Make Kaplan-Meier plots

Usage

doKMPlots(
  input.d,
  time,
  status,
  var.name,
  var.description,
  surv.type = c("os", "dss", "pfs", "aefs"),
  shading.colors = c("blue2", "red2", "deepskyblue", "indianred3"),
  main = NULL,
  line.name = NULL,
  line.pattern = NULL,
  legend = FALSE,
  cox.ref.group = NULL,
  use.firth = -1,
  CI = TRUE,
  bold_pval = FALSE,
  sig.level = 0.05,
  HR = TRUE,
  show.risk = TRUE,
  km.plot.ref.group = "single",
  single.test.type = "logrank",
  use.ggkm = FALSE,
  ...
)

Arguments

input.d: data.frame containing data
time: follow up time
status: status indicator
var.name: name of variable to make Kaplan-Meier plots on
var.description: description for var.name
surv.type: survival outcome. Either "os", "dss", "pfs", or "aefs".
shading.colors: colors for survival curves
main: plot title
line.name: names for each survival curve
line.pattern: line type for survival curves
legend: logical; if TRUE, the legend is overlaid on the graph (instead of on the side).
cox.ref.group: specify reference group for cox model i.e. hazard ratio(s)
use.firth: Whether to use Firth's correction for plotting the curves
CI: logical; if TRUE, will plot confidence bands
bold_pval: logical; if TRUE, p-values are bolded if statistically significant at sig.level
sig.level: significance level; default 0.05
HR: logical; if TRUE, will show hazard ratios
show.risk: logical; if TRUE, will show the number of people at risk at each time of death beneath the plot
km.plot.ref.group: specify KM plot reference group; "single" means a lump log-rank statistic
single.test.type: test to use for survival curves. Defaults to "logrank".
use.ggkm: if TRUE, will use function ggkm for plotting
...: additional arguments to other functions and methods

Value

A Kaplan-Meier plot for the specified survival outcome split on the desired variable.

Author

Samuel Leung, Derek Chiu