CumhazEval {BayesSurvival} R Documentation

## 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>.

### See Also

BayesSurv, which computes the posterior mean of the cumulative hazard as well as the radius for its credible set.

### Examples

```#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)

```

[Package BayesSurvival version 0.2.0 Index]