health Interview Survey data.