For the log-normal AFT model the median survival time for a linear
predictor \(\eta\) is \(t_{med} = \exp(\eta)\), since \(S(t)=0.5\)
when \(\log t = \eta\). Posterior draws of \(\theta\) and \(\beta\)
therefore induce a posterior distribution of \(t_{med}\) whose quantiles
are reported.
Usage
median_survival(fit, newdata = NULL, level = 0.95, ...)
Arguments
- fit
A fitted `exnex_surv` object.
- newdata
Optional data frame (one row gives one median).
- level
Credible-interval level (default `0.95`).
- ...
Unused.
Value
A `data.frame` with columns `group`, `median`, `lower`, `upper`.