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[](https://travis-ci.org/kassambara/survminer) [](https://cran.r-project.org/package=survminer) [](https://cran.r-project.org/package=survminer) [](https://cran.r-project.org/package=survminer)
<br/>
- [survminer: Survival Analysis and Visualization](#survminer-survival-analysis-and-visualization)
- [Installation and loading](#installation-and-loading)
- [ggsurvplot: Drawing survival curves](#ggsurvplot-drawing-survival-curves)
- [Fitting survival curves](#fitting-survival-curves)
- [Basic plots](#basic-plots)
- [Computing and passing p-values](#computing-and-passin-p-values)
- [Customized survival curves](#customized-survival-curves)
- [More customized survival curves](#more-customized-survival-curves)
- [Uber customized survival curves](#uber-customized-survival-curves)
- [Uber platinum customized survival curves](#uber-platinum-customized-survival-curves)
- [Uber platinum premium customized survival curves](#uber-platinum-premium-customized-survival-curves)
- [Blog posts](#blog-posts)
survminer: Survival Analysis and Visualization
==============================================
The **survminer** R package provides functions for facilitating **survival analysis** and **visualization**.
The main functions, in the package, are organized in different categories as follow.
**Survival Curves**
<hr/>
<br/>
- **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.
<br/>
**Diagnostics of Cox Model**
<hr/>
<br/>
- **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.
<br/>
**Summary of Cox Model**
<hr/>
<br/>
- **ggforest**(): Draws forest plot for CoxPH model.
- **ggcoxadjustedcurves**(): Plots adjusted survival curves for coxph model.
<br/>
**Competing Risks**
<hr/>
<br/>
- **ggcompetingrisks**(): Plots cumulative incidence curves for competing risks.
<br/>
> Find out more at <http://www.sthda.com/english/rpkgs/survminer/>, and check out the documentation and usage examples of each of the functions in survminer package.
Installation and loading
------------------------
Install from [CRAN](https://cran.r-project.org/package=survminer) as follow:
``` r
install.packages("survminer")
```
Or, install the latest version from [GitHub](https://github.com/kassambara/survminer):
``` r
if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/survminer", build_vignettes = FALSE)
```
Load survminer:
``` r
library("survminer")
```
ggsurvplot: Drawing survival curves
-----------------------------------
### Fitting survival curves
``` r
require("survival")
fit <- survfit(Surv(time, status) ~ sex, data = lung)
```
### Basic plots
``` r
ggsurvplot(fit, data = lung)
```
Censor shape can be changed as follow:
``` r
ggsurvplot(fit, data = lung, censor.shape="|", censor.size = 4)
```
### Customized survival curves
``` r
ggsurvplot(
fit,
data = lung,
size = 1, # change line size
palette =
c("#E7B800", "#2E9FDF"),# custom color palettes
conf.int = TRUE, # Add confidence interval
pval = TRUE, # Add p-value
risk.table = TRUE, # Add risk table
risk.table.col = "strata",# Risk table color by groups
legend.labs =
c("Male", "Female"), # Change legend labels
risk.table.height = 0.25, # Useful to change when you have multiple groups
ggtheme = theme_bw() # Change ggplot2 theme
)
```
Note that, additional arguments are available to customize the main title, axis labels, the font style, axis limits, legends and the number at risk table.
### More customized survival curves
Focus on `xlim` and `break.time.by` parameters which do not change the calculations of estimates of survival surves. Also note `risk.table.y.text.col = TRUE` and `risk.table.y.text = FALSE` that provide bars instead of names in text annotations of the legend of risk table.
``` r
ggsurvplot(
fit, # survfit object with calculated statistics.
data = lung, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimates of survival curves.
xlim = c(0,500), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in days", # customize X axis label.
break.time.by = 100, # break X axis in time intervals by 500.
ggtheme = theme_light(), # customize plot and risk table with a theme.
