Dear all,
I'm now handling with longitudinal data analysis.
I want to build up a joint model of survival and mixed effect model, but I haven't found an available SAS program for this issue.
My outcome variable is Hypertension (value 0 1), covariates are composition of both categorical and continuous vars. Most of them are measured yearly for 5 times.
Is there anybody having experience with this statistical method and SAS program or macro for this analysis?
Trang,
I'm pretty rusty on time-dependent covariates, so I can just refer you to, say, Paul Allison's BBU on survival analysis.
I can comment on treating sodium as continous versus quantiles. If a predictor is reasonalby linear in the hazard space, it will be a better predictor than quantiles for a couple of reasons. For one, with more granularity in the measure, you can get better estimates. The second is that you are not really looking at quantiles as a test; you are looking at a 4-level categorial data and that dilutes the effect. I'm not sure how you can test for monotonicity. You could test for linear or quadratic effects, but that takes additional assumptions on the quantiles that may not be supported by the data.
Looking at your previous query, the granularity of the data (time recorded in whole years) might allow you to do the analysis completely in PROC MIXED.
That said, I'm not sure that a longitudinal data analysis is appropriate for hypertension as an outcome. For "essential" hypertension (i.e. the common type that adults acquire), once the medical diagnosis is made, it continues for the person's lifetime. The hypertension may be in control, but the diagnosis stays. In that case, diagnosis and time to diagnosis are appropriate for a survival analysis (though the huge number of ties will reduce the power).
If your true outcome variable is "blood pressure in control", then a longitudinal analysis may be appropriate.
Dear Doc_Duke,
Thank you very much for your reply. I agree with you. Can I ask you some more questions?
Thank you very much for your support.
Trang
Trang,
I'm pretty rusty on time-dependent covariates, so I can just refer you to, say, Paul Allison's BBU on survival analysis.
I can comment on treating sodium as continous versus quantiles. If a predictor is reasonalby linear in the hazard space, it will be a better predictor than quantiles for a couple of reasons. For one, with more granularity in the measure, you can get better estimates. The second is that you are not really looking at quantiles as a test; you are looking at a 4-level categorial data and that dilutes the effect. I'm not sure how you can test for monotonicity. You could test for linear or quadratic effects, but that takes additional assumptions on the quantiles that may not be supported by the data.
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
Need courses to help you with SAS Life Sciences Analytics Framework, SAS Health Cohort Builder, or other topics? Check out the Health and Life Sciences learning path for all of the offerings.