Repeated measured regression analysis (Mixed model)

ammarhm — Mon, 01 Feb 2016 14:45:35 GMT

Hi all,

Need a hand doing this with SAS:

Here is the senario: I have 100 patients who underwent surgery for pain relief, the surgery would shorten a ligament by x centemeters

Pain is measured pre-op (pre_pain) and 1 year post-op (post_pain), so we have a repeated measure sitiation.

The theory is: Is the length of the removed ligament related to pain 1 year after surgery?

Variables I want to correct for: Age and sex

So my understanding is that this needs to be done through mixed modelling to controll for individual patient variation.

somthing like:

model post_pain= age sex pre_pain ligament_length (in a mixed model )

I have checked alot in the internet but there is not much written on how to do this with SAS, som suggest GLIMMIX, other MIXED etc

Would really appreciate your help.

Note: Data is in long form

Best regards

Re: Repeated measured regression analysis (Mixed model)

gergely_batho — Wed, 13 Jul 2016 09:32:02 GMT

Hi,

I'm not so skilled in mixed modeling, but I think if your model is:

model post_pain= age sex pre_pain ligament_length

than you must have 100 observation, each for a patient, no repeats, and you are using pre_pain as a regular regressor.

I think it is a valid model, and in this case you don't need a mixed model. Just use prog glm. You will look at the p-value of ligament_length.

If you want to model it as repeated measures, I would start with this example:

https://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mixed_examples02.htm

It's just a technical detail, but you will need to restructure your dataset to have 200 observations, you will drop post_pain, pre_pain, instead you will have PatientID, PrePost, Pain.

Proc mixed is for linear models where ~~response variable~~ random term is normaly distributed.

With proc glimmix you are able to model other distributions.

Greg

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Repeated measured regression analysis (Mixed model)

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