Propensity score matching

Hello,

To introduce myself, I’m physiotherapist and also clinical research assistant ; I have a French degree in statistics (University Degree) and I’m interested in propensity score to analyze retrospective data.

We are studying Single Event Multilevel Surgery (SEMLS) were multiple orthopedic procedures may be associated at the same time in order to improve the gait of cerebral palsy patients. The “independent” data of interest is one type of surgical procedure (treatment == 1 , control == 0) (in fact 8 others surgical procedures may be combined in the data set ). We have multiple variables to explore (+1000), but we focus on 10 variables on which we have the hypothesis that could be improved after surgery (and may not be biased by the other surgical procedures effects). We consider propensity score could be the best solution to be able to compare our two samples in order to assess the effect of this specific procedure.

We tried to perform propensity score matching with MatchIt package using argument “nearest” and we didn’t obtained comparable samples… Probably because of some outliers. So we introduced a “caliper=0.1” to limit the matching. (I don’t know if it’s a correct strategy but it works…).
We then assessed the overall effect of this procedure.
Studying the data it seems that some subgroups among the patients specified with respect to some parameters (that has been used in the matching) may have better outcomes. The question is :
Can we perform subgroup analysis after a PSM was realized on the global population ?
Or shall we better, first define the subgroups candidates, then realize some à posteriori matching on those subpopulations only ?

A bonus question :
What is the difference between this method :
mod_match <- matchit(catholic ~ race_white + w3income + p5hmage + p5numpla + w3momed_hsb,
method = "nearest", data = ecls_nomiss)
and the fact of Performing a logistic regression, then to compute propensity score with “predict” (to create a variable called SP), and do :
matchit(treatment ~ SP, data = data, method = “nearest”, ratio =1)

Thank you in advance for your answers,

Best regards,

Anelor