summary.expertsurv {expertsurv} | R Documentation |
Prints a summary table for the distribution the mean survival time for a given model and data
Description
Calculates the mean survival time as the area under the survival curve -
ported from survHE
Usage
## S3 method for class 'expertsurv'
summary(object, mod = 1, t = NULL, nsim = 1000, ...)
Arguments
object |
a |
mod |
the model to be analysed (default = 1) |
t |
the vector of times to be used in the computation. Default = NULL, which means the observed times will be used. NB: the vector of times should be: i) long enough so that S(t) goes to 0; and ii) dense enough so that the approximation to the AUC is sufficiently precise |
nsim |
the number of simulations from the survival curve distributions to be used (to compute interval estimates) |
... |
Additional options |
Value
A list comprising of the following elements:
mean.surv |
A matrix with the simulated values for the mean survival times |
tab |
A summary table |
Author(s)
Gianluca Baio
References
Baio G (2020). “survHE: Survival Analysis for Health Economic Evaluation and Cost-Effectiveness Modeling.” Journal of Statistical Software, 95(14), 1–47. doi:10.18637/jss.v095.i14.
See Also
Examples
require("dplyr")
data2 <- data %>% rename(status = censored) %>% mutate(time2 = ifelse(time > 10, 10, time),
status2 = ifelse(time> 10, 0, status))
mle = example1 <- fit.models.expert(formula=Surv(time2,status2)~1,data=data2,
distr=c("wph", "gomp"),
method="mle")
summary(mle)