survminer cheatsheet

The cheatsheet can be downloaded from STHDA and from Rstudio. It contains selected important functions, such as:

• ggsurvplot() for plotting survival curves
• ggcoxzph() and ggcoxdiagnostics() for assessing the assumtions of the Cox model
• ggforest() and ggcoxadjustedcurves() for summarizing a Cox model

Additional functions, that you might find helpful, are briefly described in the next section.

survminer overview

The main functions, in the package, are organized in different categories as follow.

• ggsurvplot(): Draws survival curves with the ‘number at risk’ table, the cumulative number of events table and the cumulative number of censored subjects table.

• arrange_ggsurvplots(): Arranges multiple ggsurvplots on the same page.

• ggsurvevents(): Plots the distribution of event’s times.

• surv_summary(): Summary of a survival curve. Compared to the default summary() function, surv_summary() creates a data frame containing a nice summary from survfit results.

• surv_cutpoint(): Determines the optimal cutpoint for one or multiple continuous variables at once. Provides a value of a cutpoint that correspond to the most significant relation with survival.

• pairwise_survdiff(): Multiple comparisons of survival curves. Calculate pairwise comparisons between group levels with corrections for multiple testing.

• ggcoxzph(): Graphical test of proportional hazards. Displays a graph of the scaled Schoenfeld residuals, along with a smooth curve using ggplot2. Wrapper around plot.cox.zph().

• ggcoxdiagnostics(): Displays diagnostics graphs presenting goodness of Cox Proportional Hazards Model fit.

• ggcoxfunctional(): Displays graphs of continuous explanatory variable against martingale residuals of null cox proportional hazards model. It helps to properly choose the functional form of continuous variable in cox model.

• ggforest(): Draws forest plot for CoxPH model.

• ggcoxadjustedcurves(): Plots adjusted survival curves for coxph model.

• ggcompetingrisks(): Plots cumulative incidence curves for competing risks.

Find out more at https://www.sthda.com/english/rpkgs/survminer/, and check out the documentation and usage examples of each of the functions in survminer package.

Infos

This analysis has been performed using R software (ver. 3.3.2).

Release notes

In this post, we present only the most important changes in v0.3.0. See the release notes for a complete list.

Installing and loading survminer

Install the latest developmental version from GitHub:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/survminer", build_vignettes = TRUE)

Or, install the latest release from CRAN as follow:

install.packages("survminer")

To load survminer in R, type this:

library("survminer")

Survival curves

New arguments displaying supplementary survival tables - cumulative events & censored subjects - under the main survival curves:

# Fit survival curves
require("survival")
fit <- survfit(Surv(time, status) ~ sex, data = lung)

# Plot informative survival curves
library("survminer")
ggsurvplot(fit, data = lung,
           title = "Survival Curves",
           pval = TRUE, pval.method = TRUE,    # Add p-value &  method name
           surv.median.line = "hv",            # Add median survival lines
           legend.title = "Sex",               # Change legend titles
           legend.labs = c("Male", "female"),  # Change legend labels
           palette = "jco",                    # Use JCO journal color palette
           risk.table = TRUE,                  # Add No at risk table
           cumevents = TRUE,                   # Add cumulative No of events table
           tables.height = 0.15,               # Specify tables height
           tables.theme = theme_cleantable(),  # Clean theme for tables
           tables.y.text = FALSE               # Hide tables y axis text
)

survminer

Cumulative events and censored tables are good additional feedback to survival curves, so that one could realize: what is the number of risk set AND what is the cause that the risk set become smaller: is it caused by events or by censored events?

Arranging multiple ggsurvplots on the same page

The function arrange_ggsurvplots() [in survminer] can be used to arrange multiple ggsurvplots on the same page.

