Sample size and Power analysis for propensity score methods for causal inference in non-randomized interventional studies

Hello everyone. I am looking for validated procedures for sample size estimation (of nonrandomized intervention studies) and power analysis (of electronic health record-based studies with limited number of records) for propensity score methods (matching and weighting).

Are there recommended procedures?

I found these two:

However, the first requires information on the C statistic of the model that estimates the propensity scores, which is not usually reported in the studies (should it be reported more often?). The second simplifies the problem to a weighted Mantel-Haenzel test but no practical suggestions are given.


I believe your question is not complete for several reasons.

  1. Sample Size and Power estimation depends on several factors besides : Type1 error rate, Power and effect size of interest. It depends on the type of study , is this time fixed, time varying, etc
  2. The use of Propensity score methods or Matching indicates that you are trying to make your covariates independent of exposure. You are trying to decouple the effects of covariates on exposure and mimic RCT. This has nothing to do with power and sample size estimation.

So based on these factors I think your question raises more questions.