I've been running in circles trying to figure out of this is possible with Proc Logistic (or similar procedures) for awhile, so hopefully someone here has some ideas. The basic question is, "Are two (or more) logistic models different?" I'm doing a basic survival analysis with 4 variables in my data set, dosage, deaths at the given dose, sample size at the dose, and a categorical treatment regimen. What I'm trying to do is build a model for each treatment regimen where you can predict the percentage surviving after being given a specific dose, and then see if the models are significantly different between treatment regiments. I used proc logistic to produce models for each treatment, but what options are out there for comparing the models outside of testing for differences in parameter estimates or seeing overlap in the confidence intervals plots=effects statement? Here's a quick dummy set of data: DATA dosedata; INPUT Treatment Dose Deaths n ; DATALINES; 1 5 30 30 1 10 25 30 1 15 10 30 1 20 0 30 2 5 30 30 2 10 30 30 2 15 25 30 2 20 20 30 3 5 10 30 3 10 8 30 3 15 5 30 3 20 0 30 3 25 0 30 3 30 0 30 ; run; proc logistic data =dosedata plots=effect plots=ROC ; by treatment ; model deaths/n = dose / lackfit ; run; quit; Using the by statement, proc logistic calculates a model for each individual treatment. It's on the right track as I get a different intercept and slope estimate for each model. At this point though, are there options for a sort of test somewhat equivalent to a multiple comparisons test of means, except in this case for the entire model? I could do comparisons of point estimates such as at what dosage 25% mortality occurs in each model, but I would like to be able to examine the entire binomial distribution instead if it's doable. I've seen the contrast statement for proc logistic, but this appears to only be for contrasting parameter estimates. I could do two separate tests, one to test differences in y-intercepts, and the other for the slope parameters between treatments. I may be overthinking this too though and there might a much simpler way (and procedure) that can address this question. I haven't seen much for hints when I've googled this question, so any particular thoughts on directions to check out are much appreciated.
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