# List of ggsurvplots
splots <- list()
splots[[1]] <- ggsurvplot(fit, data = lung,
                          risk.table = TRUE,
                          tables.y.text = FALSE,
                          ggtheme = theme_light())

splots[[2]] <- ggsurvplot(fit, data = lung,
                          risk.table = TRUE,
                          tables.y.text = FALSE,
                          ggtheme = theme_grey())

# Arrange multiple ggsurvplots and print the output
arrange_ggsurvplots(splots, print = TRUE,
  ncol = 2, nrow = 1, risk.table.height = 0.25)

survminer

If you want to save the output into a pdf, type this:

# Arrange and save into pdf file
res <- arrange_ggsurvplots(splots, print = FALSE)
ggsave("myfile.pdf", res)

Distribution of events’ times

The function ggsurvevents() [in survminer] calculates and plots the distribution for events (both status = 0 and status = 1). It helps to notice when censoring is more common (@pbiecek, #116). This is an alternative to cumulative events and censored tables, described in the previous section.

For example in colon dataset, as illustrated below, censoring occur mostly after the 6’th year:

require("survival")
surv <- Surv(colon$time, colon$status)
ggsurvevents(surv)

survminer

Adjusted survival curves for Cox model

Adjusted survival curves show how a selected factor influences survival estimated from a cox model. If you want read more about why we need to adjust survival curves, see this document: Adjusted survival curves.

Briefly, in clinical investigations, there are many situations, where several known factors, potentially affect patient prognosis. For example, suppose two groups of patients are compared: those with and those without a specific genotype. If one of the groups also contains older individuals, any difference in survival may be attributable to genotype or age or indeed both. Hence, when investigating survival in relation to any one factor, it is often desirable to adjust for the impact of others.

The cox proportional-hazards model is one of the most important methods used for modelling survival analysis data.

Here, we present the function ggcoxadjustedcurves() [in survminer] for plotting adjusted survival curves for cox proportional hazards model. The ggcoxadjustedcurves() function models the risks due to the confounders as described in the section 5.2 of this article: Terry M Therneau (2015); Adjusted survival curves. Briefly, the key idea is to predict survival for all individuals in the cohort, and then take the average of the predicted curves by groups of interest (for example, sex, age, genotype groups, etc.).

# Data preparation and computing cox model
library(survival)
lung$sex <- factor(lung$sex, levels = c(1,2),
                   labels = c("Male", "Female"))
res.cox <- coxph(Surv(time, status) ~ sex + age + ph.ecog, data =  lung)

# Plot the baseline survival function
# with showing all individual predicted surv. curves
ggcoxadjustedcurves(res.cox, data = lung,
                    individual.curves = TRUE)

survminer

# Adjusted survival curves for the variable "sex"
ggcoxadjustedcurves(res.cox, data = lung,
                   variable  = lung[, "sex"],   # Variable of interest
                   legend.title = "Sex",        # Change legend title
                   palette = "npg",             # nature publishing group color palettes
                   curv.size = 2                # Change line size
                   )

survminer

Graphical summary of Cox model

The function ggforest() [in survminer] can be used to create a graphical summary of a Cox model, also known as forest plot. For each covariate, it displays the hazard ratio (HR) and the 95% confidence intervals of the HR. By default, covariates with significant p-value are highlighted in red.

# Fit a Cox model
library(survival)
res.cox <- coxph(Surv(time, status) ~ sex + age + ph.ecog, data =  lung)
res.cox

## Call:
## coxph(formula = Surv(time, status) ~ sex + age + ph.ecog, data = lung)
##
##               coef exp(coef) se(coef)     z       p
## sexFemale -0.55261   0.57544  0.16774 -3.29 0.00099
## age        0.01107   1.01113  0.00927  1.19 0.23242
## ph.ecog    0.46373   1.58999  0.11358  4.08 4.4e-05
##
## Likelihood ratio test=30.5  on 3 df, p=1.08e-06
## n= 227, number of events= 164
##    (1 observation deleted due to missingness)

# Create a forest plot
ggforest(res.cox)

survminer

Pairwise comparisons for survival curves

When you compare three or more survival curves at once, the function survdiff() [in survival package] returns a global p-value whether to reject or not the null hypothesis.

With this, you know that a difference exists between groups, but you don’t know where. You can’t know until you test each combination.

Therefore, we implemented the function pairwise_survdiff() [in survminer]. It calculates pairwise comparisons between group levels with corrections for multiple testing.

