I believe you would use a multinomial logistic regression. However, I think it’s important to consider what it is you are trying to accomplish in order to ensure that your model is setup correctly. In a typical
propensity score case of one treatment condition and one control condition, you are trying to ensure that all the cases in the control condition are the same as those in the treatment condition (The probability is equal across groups). The Control condition
cases are thus assigned a likelihood score for being in the treatment, instead of control. Of course, to ensure equality, we do this for all cases, those in treatment as well, as we may have treatment cases that are more similar to or more likely to be in
control, but we frame it around the control units being in treatment. In your case, there isn’t one treatment, but multiple treatment groups. Presumably there is one control condition, and two treatment conditions. In that case, what are you referencing
it too? I would argue that we weren’t really referencing it to the treatment condition in the first place, that was just an easy way to think about the problem. In fact, we were simply creating a probability that individuals were in the group they were supposed
to be in (Realistically we don’t want to be able to predict the group they are in, we want the probabilities to be equal across groups). Since our actual goal is to create probabilities that indicate that everyone was placed as they should (with the goal
being we can’t actually tell who should have been in what group), the referent group doesn’t matter. In your situation, I’d actually pick the control group as my referent group.
I’ll also put this out there. A propensity score is only as good as the variables used to predict the probability. I worked on a problem where the project PI was very excited that his propensity score model
was non-significant with poor ability to place participants. He correctly understood that this indicated an inability to differentiate the participants by these variables, what he failed to realize was that he lacked appropriate instrumental variables for
modeling this. In other words, you really need to be sure the model is solid as well. It’s highly unlikely that the model will be non-significant if done right, you just want a generally poor fit with a solid set of instrumental variables.
Matthew J Poes
Research Data Specialist
Center for Prevention Research and Development
University of Illinois
510 Devonshire Dr.
Champaign, IL 61820
Phone: 217-265-4576
email: [hidden email]
From: SPSSX(r) Discussion [mailto:[hidden email]]
On Behalf Of la volta statistics
Sent: Friday, November 30, 2012 7:57 AM
To: [hidden email]
Subject: propensity score for 3 treatments
Hi all
To control for confounding bias from non-random treatment assignment with 3 different treatments, I would like to calculate a propensity score I later can use in a cox regression
model. I know the procedures for a two-treatment approach (logistic regression). But how would I calculate such a score when I have three treatments?
Thanks in advance
Christian
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la volta statistics
Christian Schmidhauser, Dr.phil.II
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email: [hidden email]
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