Posterior predictive check of the fitted log-normal AFT model: the posterior predictive mean survival curve per group (averaged over all posterior draws) is overlaid with the observed Kaplan-Meier curve and a band of replicated Kaplan-Meier curves. Systematic separation of the observed KM from the posterior mean band indicates model misfit for that group.
Usage
posterior_predictive_check(
fit,
times = NULL,
n_replicates = 200,
newdata = NULL,
plot = TRUE,
...
)Arguments
- fit
A fitted `pooling_surv` object.
- times
Optional vector of evaluation times; defaults to a grid of 100 points between 0 and the largest observed time.
- n_replicates
Number of posterior draws used for the replicated Kaplan-Meier band. Default 200.
- newdata
Optional data frame with one row per group giving the covariates for the predictive curves. If `NULL`, covariates are zero.
- plot
Should a `ggplot2` figure be drawn? Default `TRUE`.
- ...
Unused.
Value
Invisibly returns a list with `posterior` (data frame: `group`, `time`, `mean`, `lwr`, `upr`) and `observed` (data frame: `group`, `time`, `surv`). Requires `ggplot2` when `plot = TRUE`.
Examples
set.seed(2719)
trial_data <- simulate_data(n = 90, K = 3, censoring_rate = 0.3)
fit <- pooling_surv(survival::Surv(time, event) ~ group,
data = trial_data,
iter = 300, warmup = 150, chains = 2)
if (FALSE) posterior_predictive_check(fit) # \dontrun{}