• Multiple survival curves with global p-value:

library("survival")
library("survminer")
# Survival curves with global p-value
data(myeloma)
fit2 <- survfit(Surv(time, event) ~ molecular_group, data = myeloma)
ggsurvplot(fit2, data = myeloma,
           legend.title = "Molecular Group",
           legend.labs = levels(myeloma$molecular_group),
           legend = "right",
           pval = TRUE, palette = "lancet")

survminer

• Pairwise survdiff:

# Pairwise survdiff
res <- pairwise_survdiff(Surv(time, event) ~ molecular_group,
     data = myeloma)
res

##
##  Pairwise comparisons using Log-Rank test
##
## data:  myeloma and molecular_group
##
##                  Cyclin D-1 Cyclin D-2 Hyperdiploid Low bone disease MAF   MMSET
## Cyclin D-2       0.723      -          -            -                -     -
## Hyperdiploid     0.328      0.103      -            -                -     -
## Low bone disease 0.644      0.447      0.723        -                -     -
## MAF              0.943      0.723      0.103        0.523            -     -
## MMSET            0.103      0.038      0.527        0.485            0.038 -
## Proliferation    0.723      0.988      0.103        0.485            0.644 0.062
##
## P value adjustment method: BH

• Symbolic number coding:

# Symbolic number coding
symnum(res$p.value, cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1),
   symbols = c("****", "***", "**", "*", "+", " "),
   abbr.colnames = FALSE, na = "")

##                  Cyclin D-1 Cyclin D-2 Hyperdiploid Low bone disease MAF MMSET
## Cyclin D-2
## Hyperdiploid
## Low bone disease
## MAF
## MMSET                       *                                        *
## Proliferation                                                            +
## attr(,"legend")
## [1] 0 '****' 1e-04 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1 \t    ## NA: ''

Visualizing competing risk analysis

Competing risk events refer to a situation where an individual (patient) is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these will prevent any other event from ever happening.

For example, when studying relapse in patients who underwent HSCT (Hematopoietic stem cell transplantation), transplant related mortality is a competing risk event and the cumulative incidence function (CIF) must be calculated by appropriate accounting.

A ‘competing risks’ analysis is implemented in the R package cmprsk. Here, we provide the ggcompetingrisks() function [in survminer] to plot the results using ggplot2-based elegant data visualization.

# Create a demo data set
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
set.seed(2)
failure_time <- rexp(100)
status <- factor(sample(0:2, 100, replace=TRUE), 0:2,
                 c(&#39;no event&#39;, &#39;death&#39;, &#39;progression&#39;))
disease <- factor(sample(1:3,  100,replace=TRUE), 1:3,
                  c(&#39;BRCA&#39;,&#39;LUNG&#39;,&#39;OV&#39;))

# Cumulative Incidence Function
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
require(cmprsk)
fit3 <- cuminc(ftime = failure_time, fstatus = status,
              group = disease)

# Visualize
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ggcompetingrisks(fit3, palette = "Dark2",
                 legend = "top",
                 ggtheme = theme_bw())

survminer

The ggcometingrisks() function has also support for multi-state survival objects (type = “mstate”), where the status variable can have multiple levels. The first of these will stand for censoring, and the others for various event types, e.g., causes of death.

# Data preparation
df <- data.frame(time = failure_time, status = status,
                 group = disease)

# Fit multi-state survival
library(survival)
fit5 <- survfit(Surv(time, status, type = "mstate") ~ group, data = df)
ggcompetingrisks(fit5, palette = "jco")

survminer

Related articles

• Survival Analysis
  • Survival Analysis Basics: Curves and Logrank Tests
  • Cox Proportional Hazards Model
  • Cox Model Assumptions

Infos

This analysis has been performed using R software (ver. 3.3.2).

