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Ederi
Ederi
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I have a dataset with protein measured twice from the same individual at three different timepoints. The data also includes the mean protein measure (the mean of the two repeated measures) for each timepoint.

The data looks like this:

    ID  Timepoint   ProteinMeasure1   ProteinMeasure2   MeanProtein
    1          1               2,47              5,94           4,2
    1          2               3,89              4,16             4
    1          3               2,12              2,29           2,2
    2          1               6,44              6,57           6,5
    2          2                 20             21,24          20,6
    2          3               9,81              9,97           9,9

The researchers originally wanted to see the effect of time on the change in MeanProtein. I am asking: Would it be better/more accurate to test for the effect of time on protein change in a mixed model, using the repeated measures (subject) as a random effect, rather than performing a model on just the mean measures? If this is the case, why would it be better?

I have a dataset with protein measured twice from the same individual at three different timepoints. The data also includes the mean protein measure (the mean of the two repeated measures) for each timepoint.

The data looks like this:

    ID  Timepoint   ProteinMeasure1   ProteinMeasure2   MeanProtein
    1          1               2,47              5,94           4,2
    1          2               3,89              4,16             4
    1          3               2,12              2,29           2,2
    2          1               6,44              6,57           6,5
    2          2                 20             21,24          20,6
    2          3               9,81              9,97           9,9

The researchers originally wanted to see the effect of time on the change in MeanProtein. I am asking: Would it be better/more accurate to test for the effect of time on protein change in a mixed model, using the repeated measures as a random effect, rather than performing a model on just the mean measures? If this is the case, why would it be better?

I have a dataset with protein measured twice from the same individual at three different timepoints. The data also includes the mean protein measure (the mean of the two repeated measures) for each timepoint.

The data looks like this:

    ID  Timepoint   ProteinMeasure1   ProteinMeasure2   MeanProtein
    1          1               2,47              5,94           4,2
    1          2               3,89              4,16             4
    1          3               2,12              2,29           2,2
    2          1               6,44              6,57           6,5
    2          2                 20             21,24          20,6
    2          3               9,81              9,97           9,9

The researchers originally wanted to see the effect of time on the change in MeanProtein. I am asking: Would it be better/more accurate to test for the effect of time on protein change in a mixed model, using the repeated measures (subject) as a random effect, rather than performing a model on just the mean measures? If this is the case, why would it be better?

Source Link
Ederi
Ederi
  • 146
  • 6

What is the benefit of using repeated measures in a mixed model vs. running a general linear model on the average of the repeated measures?

I have a dataset with protein measured twice from the same individual at three different timepoints. The data also includes the mean protein measure (the mean of the two repeated measures) for each timepoint.

The data looks like this:

    ID  Timepoint   ProteinMeasure1   ProteinMeasure2   MeanProtein
    1          1               2,47              5,94           4,2
    1          2               3,89              4,16             4
    1          3               2,12              2,29           2,2
    2          1               6,44              6,57           6,5
    2          2                 20             21,24          20,6
    2          3               9,81              9,97           9,9

The researchers originally wanted to see the effect of time on the change in MeanProtein. I am asking: Would it be better/more accurate to test for the effect of time on protein change in a mixed model, using the repeated measures as a random effect, rather than performing a model on just the mean measures? If this is the case, why would it be better?