Serial Corelation

Sandeep123

I have a data set in which multiple values were collected from same patient. There are 25 patients but each patient would have about 30 observations.

These patients have their transcutaneous CO2 is measured at different time points.. And we are comparing different characteristics (oxygen requirement,  Respiratory support) with normal and abnormal transcutaneous CO2 measurements. Also correlating transcutaneous CO2 with FiO2. We will also be doing multi variate regression analysis.

This obviously leads to problem of non- independent observations and error in the P value by standard tests.

I was talking to a statistician who analyses the data using SAS . He mentioned that he can adjust the statistical test for serial correlation in SAS.

 is it possible in jmp  to do that ?

Mark_Bailey

So you have about 30 observations for each of 25 patients. Each observation is a time point that includes patient ID, transcutaneous CO2, FiO2, oxygen requirement, respiratory support. Is this much correct?

 

I understand by 'serial correlation' that you mean the response is auto-correlated. Do you know why? For example, are the measurements taken over a period of time that is too short for the random variation, or is there a treatment over the same time period that affects the response?

 

Which of these variables is the response or outcome that is important to you?

 

Are there any other variables, such as age, gender, or disease state?

 

Is the normal or abnormal transcutaneous CO2 determined independently for each observation?

 

Did the statistician explain the nature of the adjustment that would be used?

 

I think that a mixed model with patient as a random effect would be OK, but it might be necessary to nest the other parameters for a random coefficient model. JMP can perform some of these regressions. JMP Pro might be required for the other regressions.