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Propensity score methods in health economics can yield different results due to varied approaches. Selecting the correct parameter aligned with research questions is crucial for consistent, reliable treatment effect estimation.

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Area of Science:

  • Health Economics and Outcomes Research
  • Biostatistics
  • Epidemiology

Background:

  • Propensity score methods are widely used in health economics and outcomes research.
  • Divergent approaches can lead to confusion and inconsistent results from the same data.
  • A unified conceptual framework is needed to clarify method selection.

Purpose of the Study:

  • To provide a conceptual overview of propensity score methods using a potential outcomes framework.
  • To demonstrate how different mean treatment effect parameters can be estimated.
  • To guide the selection of appropriate methods based on scientific questions.

Main Methods:

  • Utilized the potential outcomes framework to conceptualize mean treatment effect parameters.
  • Explored alternate data-generating processes to understand parameter variations.
  • Applied various propensity score methods (blocking, weighting, matching, doubly robust) to a case study.
  • Case study: Comparative effectiveness of apixaban vs. warfarin on stroke risk in atrial fibrillation patients.

Main Results:

  • Different mean treatment effect parameters yield distinct values, especially with heterogeneous treatment effects.
  • Propensity score methods can estimate different parameters, leading to varied results.
  • Once a target parameter is defined and aligned with research questions, estimates across methods become consistent.
  • Case study showed significant differences initially, but convergence upon targeting a specific parameter.

Conclusions:

  • Propensity score methods can estimate multiple mean treatment effect parameters.
  • The choice of method must align with the specific scientific question and target parameter.
  • Clarifying the target parameter is essential before selecting a propensity score estimation technique.
  • This framework aids applied researchers in choosing appropriate propensity score methods for robust analysis.