SurvEval {BayesSurvival} R Documentation

## Evaluate whether a true survival 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) survival function is contained in the credible set generated by the function BayesSurv.

### Usage

```SurvEval(time.grid, true.surv, post.mean, radius)
```

### Arguments

 `time.grid` The time grid on which to evaluate the survival function. `true.surv` The true survival function. `post.mean` The posterior mean of the survival function, given as a function. `radius` The radius of the credible set for the survival function

### Value

 `covered` Indicator whether the true survival 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 survival function 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} )
surv.true <- function(t){exp(-cumhaz.true(t))}

sim.df <- data.frame(id = 1:1000)
df <- simsurv(x = sim.df, maxt = 1, hazard = hazard.true)

bs <- BayesSurv(df, "eventtime", "status")
surv.pm <- approxfun(bs\$surv.eval.grid, bs\$surv.post.mean)
SurvEval(bs\$surv.eval.grid, surv.true, surv.pm, bs\$surv.radius)

```

[Package BayesSurvival version 0.2.0 Index]