I have a dataset, where I would like to see whether there is a group difference in the measurement "concentration". I have repeated measurements for some subjects, which is why I use a mixed-effects model to account for the within-subject repeated measures. From my literature, it is clear, that "concentration" is age-dependent, which is why I end up with the following model:
concentration = intercept + study_group * age, random intercept: subject concentration is numeric and has values from 0 - 2 study_group is a factor with 2 levels age is numeric and has values from 36 to 48
I followed a tutorial (https://ourcodingclub.github.io/tutorials/mixed-models/) that recommended to standardize (centre) my explanatory variable and centred my age variable.
Using the centred age variable, my mixed-effects model showed a significant p-value for the study group, but the model with the uncentred age did not show a significant p-value for my study group.
While looking at the summary of the models I noticed, that Intercept and study group changed values but not the age or interaction term.
I know would like to understand why this is the case.