Yes there are multiple measurements per subject ... View more

I tried running the cox regression using the frailty with penalized likelihood and this code frailtyPenal(Surv(days_events,OUTCOME)~cluster(id)+resqualintra,n.knots=1, kappa=100,data=base) Moreover, I tried running a log-normal survival model and this is code (brack.AFT.GF <- flexsurvreg(Surv(days_events, OUTCOME) ~ resqualintra, data=base, dist="lnorm")) log-normal survivalmodel Cox Frailty with penalized likelihood model As we observe, the parametric model I have a negative estimate and for the Frailty model I have a Positive estimate; SO this is my issue,What could be an explanation to this? ... View more

Thanks 😊 for your reply and concern ... View more

actually i have access to this article but it does not contain the source code for  GEEGOR.mac that is why am asking the file,. this is the code I found in the article, so it shows I need the the GEEGOR.mac file   % "c:\geegor.mac"; %multrpm(dataset=longil_cat,id=id, margin=1, co=1,y=bmi_cat,x=time **bleep**e sex smoking,numcov=5,xcorr=**bleep**e sex ,pc=1, maxit=50,criteria=0.00001); ... View more

Thanks for your reply,  I have tried the code, below and I have very strange results like very high standard errors, what might be the case? because I sude glimmiw for equal slopes and the results look attractive   proc nlmixed data=longil_cat noad qpoints=30; title 'BMICAT, PROC NLMIXED, ordinal, adaptive, q=30'; parms int1=2.5080 int2=4.5586 d= 0.07258; eta1 =int1+ b+beta11*time + beta12***bleep**e + beta13*smoking + beta14*sex + beta15*time***bleep**e + beta16*time*sex; eta2 = int2 + b + beta11*time + beta22***bleep**e + beta13*smoking + beta14*sex + beta25*time***bleep**e + beta26*time*sex; if bmi_cat=1 then z = (1/(1+exp(-eta1))); else if bmi_cat=2 then z = (1/(1+exp(-eta1))) - (1/(1+exp(-eta2))); else z = 1 - (1/(1+exp(-eta2))); if z > 1e-20 then ll = log(z); else ll = -1e10; model bmi_cat ~ general(ll); random b ~normal(0,d**2) subject= id out=EB; estimate 'var(d)' d*d; run; ... View more

But it does not take into consideration the random effect ... View more

No , the code below shows a cumulative logit model for  equal slopes across the category of the outcome, But I want a was procedure that can produce me different slopes across different categories; if it is possible   proc glimmix data=longil_cat method=RMPL; title 'PROC GLIMMIX analysis, ordinal, RMPL'; class id sex(ref="0") smoking(ref="0"); model bmi_cat = time smoking **bleep**e sex time***bleep**e time*sex/ dist=multinomial link=cumlogit solution; /*random intercept time/ subject=id type=un;*/ random intercept/ subject=id type=un; /*nloptions maxiter=5000;*/ run; ... View more

Greetings, Thanks very much for your reply, Actually am working on a project concerning discrete longitudinal analysis. And actually there is evidence of different slopes, so I want to have initial values for me run the NLMIXED procedure.  when there is evidence of equal slopes, e usually do the GLIMMIX procedure to get the initial values.   ... View more