Helper function: KM plot

Usage

h_ggkm(
  data,
  xticks = NULL,
  yval = "Survival",
  censor_show,
  xlab,
  ylab,
  ylim = NULL,
  title,
  footnotes = NULL,
  max_time = NULL,
  lwd = 1,
  lty = NULL,
  pch = 3,
  size = 2,
  col = NULL,
  ci_ribbon = FALSE,
  ggtheme = nestcolor::theme_nest()
)

Arguments

data

(data.frame)
survival data as pre-processed by h_data_plot.

xticks

(numeric, number, or NULL)
numeric vector of ticks or single number with spacing between ticks on the x axis. If NULL (default), labeling::extended() is used to determine an optimal tick position on the x axis.

yval

(string)
value of y-axis. Options are Survival (default) and Failure probability.

censor_show

(flag)
whether to show censored.

xlab

(string)
label of x-axis.

ylab

(string)
label of y-axis.

ylim

(vector of numeric)
vector of length 2 containing lower and upper limits for the y-axis. If NULL (default), the minimum and maximum y-values displayed are used as limits.

title

(string)
title for plot.

footnotes

(string)
footnotes for plot.

max_time

(numeric)
maximum value to show on X axis. Only data values less than or up to this threshold value will be plotted (defaults to NULL).

lwd

(numeric)
line width. Length of a vector should be equal to number of strata from survival::survfit().

lty

(numeric)
line type. Length of a vector should be equal to number of strata from survival::survfit().

pch

(numeric, string)
value or character of points symbol to indicate censored cases.

size

(numeric)
size of censored point, a class of unit.

col

(character)
lines colors. Length of a vector should be equal to number of strata from survival::survfit().

ci_ribbon

(flag)
draw the confidence interval around the Kaplan-Meier curve.

ggtheme

(theme)
a graphical theme as provided by ggplot2 to control outlook of the Kaplan-Meier curve.

Value

A ggplot object.

Examples

# \donttest{
library(dplyr)
library(survival)

fit_km <- tern_ex_adtte %>%
  filter(PARAMCD == "OS") %>%
  survfit(form = Surv(AVAL, 1 - CNSR) ~ ARMCD, data = .)
data_plot <- h_data_plot(fit_km = fit_km)
xticks <- h_xticks(data = data_plot)
gg <- h_ggkm(
  data = data_plot,
  censor_show = TRUE,
  xticks = xticks,
  xlab = "Days",
  yval = "Survival",
  ylab = "Survival Probability",
  title = "Survival"
)
gg

# }