CumhazEval: Evaluate whether a true cumulative hazard function is contained in the credible set.

Description

This function is intended to evaluate the Bayesian procedure in a simulation study. To that end, this function can be used to check whether the true (user-defined) cumulative hazard function is contained in the credible set generated by the function BayesSurv.

Usage

CumhazEval(time.grid, true.cumhaz, post.mean, radius)

Arguments

time.grid

The time grid on which to evaluate the cumulative hazard.

true.cumhaz

The true cumulative hazard function.

post.mean

The posterior mean of the cumulative hazard, given as a function.

radius

The radius of the credible set for the cumulative hazard.

Value

covered

Indicator whether the true cumulative hazard function is completely covered by the credible set on the times contained in time.grid. 0 = not completely covered, 1 = completely covered.

References

Castillo and Van der Pas (2020). Multiscale Bayesian survival analysis. <arXiv:2005.02889>.

Examples

Run this code

# NOT RUN {
#Demonstration on a simulated data set
library(simsurv)
library(ggplot2)
hazard.true <- function(t,x, betas, ...){1.2*(5*(t+0.05)^3 - 10*(t+0.05)^2 + 5*(t+0.05) ) + 0.7}
cumhaz.true <- Vectorize( function(t){integrate(hazard.true, 0, t)$value} )
sim.df <- data.frame(id = 1:1000)
df <- simsurv(x = sim.df, maxt = 1, hazard = hazard.true)

bs <- BayesSurv(df, "eventtime", "status")
K <- length(bs$haz.post.mean)
cumhaz.pm <- approxfun(c(0, (bs$time.max/K)*(1:K) ), c(0, cumsum(bs$haz.post.mean*bs$time.max/K)))
CumhazEval(bs$surv.eval.grid, cumhaz.true, cumhaz.pm, bs$cumhaz.radius)


# }