Hi,
I'm new to modelling, and I have created the following model aimed to evaluate the factors that contribute to whether a patient receives a specific medication. Relevant covariates include the patient's age, sex, comorbidities (CS1, CS2, CS3), surgery (10 types), surgical urgency (elective vs other), and site the surgery was performed at.
I've created the following model, which has 4 clinically relevant and highly statistically significant interactions.
proc logistic data=TXACharlson; class sex(ref='M') admitCategory(ref='Elective') Surgery_name(ref="Open Hip arthroplasty") site(ref='Ottawa') / param=ref; model TXAstatus (event='1') = age age*age sex CS1 CS2 CS3 admitCategory Surgery_name site Surgery_name*admitCategory Surgery_name*age admitCategory*age surgery_name*site / clparm=both clodds=pl rl lackfit;
oddsratio surgery_type / at (age=65);
oddsratio surgery_type / at (age=70);
oddsratio surgery_type / at (age=75); run;
In order to more easily interpret these interactions, I was hoping to output odds ratios for each interaction, if possible. When I try to use an 'oddsratio' statement (ie)
oddsratio surgery_type / at (age=65);
oddsratio surgery_type / at (age=70);
oddsratio surgery_type / at (age=75);
Instead of outputing the odds ratios for each surgery type at these ages, it outputs a very complex matrix involving some of the other interaction terms (ie,
Surgery_Name Open spinal cord decompression vs Spinal fusion with vertebrectomy at Age=68 admitCategory=Elective site=Winnipeg
Any other suggestions for how to more meaningfully interpret covariates involved in multiple interactions?
Thanks in advance,
Brett
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