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Developing and Applying the Propensity Score to Make Causal Inferences: Variable Selection and Stratification.

Jill L Adelson1, D B McCoach2, H J Rogers2

  • 1Department of Counseling and Human Development, University of Louisville, LouisvilleKY, United States.

Frontiers in Psychology
|September 2, 2017
PubMed
Summary
This summary is machine-generated.

Propensity score analysis requires careful variable selection to reduce bias. Including variables strongly linked to both outcome and treatment assignment is crucial for accurate effect size estimation.

Keywords:
Monte Carlo simulationeffect sizepropensity scorestratificationvariable selection

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Propensity score analysis is a common method for reducing bias in observational studies.
  • Variable selection and stratification are key components of propensity score analysis.
  • The impact of these choices on bias reduction requires further investigation.

Purpose of the Study:

  • To evaluate the influence of variable selection and the number of strata on propensity score analysis.
  • To assess how these factors affect bias reduction and average effect size estimation.
  • To compare propensity score analysis to unadjusted quasi-assignment.

Main Methods:

  • Monte Carlo simulation was employed to examine various scenarios.
  • Variable selection included confounders with different relationships to outcome and treatment assignment.
  • Stratification was performed using 5, 10, or 20 strata.

Main Results:

  • Models lacking variables strongly related to both outcome and assignment may increase bias.
  • Including variables highly related to treatment but not outcome can also inflate bias.
  • Quintile stratification was most effective in reducing bias in 75% of models; richer models required fewer strata.

Conclusions:

  • Effective propensity score modeling necessitates including covariates strongly associated with both treatment assignment and outcome.
  • The optimal number of strata depends on model richness and sample size.
  • Examining strata for overlap is essential when using stratification in propensity score analysis.