Plot standardized metrics from a fitted flexsurv model

Plot standardized metrics such as the marginal survival, restricted mean survival and hazard, based on a fitted flexsurv model.

Usage

# S3 method for standsurv
plot(x, contrast = FALSE, ci = FALSE, expected = FALSE, ...)

Arguments

x

A standsurv object returned by standsurv

contrast

Should contrasts of standardized metrics be plotted. Defaults to FALSE

ci

Should confidence intervals be plotted (if calculated in standsurv)?

expected

Should the marginal expected survival / hazard also be plotted? This can only be invoked if rmap and ratetable have been passed to standsurv

...

Not currently used

Value

A ggplot showing the standardized metric calculated by standsurv over time. Modification of the plot is possible by adding further ggplot objects, see Examples.

Examples

## Use bc dataset, with an age variable appended
## mean age is higher in those with smaller observed survival times 
newbc <- bc
newbc$age <- rnorm(dim(bc)[1], mean = 65-scale(newbc$recyrs, scale=FALSE), 
sd = 5)

## Fit a Weibull flexsurv model with group and age as covariates
weib_age <- flexsurvreg(Surv(recyrs, censrec) ~ group+age, data=newbc,
                       dist="weibull")
## Calculate standardized survival and the difference in standardized survival
## for the three levels of group across a grid of survival times
standsurv_weib_age <- standsurv(weib_age,
                                           at = list(list(group="Good"),
                                                     list(group="Medium"),
                                                     list(group="Poor")),
                                           t=seq(0,7, length=100),
                                           contrast = "difference", ci=TRUE,
                                           boot = TRUE, B=10, seed=123)
#> Calculating bootstrap standard errors / confidence intervals
#> Calculating bootstrap standard errors / confidence intervals for contrasts
plot(standsurv_weib_age)

plot(standsurv_weib_age) + ggplot2::theme_bw() + ggplot2::ylab("Survival") +
 ggplot2::xlab("Time (years)") + 
 ggplot2::guides(color=ggplot2::guide_legend(title="Prognosis"),
                               fill=ggplot2::guide_legend(title="Prognosis"))

plot(standsurv_weib_age, contrast=TRUE, ci=TRUE) + 
 ggplot2::ylab("Difference in survival")