h_ggkm {tern}

Helper function to create a KM plot

Description

Draw the Kaplan-Meier plot using ggplot2.

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

yval

(string)
type of plot, to be plotted on the y-axis. Options are Survival (default) and Failure probability.

censor_show

(flag)
whether to show censored observations.

xlab

(string)
x-axis label.

ylab

(string)
y-axis label.

ylim

(numeric(2))
vector containing lower and upper limits for the y-axis, respectively. If NULL (default), the default scale range is used.

title

(string)
plot title.

footnotes

(string)
plot footnotes.

max_time

(numeric(1))
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. If a vector is given, its length should be equal to the number of strata from survival::survfit().

lty

(numeric)
line type. If a vector is given, its length should be equal to the number of strata from survival::survfit().

pch

(string)
name of symbol or character to use as point symbol to indicate censored cases.

size

(numeric(1))
size of censored point symbols.

col

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

ci_ribbon

(flag)
whether the confidence interval should be drawn around the Kaplan-Meier curve.

ggtheme

(theme)
a graphical theme as provided by ggplot2 to format the Kaplan-Meier plot.

Value

A ggplot object.

Examples

library(dplyr)
library(survival)

fit_km <- tern_ex_adtte %>%
  filter(PARAMCD == "OS") %>%
  survfit(formula = 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