cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
Choose Language Hide Translation Bar
MKinninmonth
Level II

How do I analyse paired comparisons with an internal control

Hello,

 

I am relatively new to JMP (currently using JMP 15) and I am struggling to identify how to analyse paired comparisons where each test subject has a control measure and an intervention measure at every timepoint.

 

The analysis being performed is looking at the change in continuous variable measured on the face over a defined time period, we know from experience that this variable fluctuates over time regardless of intervention therefore the face is split by left and right and the intervention is applied to one side of the face and the other side kept untreated as an internal control. Therefore each subject has 2 measures at each timepoint "intervention" and "control" and then is measured at baseline and T1 after a predefined length of treatment.

I have included a dummy set of data as an attachment that shows how the raw data is presented.

 

I want to perform analysis that will tell us if there is a significant difference between control and baseline measures within treatments and then a significant difference in change over time between treatments.

When I use the matched pairs analysis method I get the comparison for each treatment back to baseline but I cannot see a way to also get the intevention vs control analysis as the data we are working on is confidential.

 

Should I be using the fit model analysis for this comparison instead, if so how should I set up my model to get a paired comparison rather than an unpaired analysis?

 

@Emmanuel_Romeu 

10 REPLIES 10

Re: How do I analyse paired comparisons with an internal control

Hello Malcolm,

results seems to me similar for both i.e. Mixed models with Residual covariance structure as instance vs. REML method

as highlighted below 

 
Emmanuel_Romeu_2-1635237679317.png

 

Emmanuel_Romeu_4-1635238400893.png

The mixed model will help you select the most appropriate covariance structure based on AICc.