I have fit a Cox model (with a cluster-specific random intercept) using `stan_surv`

. Is there an easy way to predict the median survival time for each observation? Note that this is not what is returned by `posterior_survfit`

(at least not by default), which returns the median of the posterior of the survival probabilities for a number of time points. The “median survival time” is the (first) time that this probability drops below 50%.

Ideally I would be able to get the posterior for the median survival time, for each observation. Is there an easy way to do this?

As an aside, I came across a potential bug. When I fit the model I included a random effect `(1|id)`

, where `id`

is a factor. When I call `posterior_survfit`

, I get the following warning:

```
In model.frame.default(TermsF, data, xlev = xlevs, drop.unused.levels = drop.unused.levels, ... :
variable 'id' is not a factor
```

And then in the return object, the `id`

column just has the numbers 1 through the number of levels, as opposed to the actual factor labels. I assume these correspond to as.numeric(id), but it would be more convenient if the function supported factors without the warning.

Thanks in advance.