Statistical methods used to control for treatment selection bias in observational studies
Propensity score is the probability of treatment assignment conditional on observed baseline characteristics
A propensity score allows one to conduct an observational study in such a way that it might mimic some of the characteristics of a randomised controlled trial
This is good - observational studies cannot rely on randomisation to correct for confounding factors
If a group of subjects have the same propensity score, they should all have the same baseline characteristics.
Thus, propensity score analysis can compare subjects with similar propensity scores, thus attempting to negate the presence of confounding factors
Advantages
Running an observational study with propensity score analysis is cheaper and easier than running an RCT
This is a way of studying treatments which cannot be ethically randomised
Disadvantages
This is a statistical method, so it can only demonstrate an association, not causation
Propensity analysis assumes that all possible confounding variables have been measured in the observed population (but of course they have not)
Nobody can agree which confounding variables to measure, and when
One can include all sorts of meaningless variables, which add complexity without adding any validity