I'm a bit unsure about a current project I'm concerned with.
I have an outcome of interest, say $y$, and a main predictor, say $x$, which both are continuous. I have multiple measurements per subject of $x$ and $y$, at different timepoints. Apart from that, I have some other covariates, which should be included in the model.

I now have the impression that I ignore the time information when I just specify a random effect on intercept + slope (the main predictor). However, if I specify random intercept + slope (time), I feel that I'm not addressing the research question, how the main predictor affects the outcome variable.

Basically there are two questions:

 - How does the main predictor affect the outcome? 
 - How does time affect the outcome? 

I'm not sure how to handle this/set up the random effects. I never had the situation of including two random effects and I don't think that's the right way here. I would probably include all covariates as fixed effects, plus an interaction term Time * Main Predictor, but what about the random effects?