New features

• New function surv_summary() for creating data frame containing a nice summary of survival curves (#64).
• It’s possible now to facet the output of ggsurvplot() by one or more factors (#64).
• Now, ggsurvplot() can be used to plot cox model (#67).
• New functions added for determining and visualizing the optimal cutpoint of continuous variables for survival analyses:
  • surv_cutpoint(): Determine the optimal cutpoint for each variable using ‘maxstat’. Methods defined for surv_cutpoint object are summary(), print() and plot().
  • surv_categorize(): Divide each variable values based on the cutpoint returned by surv_cutpoint() (#41).
• New argument ‘ncensor.plot’ added to ggsurvplot(). A logical value. If TRUE, the number of censored subjects at time t is plotted. Default is FALSE (#18).

Minor changes

• New argument ‘conf.int.style’ added in ggsurvplot() for changing the style of confidence interval bands.
• Now, ggsurvplot() plots a stepped confidence interval when conf.int = TRUE (#65).
• ggsurvplot() updated for compatibility with the future version of ggplot2 (v2.2.0) (#68)
• ylab is now automatically adapted according to the value of the argument fun. For example, if fun = “event”, then ylab will be “Cumulative event”.
• In ggsurvplot(), linetypes can now be adjusted by variables used to fit survival curves (#46)
• In ggsurvplot(), the argument risk.table can be either a logical value (TRUE|FALSE) or a string (“absolute”, “percentage”). If risk.table = “absolute”, ggsurvplot() displays the absolute number of subjects at risk. If risk.table = “percentage”, the percentage at risk is displayed. Use “abs_pct” to show both the absolute number and the percentage of subjects at risk. (#70).
• New argument surv.median.line in ggsurvplot(): character vector for drawing a horizontal/vertical line at median (50%) survival. Allowed values include one of c(“none”, “hv”, “h”, “v”). v: vertical, h:horizontal (#61).
• Now, the default theme of ggcoxdiagnostics() is ggplot2::theme_bw().

Bug fixes

It also includes numerous bug fixes as described in the release notes: v0.2.3 and v0.2.4

Summary of survival curves

Compared to the default summary() function, the surv_summary() function [in survminer] creates a data frame containing a nice summary from survfit results.

# Fit survival curves
require("survival")
fit <- survfit(Surv(time, status) ~ sex, data = lung)

# Summarize
library("survminer")
res.sum <- surv_summary(fit)
head(res.sum)

##   time n.risk n.event n.censor      surv    std.err     upper     lower
## 1   11    138       3        0 0.9782609 0.01268978 1.0000000 0.9542301
## 2   12    135       1        0 0.9710145 0.01470747 0.9994124 0.9434235
## 3   13    134       2        0 0.9565217 0.01814885 0.9911586 0.9230952
## 4   15    132       1        0 0.9492754 0.01967768 0.9866017 0.9133612
## 5   26    131       1        0 0.9420290 0.02111708 0.9818365 0.9038355
## 6   30    130       1        0 0.9347826 0.02248469 0.9768989 0.8944820
##   strata sex
## 1  sex=1   1
## 2  sex=1   1
## 3  sex=1   1
## 4  sex=1   1
## 5  sex=1   1
## 6  sex=1   1

# Information about the survival curves
attr(res.sum, "table")

##       records n.max n.start events   *rmean *se(rmean) median 0.95LCL
## sex=1     138   138     138    112 325.0663   22.59845    270     212
## sex=2      90    90      90     53 458.2757   33.78530    426     348
##       0.95UCL
## sex=1     310
## sex=2     550

Plot survival curves

ggsurvplot(
   fit,                     # survfit object with calculated statistics.
   pval = TRUE,             # show p-value of log-rank test.
   conf.int = TRUE,         # show confidence intervals for 
                            # point estimaes of survival curves.
   #conf.int.style = "step",  # customize style of confidence intervals
   xlab = "Time in days",   # customize X axis label.
   break.time.by = 200,     # break X axis in time intervals by 200.
   ggtheme = theme_light(), # customize plot and risk table with a theme.
   risk.table = "abs_pct",  # absolute number and percentage at risk.
  risk.table.y.text.col = T,# colour risk table text annotations.
  risk.table.y.text = FALSE,# show bars instead of names in text annotations
                            # in legend of risk table.
  ncensor.plot = TRUE,      # plot the number of censored subjects at time t
  surv.median.line = "hv",  # add the median survival pointer.
  legend.labs = 
    c("Male", "Female"),    # change legend labels.
  palette = 
    c("#E7B800", "#2E9FDF") # custom color palettes.
)

survminer

Determine the optimal cutpoint for continuous variables

The survminer package determines the optimal cutpoint for one or multiple continuous variables at once, using the maximally selected rank statistics from the ‘maxstat’ R package. To learn more, read this: M. Kosiński. R-ADDICT November 2016. Determine optimal cutpoints for numerical variables in survival plots.

