Hither! can I get the SAS code for The joint modeling of the "longitudinal ordinal and parametric proportional Hazards model" and I'm new to such code so please tell me with an example?
Hello @meleseenyew1919 ,
I think you should elaborate a bit more on the data you have at your disposal (or the data you will collect) and on the purpose of your research. Keep it concise but clear.
Also, why are you talking about joint modelling? Are there two targets (target variables) you want to model simultaneously?
I guess you will need PROC GLIMMIX or PROC PHREG (SAS/Stat) but to get a concrete code example, you will need to provide us with more detail. Otherwise the example may be completely wrong.
Cheers,
Koen
Hello @meleseenyew1919 ,
I think you should elaborate a bit more on the data you have at your disposal (or the data you will collect) and on the purpose of your research. Keep it concise but clear.
Also, why are you talking about joint modelling? Are there two targets (target variables) you want to model simultaneously?
I guess you will need PROC GLIMMIX or PROC PHREG (SAS/Stat) but to get a concrete code example, you will need to provide us with more detail. Otherwise the example may be completely wrong.
Cheers,
Koen
Hello @meleseenyew1919 ,
I will post an example in the afternoon (Brussels time). No time right now.
But I think (I am sure) you can find a lot on internet as well on joint modelling of longitudinal and survival time-to-event data with SAS PROC GLIMMIX.
If your data require 'special features' to be built in in the example code, let me know.
Koen
Hello @meleseenyew1919 ,
I know. I should have said eve (Brussels time) instead of afternoon. Sorry for that. Have been busy until now. Will start looking for an appropriate example now. Check back in 30 minutes or so.
Koen
Hello @meleseenyew1919 ,
As you have probably figured out already you need the
dist=byobs(dist)
model option in the MODEL statement of PROC GLIMMIX, where dist is a variable in your data set (having two distinct values for your two targets).
But I don't find an appropriate distribution and link function for your ordinal outcome in PROC GLIMMIX. I guess that's exactly your problem.
I need to consult a colleague for this.
But are you sure you want to solve this via a 'real' JOINT MODEL?
Because, in principle, the joint modeling of longitudinal and time-to-event data could be considered a form of mixed outcome (outcomes of a different data type). And for mixed outcomes, joint models are only one of the three(?) possible approaches to achieve a 'combined' solution.
I'm sorry you have to wait till tomorrow.
You can also check:
Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data. With Applications in R, Chapman & Hall/CRC,
Boca Raton, FL.
, but it will probably not contain SAS code 🤔😕.
Coming back on this tomorrow.
Good luck,
Koen
Hello @meleseenyew1919 ,
You marked my first "answer" as a solution. I think that was a bit too "flattered" as your actual question had not been answered at all. When community members see that a question has been solved, they don't look at it anymore. That way you have blocked possible good answers for yourself. But OK, we'll work it out. Unfortunately not today, but hopefully tomorrow.
Have a nice evening,
Koen
Hello @meleseenyew1919 ,
I will try to find some sample code today.
Is there a PROC GLIMMIX in the article:
Gilani N. Joint modeling of repeated ordinal measures and time to event data for CHD risk assessment. Biom Biostat Int J. 2019;8(6):204‒212.
? (I haven't opened up this article)
If there's some PROC GLIMMIX code in this article, does it meet your expectations or are there shortcomings? If the code there does not meet your expectations (case), explain why.
Thanks,
Koen
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