kaplan_meier_plot {survivalAnalysis}

Kaplan Meier plots from survival results.

Description

Uses ggsurvplot from the survminer package to create publication-ready plots.

Usage

kaplan_meier_plot(..., mapped_plot_args = list(), p_lessthan_cutoff = 0.001)

Arguments

...

One or many SurvivalAnalysisResult objects as returned by analyse_survival and arguments that will be passed to ggsurvplot. Bare lists will be spliced. If using lists, the same argument may be contained in multiple lists; in this case, the last occurrence is used, i.e. you can first pass a list with default arguments, and then override some of them. In addition to all arguments supported by ggsurvplot, these arguments and shortcuts can be used additionally:

• break.time.by: breakByYear, breakByHalfYear, breakByQuarterYear, breakByMonth (numeric value only in ggsurvplot)

• xscale: scaleByYear, scaleByMonth (numeric value only in ggsurvplot)

• hazard.ratio (logical): display hazard ratios in addition to p value, complementing pval=T

• xlab: {.OS,.PFS,.TTF,.DFS}.{years,months,days}

• table.layout: clean, displays risk table only with color code and number, no grid, axes or labels. (do not forget risk.table=TRUE to see something)

• papersize: numeric vector of length 2, c(width, height), unit inches. kaplan_meier_plot will store a "papersize" attribute with this value which is read by save_pdf

mapped_plot_args

Optionally, if given n objects to plot, a named list of vectors of size n. The name is an argument names passed to ggsurvplot. The elements of the vector will be mapped 1:1 to each object. This allows to perform batch plotting where only few arguments differ (e.g. titles A, B, C...) between the plots.

p_lessthan_cutoff

The lower limit below which p value will be displayed as "less than". If p_lessthan_cutoff == 0.001, the a p value of 0.002 will be displayed as is, while 0.0005 will become "p < 0.001".

Value

If given one result to plot, one ggsurvplot object; if given more than one result, a list of ggsurvplot objects.

Examples

library(magrittr)
library(dplyr)
survival::aml %>%
  analyse_survival(vars(time, status), x) %>%
  kaplan_meier_plot(break.time.by="breakByMonth",
                    xlab=".OS.months",
                    risk.table=TRUE,
                    ggtheme=ggplot2::theme_bw(10))