Here, we’ll use the myeloma data sets [in the survminer package]. It contains survival data and some gene expression data obtained from multiple myeloma patients.

# 0. Load some data
data(myeloma)
head(myeloma[, 1:8])

##          molecular_group chr1q21_status treatment event  time   CCND1
## GSM50986      Cyclin D-1       3 copies       TT2     0 69.24  9908.4
## GSM50988      Cyclin D-2       2 copies       TT2     0 66.43 16698.8
## GSM50989           MMSET       2 copies       TT2     0 66.50   294.5
## GSM50990           MMSET       3 copies       TT2     1 42.67   241.9
## GSM50991             MAF                  TT2     0 65.00   472.6
## GSM50992    Hyperdiploid       2 copies       TT2     0 65.20   664.1
##          CRIM1 DEPDC1
## GSM50986 420.9  523.5
## GSM50988  52.0   21.1
## GSM50989 617.9  192.9
## GSM50990  11.9  184.7
## GSM50991  38.8  212.0
## GSM50992  16.9  341.6

# 1. Determine the optimal cutpoint of variables
res.cut <- surv_cutpoint(myeloma, time = "time", event = "event",
   variables = c("DEPDC1", "WHSC1", "CRIM1"))

summary(res.cut)

##        cutpoint statistic
## DEPDC1    279.8  4.275452
## WHSC1    3205.6  3.361330
## CRIM1      82.3  1.968317

# 2. Plot cutpoint for DEPDC1
# palette = "npg" (nature publishing group), see ?ggpubr::ggpar
plot(res.cut, "DEPDC1", palette = "npg")

## $DEPDC1

survminer

# 3. Categorize variables
res.cat <- surv_categorize(res.cut)
head(res.cat)

##           time event DEPDC1 WHSC1 CRIM1
## GSM50986 69.24     0   high   low  high
## GSM50988 66.43     0    low   low   low
## GSM50989 66.50     0    low  high  high
## GSM50990 42.67     1    low  high   low
## GSM50991 65.00     0    low   low   low
## GSM50992 65.20     0   high   low   low

# 4. Fit survival curves and visualize
library("survival")
fit <- survfit(Surv(time, event) ~DEPDC1, data = res.cat)
ggsurvplot(fit, risk.table = TRUE, conf.int = TRUE)

survminer

Facet the output of ggsurvplot()

In this section, we’ll compute survival curves using the combination of multiple factors. Next, we’ll factet the output of ggsurvplot() by a combination of factors

1. Fit (complex) survival curves using colon data sets

require("survival")
fit2 <- survfit( Surv(time, status) ~ sex + rx + adhere,
                data = colon )

2. Visualize the output using survminer

ggsurv <- ggsurvplot(fit2, fun = "event", conf.int = TRUE,
  risk.table = TRUE, risk.table.col="strata", 
  ggtheme = theme_bw())

ggsurv

survminer

3. Faceting survival curves. The plot below shows survival curves by the sex variable faceted according to the values of rx & adhere.

