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Propensity Score Weighting Analysis

Unlike randomized clinical trials, observational studies must adjust for differences such as confounding to ensure patient characteristics are comparable across treatment groups. This is frequently addressed through propensity scores (PS), which summarizes differences in patient characteristics between treatment groups. Propensity Score is the probability that each individual will be assigned to receive the treatment of interest given their measured covariates. Matching or Weighting on the PS is used to adjust comparisons between the 2 groups, thus reducing the potential bias in estimated effects of observational studies.

The following use cases assume a binary treatment or exposure in order to infer causality. Given a treatment and control with one outcome observed per unit, can we estimate the treatment effect? Note we can only estimate the treatment effect, identification of causality is not possible through observational studies.

Estimation of Propensity Scores

Propensity scores are most commonly estimated using binomial regression models (logistic regression, probit, etc.). Other propensity score methods include:

  • Classification Trees
  • Bagging/Boosting
  • Neural Networks
  • Recursive Partitioning

All the covariates related to treatment and outcome that were measured before treatment should be included in the propensity score estimation model. The example below estimates propensity scores for the treatment variable group predicted from covariates var1, var2, and var3.

PROC LOGISTIC data=ps_est;
  title 'Propensity Score Estimation';
  model group = var1-var3 / lackfit outroc = ps_r;
  output out = ps_p;
run;


References

Overlap Weighting: A Propensity Score Method That Mimics Attributes of a Randomized Clinical Trials (Thomas, Li, Pencina; 2020)



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