ppc_km_overlay(): add argument for left-truncation?

[anddis]

Summary
It would be great if ppc_km_overlay() could accomodate left-truncation when computing the Kaplan-Meier functions for the original sample and for the simulated data from the PPD, and not only right-censoring.

Would you consider adding a truncation_y (?) argument to ppc_km_overlay() to pass the left-truncation variable in the original sample to survival::Surv()?

Example
Weibull survival times, observed conditionally on truncation times.

Standard plot produced by ppc_km_overlay() together with KM estimates that take into account left-truncation (red, solid line) and true survival curve (black, dashed line) for the original sample (note that the ppc_km_overlay()'s KM curves for the simulated PPD data ignore truncation too, but the underlying simulated times via brms::posterior_predict() are conditional on the observed truncation times).

library(brms)
library(survival)
library(ggplot2)

simdata <- local({
  set.seed(1901)
  N <- 1000
  y <- rweibull(N, shape = 1.2, scale = 1) 
  v <- rexp(N)
  data.frame(time = y, v = v)
})
simdata_trunc <- simdata[simdata$time >= simdata$v, ]

formula_brms <- bf(time | trunc(lb = v, ub = Inf) ~ 1,
                   family = weibull())
fit_brms <- brm(formula_brms,
                data = simdata_trunc,
                chains = 1,
                iter = 2000,
                seed = 12345,
                backend = "cmdstanr",
                refresh = 0)

set.seed(9)
pp <- posterior_predict(fit_brms, draw_ids = 1:5)
ppc <- bayesplot::ppc_km_overlay(simdata_trunc$time, pp, status_y = rep(1, nrow(simdata_trunc)))
km_trunc <- survfit(Surv(v, time, rep(1, nrow(simdata_trunc))) ~ 1, data = simdata_trunc)

ppc +
  geom_line(data = data.frame(time = km_trunc$time, surv = km_trunc$surv),
            aes(x = time, y = surv), col = "red",
            inherit.aes = FALSE, ,lwd = 1) +
  geom_function(fun = pweibull, args = list(lower.tail = FALSE, shape = 1.2, scale = 1),
                col = "black", lty = 2, inherit.aes = FALSE)