curv_facet <- ggsurv$plot + facet_grid(rx ~ adhere)
curv_facet

survminer

4. Facetting risk tables: Generate risk table for each facet plot item

ggsurv$table + facet_grid(rx ~ adhere, scales = "free")+
 theme(legend.position = "none")

survminer

5. Generate risk table for each facet columns

tbl_facet <- ggsurv$table + facet_grid(.~ adhere, scales = "free")
tbl_facet + theme(legend.position = "none")

survminer

# Arrange faceted survival curves and risk tables
g2 <- ggplotGrob(curv_facet)
g3 <- ggplotGrob(tbl_facet)
min_ncol <- min(ncol(g2), ncol(g3))
g <- gridExtra::rbind.gtable(g2[, 1:min_ncol], g3[, 1:min_ncol], size="last")
g$widths <- grid::unit.pmax(g2$widths, g3$widths)
grid::grid.newpage()
grid::grid.draw(g)

survminer

Related articles

• Survival Analysis Basics: Curves and Logrank Tests
• Cox Proportional Hazards Model
• Cox Model Assumptions

Infos

This analysis has been performed using R software (ver. 3.3.2).

survminer - Main features

The current version contains the function ggsurvplot() for easily drawing beautiful and ready-to-publish survival curves using ggplot2. ggsurvplot() includes also some options for displaying the p-value and the ‘number at risk’ table, under the survival curves.

Installation and loading

Install from CRAN:

install.packages("survminer")

Or, install the latest version from GitHub:

# Install
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/survminer")

# Loading
library("survminer")

Getting started

The R package survival is required for fitting survival curves.

# Fit survival curves
require("survival")
fit <- survfit(Surv(time, status) ~ 1, data = lung)

# Drawing curves
ggsurvplot(fit, color = "#2E9FDF")

# Fit survival curves
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)

# Drawing survival curves
ggsurvplot(fit)

# Change font style, size and color
#++++++++++++++++++++++++++++++++++++
# Change only font size
ggsurvplot(fit, main = "Survival curve",
   font.main = 18,
   font.x =  16,
   font.y = 16,
   font.tickslab = 14)

# Change font size, style and color at the same time
ggsurvplot(fit, main = "Survival curve",
   font.main = c(16, "bold", "darkblue"),
   font.x = c(14, "bold.italic", "red"),
   font.y = c(14, "bold.italic", "darkred"),
   font.tickslab = c(12, "plain", "darkgreen"))

# Change the legend title and labels
ggsurvplot(fit, legend = "bottom", 
           legend.title = "Sex",
           legend.labs = c("Male", "Female"))

# Specify legend position by its coordinates
ggsurvplot(fit, legend = c(0.2, 0.2))

# change line size --> 1
# Change line types by groups (i.e. "strata")
# and change color palette
ggsurvplot(fit,  size = 1,  # change line size
           linetype = "strata", # change line type by groups
           break.time.by = 250, # break time axis by 250
           palette = c("#E7B800", "#2E9FDF"), # custom color palette
           conf.int = TRUE, # Add confidence interval
           pval = TRUE # Add p-value
           )

# Use brewer color palette "Dark2"
ggsurvplot(fit, linetype = "strata", 
           conf.int = TRUE, pval = TRUE,
           palette = "Dark2")

# Use grey palette
ggsurvplot(fit, linetype = "strata", 
           conf.int = TRUE, pval = TRUE,
           palette = "grey")

# Add risk table
# and change risk table y text colors by strata
ggsurvplot(fit, pval = TRUE, conf.int = TRUE,
           risk.table = TRUE, risk.table.y.text.col = TRUE)

# Customize the output and then print
res <- ggsurvplot(fit, pval = TRUE, conf.int = TRUE,
           risk.table = TRUE)
res$table <- res$table + theme(axis.line = element_blank())
res$plot <- res$plot + labs(title = "Survival Curves")
print(res)

# Change color, linetype by strata, risk.table color by strata
ggsurvplot(fit, 
           pval = TRUE, conf.int = TRUE,
           risk.table = TRUE, # Add risk table
           risk.table.col = "strata", # Change risk table color by groups
           linetype = "strata", # Change line type by groups
           ggtheme = theme_bw(), # Change ggplot2 theme
           palette = c("#E7B800", "#2E9FDF"))

# Change x axis limits (xlim)
#++++++++++++++++++++++++++++++++++++
# One would like to cut axes at a specific time point
ggsurvplot(fit, 
           pval = TRUE, conf.int = TRUE,
           risk.table = TRUE, # Add risk table
           risk.table.col = "strata", # Change risk table color by groups
           ggtheme = theme_bw(), # Change ggplot2 theme
           palette = "Dark2",
           xlim = c(0, 600))

