Comparing treatments via the propensity score: Stratification or modeling?

Jessica A. Myers, Thomas A. Louis

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


In observational studies of treatments or interventions, propensity score (PS) adjustment is often useful for controlling bias in estimation of treatment effects. Regression on PS is used most often and can be highly efficient, but it can lead to biased results when model assumptions are violated. The validity of stratification on PS depends on fewer model assumptions, but this approach is less efficient than regression adjustment when the regression assumptions hold. To investigate these issues, we compare stratification and regression adjustments in a Monte Carlo simulation study. We consider two stratification approaches: equal frequency strata and an approach that attempts to choose strata that minimize the mean squared error (MSE) of the treatment effect estimate. The regression approach that we consider is a generalized additive model (GAM) that estimates treatment effect controlling for a potentially nonlinear association between PS and outcome. We find that under a wide range of plausible data generating distributions the GAM approach outperforms stratification in treatment effect estimation with respect to bias, variance, and thereby MSE. We illustrate each approach in an analysis of insurance plan choice and its relation to satisfaction with asthma care.

Original languageEnglish (US)
Pages (from-to)29-43
Number of pages15
JournalHealth Services and Outcomes Research Methodology
Issue number1
StatePublished - Mar 2012
Externally publishedYes


  • Causal inference
  • Generalized additive model
  • Nonlinear modeling
  • Observational study
  • Optimal stratification
  • Propensity score

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health


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