- 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.

**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://rpkgs.datanovia.com/survminer/, and check out the documentation and usage examples of each of the functions in survminer package.

Install from CRAN as follow:

Or, install the latest version from GitHub:

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

Load survminer:

Censor shape can be changed as follow:

```
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.

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.

```
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
)
```

```
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
```

Helper function to customize plot labels:

```
customize_labels <- function (p, font.title = NULL,
font.subtitle = NULL, font.caption = NULL,
font.x = NULL, font.y = NULL, font.xtickslab = NULL, font.ytickslab = NULL)
{
original.p <- p
if(is.ggplot(original.p)) list.plots <- list(original.p)
else if(is.list(original.p)) list.plots <- original.p
else stop("Can't handle an object of class ", class (original.p))
.set_font <- function(font){
font <- ggpubr:::.parse_font(font)
ggtext::element_markdown (size = font$size, face = font$face, colour = font$color)
}
for(i in 1:length(list.plots)){
p <- list.plots[[i]]
if(is.ggplot(p)){
if (!is.null(font.title)) p <- p + theme(plot.title = .set_font(font.title))
if (!is.null(font.subtitle)) p <- p + theme(plot.subtitle = .set_font(font.subtitle))
if (!is.null(font.caption)) p <- p + theme(plot.caption = .set_font(font.caption))
if (!is.null(font.x)) p <- p + theme(axis.title.x = .set_font(font.x))
if (!is.null(font.y)) p <- p + theme(axis.title.y = .set_font(font.y))
if (!is.null(font.xtickslab)) p <- p + theme(axis.text.x = .set_font(font.xtickslab))
if (!is.null(font.ytickslab)) p <- p + theme(axis.text.y = .set_font(font.ytickslab))
list.plots[[i]] <- p
}
}
if(is.ggplot(original.p)) list.plots[[1]]
else list.plots
}
```

Customized plot labels:

```
# 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 <- customize_labels(
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")
)
ggsurv
```

```
# Using specific fonts for risk table and ncensor plots
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Font for Risk Table
ggsurv$table <- customize_labels(
ggsurv$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$ncensor.plot <- customize_labels(
ggsurv$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)
```

M. Kosiński. R-ADDICT January 2017. Comparing (Fancy) Survival Curves with Weighted Log-rank Tests

M. Kosiński. R-ADDICT January 2017. When You Went too Far with Survival Plots During the survminer 1st Anniversary

A. Kassambara. STHDA December 2016. Survival Analysis Basics: Curves and Logrank Tests

A. Kassambara. STHDA December 2016. Cox Proportional Hazards Model

A. Kassambara. STHDA December 2016. Cox Model Assumptions

M. Kosiński. R-ADDICT November 2016. Determine optimal cutpoints for numerical variables in survival plots

M. Kosiński. R-ADDICT May 2016. Survival plots have never been so informative

A. Kassambara. STHDA January 2016. survminer R package: Survival Data Analysis and Visualization.