#' Plot survival probability

#' @export

#' @concept others

#' @example inst/examples/cat_survival.R

cat_survival <- function(x,

add_censor_mark = TRUE,

add_pvalue = TRUE,

add_confidence_interval = FALSE,

palette = "status_1") {

check_installed(pkg = "ggsurvfit", reason = "to plot survival curves")

p <- ggsurvfit::ggsurvfit(

x = x,

linewidth = 0.5 / .pt,

theme = theme_cat(aspect_ratio = 1)

) +

scale_y_continuous(

limits = c(0, 1),

expand = c(0, 0),

labels = function(x)

x * 100

)

if (add_censor_mark) {

p <- p + ggsurvfit::add_censor_mark(show.legend = FALSE, size = 0.5)

}

if (add_pvalue) {

p <- p + ggsurvfit::add_pvalue(

location = "annotation",

size = 8 / .pt,

x = 0.5,

y = 0.1,

hjust = 0,

prepend_p = FALSE,

caption = "italic(P) == {p.value}",

parse = TRUE

)

}

if (add_confidence_interval) {

p <- p + ggsurvfit::add_confidence_interval(type = "ribbon",

show.legend = FALSE)

}

max_time <- max(p$data$time, na.rm = TRUE)

if (max_time > 3000) {

time_unit <- "days"

divisor <- 1

interval <- 500

} else if (max_time > 100) {

time_unit <- "months"

divisor <- 1

interval <- 10

} else {

time_unit <- "years"

divisor <- 1

interval <- if (max_time <= 5)

1

else if (max_time <= 10)

2

else if (max_time <= 30)

5

else

10

}

max_break <- ceiling(max_time / interval) * interval

x_breaks <- seq(0, max_break, by = interval)

p <- p +

scale_x_continuous(

breaks = x_breaks,

labels = x_breaks,

limits = c(0, max_break),

expand = expansion(mult = c(0, 0))

) +

labs(x = paste0("Time (", time_unit, ")"), y = "Survival probability")

p +

scale_color_cat(palette = palette) +

scale_fill_cat(palette = palette)

}