Hi All,

I have been using  lmer() for assessing longitudinal data with time at lv 1
and participant at lv 2.
Participants complete an 8 wk intervention and are assessed for "practice
quality" weekly for 7 wks and also complete pre-post measures (depression,
anxiety, etc). I am interested in assessing for change in practice quality
over time and determining if the individual slopes predict post-intervention
measures. For assessing change over time (with correlated random effects) I
am using this:

lmer(pqm_F ~ 1 + wk + (1 + wk|id), pqmagg, REML = 0)

which works fine. However, I am not certain how to go about fitting a model
that assess for individual slopes predicting post-intervention measures. Is
it best to take the predicted slopes and use them in an OLS regression with
these slopes predicting post measures? Any help is much appreciated!

Thank you,

AC

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