Hi All,

I would like to estimate a hierarchical mediation model where both the IV of interest (*X*) and the mediator (*M*) are level 2 variables and the DV (*Yij*) is a repeated measures variable with *i* measurements for each *j* subject.

So, breaking this into separate models, I’d have something like:

```
Y ~ X + (1 | id)
Y ~ X + M + (1 | id)
M ~ X
```

In the past, I’ve used brms’s capacity for handling multivariate outcomes to estimate the second and third models simultaneously; however, in this case, I am not sure that this is possible due to the structure of the data required for these models.

Y ~ X + M + (1 | id) requires long format data where each subject is represented across *j* rows and the entries for X and M are identical across *j* rows for each subject. However, if this data is naively used to estimate M ~ X, the sample size is inflated by a factor of *j*. That is, for M ~ X, we need each measurement of M and X to be uniquely represented in the data (rather than repeated).

So, I am wondering if there is a trick for dealing with this in brms; for instance, can I specify different data for each formula? Or, perhaps is there is another way to get around this issue?

The other option, of course, is to just estimate separate models. But, I thought that (1) this might be a common problem and (2) there might be an established way to do this.

I did search around quite a bit (on discourse and brms’s support material), but I haven’t found anything helpful.

Thanks!