Estimated effect of random factors in linear-mixed-effect-models

Hey :slight_smile:
I have a dataset which contains data of different medical parameters of patients at different measurement time points. I want to estimate the effect of parameter A on parameter B, taking into account the variance associated with different people and different measurement time points. I decided to use a linear mixed-effect model, where "time" and "patient" are treated as random factors. So my code has the following form:

lmer(parameterB ~ parameterA+(1|time)+(1|patient))

where "time" and "patient" are just the corresponding information of the random factors.If I now apply the "summary" command to the above code, I get the estimated effect of parameter A on parameter B considering "time" and "patient". My question now is, if there is also a way to get the estimated effect of parameter A on parameter B for each time point of the random factor "time"? I would like to find out the estimated effect of parameter A on parameter B at time point 1, time point 2, etc. of the random factor "time". Is there a command for this or does anyone know how I could find this out correctly and accurately?
Thank you!

This topic was automatically closed 21 days after the last reply. New replies are no longer allowed.

If you have a query related to it or one of the replies, start a new topic and refer back with a link.