Choosing propensity score matching over regression adjustment for causal inference: when,why and how it makes sense

Summary

This study identified when regression adjustment fails to adjust adequately for differences in observed covariates and where propensity score matching is the only alternative.

Multivariate analysis might fail to adjust for observed confounders if:

• 1. The means of the propensity scores in the two groups are more than one-half a standard deviation apart unless distributions of the covariates in both groups are nearly symmetric, sample sizes of the two groups are approximately the same and distributions of the covariates in the two groups have similar variances;

• 2. The ratio of the propensity score variances in the two groups is significantly different from one;

• 3. The ratio of residual variances in the two groups after adjusting for the propensity score is significantly different from one.

Conducted retrospective analysis showed that the treatment effect would be an estimated $305 (or 26%) less if the misspecified outcome model had been chosen.