ggsurvplot2: Plot Kaplan-Meier curves with a corresponding table of...
In RichardBirnie/survtools: A collection of useful functions for survival analysis

Description Usage Arguments Value See Also Examples

View source: R/visualisation.R

This function extends ggsurvplot from the survminer package to allow parametric survival curves to be overlaid on the Kaplan-Meier curve. If only the Kaplan-Meier curve is required it may be preferable to use ggsurvplot directly

1 2	ggsurvplot2(fit, psm.curves = NULL, xval = "time", yval = "est", groups = "group", ...)

`fit`	A survfit object containing the Kaplan-Meier estimate of the survival function as returned by `survfit`
`psm.curves`	List. Each element of the list is a data frame containing survival estimates from a parametric survival model as returned by summary.flexsurv. The list must be named. Names should be the distribution used for the survival analysis, e.g. weibull. See example below
`xval`	column name that contains x values (time)
`yval`	column name that contains y values (survival probability)
`groups`	column name that contains groups in your data. e.g. treatment arm.
`...`	Additional arguments to be passed to `ggsurvplot`

An object of class "ggsurvplot" "list". This is essentially a list with two elements. The first element is the plot the second element is the table of number at risk. Both can be extracted and modified using ggplot2 if required.

ggsurvplot

library(survival)
library(survminer)
library(flexsurv)
library(dplyr)
library(survtools)

#create custom colours
BM.blue = rgb(69, 185, 209, max=255)
BM.red = rgb(225, 55, 60, max=255)
BM.yellow = rgb(238, 224, 30, max=255)
BM.pink=rgb(211,78,147,max=255)
BM.Dblue=rgb(0,45,92,max=255)
BM.Dyellow = rgb(214, 200, 16, max=255)

#load some example data from the flexsurv package
#Survival times of 686 patients with primary node positive breast cancer
#censrec = censoring, 1 = dead, 0 = censored
#rectime = time of death or censoring in days
#recyrs = time of death or censoring in years
#group = prognostic group
data(bc)

# Basic Kaplan-Meier curves -----------------------------------------------
#simple Kaplan-Meier estimate
surv.km <- survfit(Surv(recyrs, censrec) ~ group, data = bc)

#edit the strata labels. These are used in the plot legend
names(surv.km$strata) <- gsub('group=', '', names(surv.km$strata))

#Plot basic Kaplan-Meier curves without any parametric curves
#This should be identical to ggsurvplot. Only shown as an example
km.fig <- ggsurvplot2(fit = surv.km, censor = FALSE, risk.table = TRUE, risk.table.height = 0.2, risk.table.y.text.col = FALSE,
                      title = 'Breast Cancer Prognosis', xlim = c(0, 7.5),
                      legend.title = 'Prognosis', legend.labs =  c('KM Good', 'KM Medium', 'KM Poor'),
                      palette = c(BM.red, BM.blue, BM.Dblue) #use this to control colour of lines
                      )

# Fit parametric survival models -------------------------------------------
#fit 6 standard distributions
#this fits the distributions and collects the model fit objects in a list
psm.list <- list(
  exp = flexsurvreg(Surv(recyrs, censrec) ~ group, data = bc, dist = 'exp'),
  weibull = flexsurvreg(Surv(recyrs, censrec) ~ group, data = bc, dist = 'weibull'),
  gompertz = flexsurvreg(Surv(recyrs, censrec) ~ group, data = bc, dist = 'gompertz'),
  llog = flexsurvreg(Surv(recyrs, censrec) ~ group, data = bc, dist = 'llogis'),
  lnorm = flexsurvreg(Surv(recyrs, censrec) ~ group, data = bc, dist = 'lnorm'),
  gengamma = flexsurvreg(Surv(recyrs, censrec) ~ group, data = bc, dist = 'gengamma')
)

#get the fitted survival curve for each distribution and store the results in a list
#this is equivalent to 'summary(model.fit.obj, type = 'survival', tidy = TRUE)' for each distribution
#Each element of the list a data frame with the results of the corresponding model
psm.summ <- lapply(psm.list, summary, type = 'survival', tidy = TRUE)

