I have some hierarchically nested data that I’m trying to model with brms. A simplified snapshot of the data is here:

snapshot.csv (180 Bytes)

What I want to do is fit two models:

The first models `outcome`

as a function of the `treat`

and the `dose`

. The problem is that `dose`

is obviously missing for those that didn’t receive the treatment. Thus, I tried to model the this question in brms using this code:

```
brm(outcome ~ (1 | treat/dose) + (1 | id), data = snapshot)
```

Is this right? Will it be a problem that there are missing values for `dose`

if `treat`

== 0?

My second question is how can I further introduce `brand`

to the model? For those that were treated, each dose was randomized to come from one of two brands: A or B. How can I model this interaction? My best guess with brms would be something like:

```
brm(outcome ~ (1 + brand | treat/dose) + (1 | id), data = snapshot)
```

- Operating System: Mac OSX Mojave
- brms Version: 2.12.0