I have fitted a Bayesian discrete-time survival model with time-varying covariates as a binomial regression, adapting to the excellent material from @Solomon, here.

```
brm_model_surv <- brm(
event | trials(1) ~ 0 + Intercept + a + b + (1|subject),
data = data, family = binomial)
```

where `a`

and `b`

are the covariates that vary over time, and `subject`

denotes the subject that may have been measured several times (therefore the random effect).

I would now like to understand how well the model (predictions) represents the data, visually for the first. I have seen plots generated via the `rstanarm`

package that show the predicted survival probability to the Kaplan-Meier curve: Graphical checks of the estimated survival function — ps_check • rstanarm (mc-stan.org)

How could I achieve a similar plot with `brms`

? I think I will need to somehow combine the predictions by, for instance, averaging the probabilities obtained through the `predict`

function across runs for each `subject`

at certain times, but perhaps I am missing a simpler way here.

- Operating System: Windows 10
- brms Version: 2.17.0