#Edit the group column to label all the permutations of group and model
for(i in 1:length(psm.summ)){
  psm.summ[[i]]$group <- paste0(names(psm.summ)[i], ' ', psm.summ[[i]]$group)
}

# Overlay parametric curves on Kaplan-Meier -------------------------------
#plot the results
#The base layer of the plot is the Kaplan Meier curve. The parametric curves are
#added as extra layers on top.
#The following arguments are specific to ggsurvplot2: psm.curves, xval, yval, groups
#All other arguments are passed directly to ggsurvplot
surv.fig <- ggsurvplot2(fit = surv.km, psm.curves = psm.summ, data = bc, xval = 'time', yval = 'est', groups = 'group',
                        censor = FALSE, risk.table = TRUE, risk.table.height = 0.2, risk.table.y.text.col = FALSE,
                        title = 'Breast Cancer Prognosis', xlim = c(0, 7.5),
                        legend.title = 'Prognosis', legend.labs =  c('KM Good', 'KM Medium', 'KM Poor'),
                        palette = rep(c(BM.red, BM.blue, BM.Dblue), length(psm.summ)+1) #use this to control colour of lines
                       )
surv.fig

# Example with a single group ---------------------------------------------
#basic KM
single.km <- survfit(Surv(recyrs, censrec) ~ 1, data = bc)

#Plot basic Kaplan-Meier curves without any parametric curves
#This should be identical to ggsurvplot. Only shown as an example
km.fig <- ggsurvplot2(fit = single.km, censor = FALSE, risk.table = TRUE, risk.table.height = 0.2, risk.table.y.text.col = FALSE,
                      title = 'Breast Cancer Prognosis', xlim = c(0, 7.5), conf.int = FALSE,
                      legend.title = '', legend.labs =  'All', palette = 'black'
)

km.fig

# Fit parametric survival models -------------------------------------------
#fit 6 standard distributions
#this fits the distributions and collects the model fit objects in a list
psm.list.single <- list(
  exp = flexsurvreg(Surv(recyrs, censrec) ~ 1, data = bc, dist = 'exp'),
  weibull = flexsurvreg(Surv(recyrs, censrec) ~ 1, data = bc, dist = 'weibull'),
  gompertz = flexsurvreg(Surv(recyrs, censrec) ~ 1, data = bc, dist = 'gompertz'),
  llog = flexsurvreg(Surv(recyrs, censrec) ~ 1, data = bc, dist = 'llogis'),
  lnorm = flexsurvreg(Surv(recyrs, censrec) ~ 1, data = bc, dist = 'lnorm'),
  gengamma = flexsurvreg(Surv(recyrs, censrec) ~ 1, data = bc, dist = 'gengamma')
)

#get the fitted survival curve for each distribution and store the results in a list
#this is equivalent to 'summary(model.fit.obj, type = 'survival', tidy = TRUE)' for each distribution
#Each element of the list a data frame with the results of the corresponding model
psm.summ.single <- lapply(psm.list.single, summary, type = 'survival', tidy = TRUE)

#Add a dummy group column
for(i in 1:length(psm.summ.single)){
  psm.summ.single[[i]]$group <- names(psm.summ.single)[i]
}

# Overlay parametric curves on Kaplan-Meier -------------------------------
#plot the results
#The base layer of the plot is the Kaplan Meier curve. The parametric curves are
#added as extra layers on top.
#The following arguments are specific to ggsurvplot2: psm.curves, xval, yval, groups
#All other arguments are passed directly to ggsurvplot
single.fig <- ggsurvplot2(fit = single.km, psm.curves = psm.summ.single, data = bc, xval = 'time', yval = 'est', groups = 'group',
                          break.time.by = 1, censor = FALSE, conf.int = FALSE, risk.table = TRUE, risk.table.height = 0.2,
                          risk.table.y.text.col = FALSE, title = 'Breast Cancer Prognosis', xlim = c(0, 8),
                          legend.title = 'Prognosis', legend.labs =  c('KM'),
                          palette = c(BM.red, BM.blue, BM.Dblue, 'black', BM.Dyellow, BM.pink, BM.yellow) #use this to control colour of lines
)
single.fig