Computes posterior survival curves \(S(t) = \Pr(T > t)\) from a fitted `exnex_surv` model for a log-normal AFT specification: $$\log T_i = \theta_{g[i]} + X_i^\top\beta + \varepsilon,\qquad \varepsilon\sim\mathcal N(0,\sigma^2).$$ For a fixed time \(t\) and linear predictor \(\eta\), the survival probability of the log-normal distribution is $$S(t) = 1 - \Phi\!\left(\frac{\log t - \eta}{\sigma}\right),$$ where \(\sigma^2\) is the residual variance. Posterior draws of \((\theta, \beta, \sigma^2)\) are propagated through this expression to obtain a full posterior distribution of \(S(t)\) at each time point.
Arguments
- fit
A fitted `exnex_surv` object.
- newdata
Optional data frame with columns matching the covariates of the model. If it contains a `group` column, that is used for the group index; otherwise the first group is used for all rows.
- times
Optional numeric vector of times at which to evaluate the curves. If `NULL`, a sensible grid is built from the observed follow-up times.
- level
Credible-interval level (default `0.95`).
- ...
Unused.