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
)
```
### Uber customized survival curves
``` r
ggsurv <- ggsurvplot(
fit, # survfit object with calculated statistics.
data = lung, # data used to fit survival curves.
risk.table = TRUE, # show risk table.
pval = TRUE, # show p-value of log-rank test.
conf.int = TRUE, # show confidence intervals for
# point estimates of survival curves.
palette = c("#E7B800", "#2E9FDF"),
xlim = c(0,500), # present narrower X axis, but not affect
# survival estimates.
xlab = "Time in days", # customize X axis label.
break.time.by = 100, # break X axis in time intervals by 500.
ggtheme = theme_light(), # customize plot and risk table with a theme.
risk.table.y.text.col = T,# colour risk table text annotations.
risk.table.height = 0.25, # the height of the risk table
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
ncensor.plot.height = 0.25,
conf.int.style = "step", # customize style of confidence intervals
surv.median.line = "hv", # add the median survival pointer.
legend.labs =
c("Male", "Female") # change legend labels.
)
ggsurv
```
### Uber platinum customized survival curves
``` r
# Changing Labels
# %%%%%%%%%%%%%%%%%%%%%%%%%%
# Labels for Survival Curves (plot)
ggsurv$plot <- ggsurv$plot + labs(
title = "Survival curves",
subtitle = "Based on Kaplan-Meier estimates",
caption = "created with survminer"
)
# Labels for Risk Table
ggsurv$table <- ggsurv$table + labs(
title = "Note the risk set sizes",
subtitle = "and remember about censoring.",
caption = "source code: website.com"
)
# Labels for ncensor plot
ggsurv$ncensor.plot <- ggsurv$ncensor.plot + labs(
title = "Number of censorings",
subtitle = "over the time.",
caption = "source code: website.com"
)
# Changing the font size, style and color
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Applying the same font style to all the components of ggsurv:
# survival curves, risk table and censor part
ggsurv <- ggpar(
ggsurv,
font.title = c(16, "bold", "darkblue"),
font.subtitle = c(15, "bold.italic", "purple"),
font.caption = c(14, "plain", "orange"),
font.x = c(14, "bold.italic", "red"),
font.y = c(14, "bold.italic", "darkred"),
font.xtickslab = c(12, "plain", "darkgreen"),
legend = "top"
)
ggsurv
```
Uber platinum premium customized survival curves
------------------------------------------------
``` r
# Using specific fonts for risk table and ncensor plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Font for Risk Table
ggsurv$table <- ggpar(
ggsurv$table,
font.title = c(13, "bold.italic", "green"),
font.subtitle = c(15, "bold", "pink"),
font.caption = c(11, "plain", "darkgreen"),
font.x = c(8, "bold.italic", "orange"),
font.y = c(11, "bold.italic", "darkgreen"),
font.xtickslab = c(9, "bold", "red")
)
# Font for ncensor plot
ggsurv$ncensor.plot <- ggpar(
ggsurv$ncensor.plot,
font.title = c(13, "bold.italic", "green"),
font.subtitle = c(15, "bold", "pink"),
font.caption = c(11, "plain", "darkgreen"),
font.x = c(8, "bold.italic", "orange"),
font.y = c(11, "bold.italic", "darkgreen"),
font.xtickslab = c(9, "bold", "red")
)
print(ggsurv)
```
Blog posts
----------
- M. Kosiński. R-ADDICT January 2017. [Comparing (Fancy) Survival Curves with Weighted Log-rank Tests](http://r-addict.com/2017/02/09/Fancy-Survival-Plots.html)
- M. Kosiński. R-ADDICT January 2017. [When You Went too Far with Survival Plots During the survminer 1st Anniversary](http://r-addict.com/2017/01/15/Too-Far-With-Survival-Plots.html)
- A. Kassambara. STHDA December 2016. [Survival Analysis Basics: Curves and Logrank Tests](http://www.sthda.com/english/wiki/survival-analysis-basics)
- A. Kassambara. STHDA December 2016. [Cox Proportional Hazards Model](http://www.sthda.com/english/wiki/cox-proportional-hazards-model)
- A. Kassambara. STHDA December 2016. [Cox Model Assumptions](http://www.sthda.com/english/wiki/cox-model-assumptions)
- M. Kosiński. R-ADDICT November 2016. [Determine optimal cutpoints for numerical variables in survival plots](http://r-addict.com/2016/11/21/Optimal-Cutpoint-maxstat.html)
- M. Kosiński. R-ADDICT May 2016. [Survival plots have never been so informative](http://r-addict.com/2016/05/23/Informative-Survival-Plots.html)
- A. Kassambara. STHDA January 2016. [survminer R package: Survival Data Analysis and Visualization](http://www.sthda.com/english/wiki/survminer-r-package-survival-data-analysis-and-visualization).
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