Hello,   I have what is likely a relatively simple question, but I haven't been able to find the answer for this on prior posts.    Using proc psmatch, my understanding is that categorical variables need to either be collapsed into a binary variable, or dummy variables need to be created. If I have an ordinal variable (THAanesth - with categories 1-5), and I create 5 dummy variables (anesth1 - anesth5), how do I code this within psmatch? Do I include n-1 dummy variables in both the class and psmodel statement?  For example (THAanesth was previously an ordinal variable 1-5, which has been split into 5 dummy variables):   proc psmatch data=THAtable2 region=cs(extend=0); class TXAstatus sex admitCategory anesth2 anesth3 anesth4 anesth5  ; psmodel TXAstatus(Treated='1')= age sex admitCategory preopHb anesth2 anesth3 anesth4 anesth5; match method=greedy(k=1) stat=lps caliper=.; assess lps var=(age sex admitCategory preopHb anesth2 anesth3 anesth4 anesth5 ) / plots=(boxplot barchart) weight=none; output out(obs=match)=psmatch1 lps=_Lps matchid=_MatchID; run;   Thanks in advance, Brett ... View more

Hi,    I'm running a logistic regression model, hoping to evaluate the impact of a patient's surgical team on their likelihood of receiving a specific medication. I would like to incorporate the patient's anesthesiologist & surgeon as separate random effects. I've structured the model as below, but I just wanted to confirm that this is a reasonable approach, as most of the glimmix documents I've read specify the random statement with "random int / subject = X". In my case, I haven't structured it that way because I have two random effects and I would rather not study the composite variability of (anesthesiologist*surgeon) if possible (I'd like to evaluate the variability in their likelihood of using a medication separately if possible). Is this a reasonable way to structure the code given those objectives?   proc glimmix data=TXAHipA method=Laplace; class anesth surgeon sex urgency year; effect age_spline = spline(age / details naturalcubic basis=tpf(noint) knotmethod=percentiles(5)); effect Hb_spline = spline(Hb / details naturalcubic basis=tpf(noint) knotmethod=percentiles(5)); model TXAstatus = age_spline Hb_spline sex urgency year / solution dist=binomial link=logit; random anesth surgeon; COVTEST / wald; output out=glimmixout pred( blup ilink) = PredProb pred(noblup ilink) = PredProb_PA; run;   Another way I've considered is as follows, but it creates issues with identifying the separate impacts of the two individuals: proc glimmix data=TXAHipA method=Laplace; class anesth surgeon sex urgency year; effect age_spline = spline(age / details naturalcubic basis=tpf(noint) knotmethod=percentiles(5)); effect Hb_spline = spline(Hb / details naturalcubic basis=tpf(noint) knotmethod=percentiles(5)); model TXAstatus = age_spline Hb_spline sex urgency year / solution dist=binomial link=logit; random intercept / subject=anesth*surgeon; COVTEST / wald; output out=glimmixout pred( blup ilink) = PredProb pred(noblup ilink) = PredProb_PA; run;   Any advice would be much appreciated. I've had a difficult time figuring out the differences between these two syntaxes and want to make my code reflects my intentions.    Thanks, Brett ... View more

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   ... View more