I would suggest you do it both ways. If subgroup analysis is important,
then it makes sense to ensure good balance within subgroups.
Kosuke Imai
Professor, Department of Politics
Center for Statistics and Machine Learning
Princeton University
http://imai.princeton.edu
On Sat, Mar 11, 2017 at 12:26 PM, GUINET Anne-laure <
anne-laure.guinet(a)fondationpoidatz.com> wrote:
Dear Pr Kimai,
First, I would like to thank you for your R notice. They are very clear
and very useful.
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.
Nevertheless, despite your enlightened notice, I have some questions
without answers.
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(treatment ~ var1 + var2 + var3 + var4 + var5,
method = "nearest", data = data)
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,
Anne-Laure GUINET
Clinical Research Assistant
Physiotherapist
*anne-laure.guinet(a)fondationpoidatz.com
<anne-laure.guinet(a)fondationpoidatz.com>*
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