Hi JMP Community,
I need help with a relatively complex Repeated Measure Mixed Linear Model:
I have collected data from subjects exposed to different treatment, at Baseline, Week 4, and Week 12 (repeated measures; see small excerpt attached). I have analyzed this data set using the Fit Model REML platform with the following model effects:
I now need to incorporate an additional effect (i.e. PLATE ID) that needs to be nested with Patient ID too (correct me if I use the term incorrectly).
Assuming that this can be done with JMP 14.1 (not Pro), what would be the correct structure of the model effects?
Would it be correct to only include a new term = Patient ID[PLATE ID] & Random ?
Thank you for your help.
I can give you my thoughts regarding your data set:
1. You have 4 Plate_ID's, 7 Patient ID and 5 ARM's and then have measures over 3 time periods. If I read it correctly, you have only 7 unique combinations. The data looks like the picture below.
2. In some cases, Patient ID and Plate_ID are confounded. ARM is confounded with Patient ID, except for when ARM=E.
3. There is no context for the DATA? How much of change in this response variable is worthy of consideration (is of practical significance)?
4. You indicate you want a linear model, but the "factors" are more than 2 levels.
So how can you handle the time series data:
1. You could take a quick look at the consistency of the time series with a range chart.
2. You could calculate a statistic to summarize that effect (average and standard deviation, attached JMP file) although the data looks to be inconsistent.
3. What are you looking for in the time series? Would the slope of the line be of value?
Sorry, I can only work with what you give me. You made no mention this was a subset of your data set...
Regarding your question "Where I actually need help is to clarify the correct model structure to account for a potential confounding factor linked to the assay platform (i.e. PLATE ID)."
Simply enough, you can't separate confounded factors, so you must be careful drawing conclusions about said factors.
No worries, I wish I could be more helpful. I really don't understand what you mean by Plate ID is nested within Patient. From the small example data set you included, there was no obvious nesting. For a variable to be nested, it must be contingent upon another variable. So for your example of batches, within batch samples are nested within batch. I just don't understand your situation to provide better guidance. I don't see how batches would be nested within Patient, but this is due to not understanding your particular situation. If you do have a variable (A) and it is nested in variable (B), then you would add the term A[B] to the model. Of course, this would not allow for interaction effects to be estimated.
First, I screwed up: I got the Nesting relationship description inverted - i.e. Patient_ID is nested in PLATE_ID - not the other way around.
Second, I created an anonymized data set that will give the JMP community a much better look at the complete data set.
Third, I'm thinking of using the following fit model structure with REML personality:
Is this approach appropriate to explore difference between the effect of ARM and TIME POINT for these repeated measures (DATA) while accounting for PLATE_ID as a possible source of variation? FYI, a quick visual survey of the data suggests that the DATA is influenced by PLATE_ID, ARM, and TIME POINT.