Hello. I am looking for help with a certain covariate for a logistic model. This is a vaccine study where the endpoint is an event (0 or 1). There are 3 doses administered. We would like to somehow incorporate the effect of when the event occurs: between dose 1 and 2 (dose=1), between dose 2 and 3(dose=2), or after dose 3(dose=3). Due to the design of the study we do not feel that using dose=1,2,3 as a continuous variable is appropriate.
Could it be treated as an ordinal variable? Could be decomposed into indicator variables like Dose=dose1, dose2, dose3. What is the best method to use in the model such that SAS applies the effect appropriately?
Currently we have model event = treatment dose1 dose2 dose3 (and the interactions)
where dose1, dose2, dose3 are categorical y/n=1/0, but the model fails due to low counts for dose1. We tried combining dose1 and dose2 and still get this warning: WARNING: There is possibly a quasi-complete separation of data points. The maximum likelihood estimate may not exist.
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable.
However, when I use the ordinal option, it runs fine. But want to make sure it's valid.
Thank you for your time.
First, when you talk about logistic regression, ordinal always refers to the Y variable, in this case EVENT. So, when you talk about dose being an ordinal variable (and also DOSE is an X variable), this doesn't make sense to me, X variables cannot be ordinal in the sense used in Logistic regression.
Also, I usually think of dose as an amount (on a continuous scale) of something administered, rather than a 0/1 variable. So I'm not really clear what these doses are.
Based on my (possibly limited) understanding of what you are trying to do, this seems more like a survival model than a logistic regression, where the subject can "survive" — not have the event — until time (dose) 1 or time (dose) 2 or time (dose) 3. In this case, you would want to run PROC PHREG, but as I have no experience with survival models, I'll have to drop out here.
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