# Plot cumulative events
ggsurvplot(fit, conf.int = TRUE,
           palette = c("#FF9E29", "#86AA00"),
           risk.table = TRUE, risk.table.col = "strata",
           fun = "event")

# Plot the cumulative hazard function
ggsurvplot(fit, conf.int = TRUE, 
           palette = c("#FF9E29", "#86AA00"),
           risk.table = TRUE, risk.table.col = "strata",
           fun = "cumhaz")

# Arbitrary function
ggsurvplot(fit, conf.int = TRUE, 
          palette = c("#FF9E29", "#86AA00"),
           risk.table = TRUE, risk.table.col = "strata",
           pval = TRUE,
           fun = function(y) y*100)

# Fit (complexe) survival curves
#++++++++++++++++++++++++++++++++++++

require("survival")
fit2 <- survfit( Surv(time, status) ~ rx + adhere,
    data = colon )

# Visualize
#++++++++++++++++++++++++++++++++++++

# Visualize: add p-value, chang y limits
# change color using brewer palette
ggsurvplot(fit2, pval = TRUE, 
           break.time.by = 800,
           risk.table = TRUE,
           risk.table.height = 0.5#Useful when you have multiple groups
           )

# Adjust risk table and survival plot heights
# ++++++++++++++++++++++++++++++++++++
# Risk table height
ggsurvplot(fit2, pval = TRUE,
          break.time.by = 800,
          risk.table = TRUE,
          risk.table.col = "strata",
          risk.table.height = 0.5, 
          palette = "Dark2")

# Change legend labels
# ++++++++++++++++++++++++++++++++++++

ggsurvplot(fit2, pval = TRUE, 
           break.time.by = 800,
           risk.table = TRUE,
           risk.table.col = "strata",
           risk.table.height = 0.5, 
           ggtheme = theme_bw(),
           legend.labs = c("A", "B", "C", "D", "E", "F"))

Infos

This article was built with:

##  setting  value                       
##  version  R version 3.2.3 (2015-12-10)
##  system   x86_64, darwin13.4.0        
##  ui       X11                         
##  language (EN)                        
##  collate  fr_FR.UTF-8                 
##  tz       Europe/Paris                
##  date     2016-02-21                  
## 
##  package      * version date       source        
##  colorspace     1.2-6   2015-03-11 CRAN (R 3.2.0)
##  dichromat      2.0-0   2013-01-24 CRAN (R 3.2.0)
##  digest         0.6.9   2016-01-08 CRAN (R 3.2.3)
##  ggplot2      * 2.0.0   2015-12-18 CRAN (R 3.2.3)
##  gridExtra      2.0.0   2015-07-14 CRAN (R 3.2.0)
##  gtable         0.1.2   2012-12-05 CRAN (R 3.2.0)
##  labeling       0.3     2014-08-23 CRAN (R 3.2.0)
##  magrittr       1.5     2014-11-22 CRAN (R 3.2.0)
##  MASS           7.3-45  2015-11-10 CRAN (R 3.2.3)
##  munsell        0.4.3   2016-02-13 CRAN (R 3.2.3)
##  plyr           1.8.3   2015-06-12 CRAN (R 3.2.0)
##  RColorBrewer   1.1-2   2014-12-07 CRAN (R 3.2.0)
##  Rcpp           0.12.3  2016-01-10 CRAN (R 3.2.3)
##  reshape2       1.4.1   2014-12-06 CRAN (R 3.2.0)
##  scales         0.3.0   2015-08-25 CRAN (R 3.2.0)
##  stringi        1.0-1   2015-10-22 CRAN (R 3.2.0)
##  stringr        1.0.0   2015-04-30 CRAN (R 3.2.0)
##  survival     * 2.38-3  2015-07-02 CRAN (R 3.2.3)
##  survminer    * 0.2.0   2016-02-18 CRAN (R 3.2.3)