This is basically an analysis of change model.

2 measurements on each subject were taken at baseline, and 2 more at follow-up. I will refrain from calling this "control" and "intervention" as that could be somewhat misleading.

We have repeated measures within patients. So we could consider a model that fits random intercepts for patients, to control for this. There are also repeated measures within each kidney of each patient. I would suggest the following model:

    measure ~ time + LR + (1 | PatientID)

In order to fit this model, it is necessary to "reshape" the data as follows:

```
PatientID   time   LR   measure
1          -0.5    L    19    
1          -0.5    R    29
1           0.5    L    27
1           0.5    R    20
2          -0.5    L    14    
2          -0.5    R    13
2           0.5    L    13
2           0.5    R    11

```

The estimate for `time` will answer the research question: What is the change in `measure` associated with the intervention, while controlling for the repeated measures within patients, and within the same kidney's of each patient.

Another approach is to fit nested random effects, and treat `LR` as a random factor:

    measure ~ time + (1 | PatientID/LR)