Gallery

The gallery exhibits both default plots as well as the many modifications one can make.

Modifications with ggplot2

Let’s begin with showing the default plot and common modifications that are made with ggplot2 functions.

Expand axis to show percentages from 0% to 100%
Limit plot to show up to 8 years of follow-up
Add the percent sign to the y-axis label
Reduce padding in the plot area around the curves
Add additional tick marks on the x-axis
Update color of the lines
Using the ggplot2 minimal theme

gg_default <-
  survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit() +
  add_confidence_interval() +
  labs(title = "Default")

gg_styled <-
  gg_default +
  coord_cartesian(xlim = c(0, 8)) +
  scale_y_continuous(
    limits = c(0, 1),
    labels = scales::percent, 
    expand = c(0.01, 0)
  ) +
  scale_x_continuous(breaks = 0:9, expand = c(0.02, 0)) +
  scale_color_manual(values = c('#54738E', '#82AC7C')) +
  scale_fill_manual(values = c('#54738E', '#82AC7C')) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(ncol = 1)) +
  labs(title = "Modified",
       y = "Percentage Survival")

gg_default + gg_styled

Grey-scale Figures

You may need a black and white figure and that is achieved using grey-scale ggplot2 functions.

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit() +
  add_confidence_interval() +
  scale_color_grey() +
  scale_fill_grey() +
  labs(title = "Grey Scale")

KMunicate

To get figures that align with the guidelines outlined in “Proposals on Kaplan–Meier plots in medical research and a survey of stakeholder views: KMunicate.”, use the theme_ggsurvfit_KMunicate() theme along with these function options.

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit(linetype_aes = TRUE) +
  add_confidence_interval() +
  add_risktable(
    risktable_stats = c("n.risk", "cum.censor", "cum.event")
  ) +
  theme_ggsurvfit_KMunicate() +
  scale_y_continuous(limits = c(0, 1)) +
  scale_x_continuous(expand = c(0.02, 0)) +
  theme(legend.position = c(0.85, 0.85))

Risk Tables

The default risk table styling is ready for publication.

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit() +
  add_confidence_interval() +
  add_risktable()

You can also group the risk table by the statistics rather than the stratum. Let’s also add additional time points where the statistics are reported and extend the y axis.

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit() +
  add_confidence_interval() +
  add_risktable(risktable_group = "risktable_stats") +
  scale_x_continuous(breaks = 0:9) +
  scale_y_continuous(limits = c(0, 1))

Use add_risktable_strata_symbol() to replace long stratum labels with a color symbol.

Without add_risktable_strata_symbol():

km_long_labels <-
  survfit2(Surv(AVAL, 1 - CNSR) ~ TRT01P, data = adtte) %>%
  ggsurvfit() +
  add_risktable(
    risktable_stats = "n.risk", 
    risktable_group = "risktable_stats",
    risktable_height = 0.2
  ) +
  scale_x_continuous(breaks = 0:5) +
  theme(legend.position = "right")
km_long_labels

With add_risktable_strata_symbol():

km_long_labels +
  add_risktable_strata_symbol()

You can further customize the risk table using themes and the add_risktable(...) arguments. For example, use the following code to increase the font size of both the risk table text and the y-axis label.

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit(size = 0.8) +
  add_risktable(
    risktable_height = 0.33,
    size = 4, # increase font size of risk table statistics
    theme =   # increase font size of risk table title and y-axis label
      list(
        theme_risktable_default(),
        theme(axis.text.y = element_text(size = 11),
              plot.title = element_text(size = 11, face = "bold"))
      )
  )

Quantiles and Censor Markings

Add guidelines for survival quantiles and markings for censored patients using add_quantile() and add_censor_mark().

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit(size = 0.8) +
  add_censor_mark(size = 2, alpha = 0.2) +
  add_quantile(y_value = 0.5, linetype = "dotted", color = "grey50", size = 0.8) +
  add_quantile(y_value = 0.75,  color = "grey50", size = 0.8)

Transformations

Show the probability of an event rather than the probability of being free from the event with transformations. Custom transformations are also available.

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit(type = "risk", size = 0.8) +
  add_confidence_interval()

Faceting

Curves created with ggsurvfit can also later be faceted using ggplot2. Note, however, that faceted curves cannot include a risk table.

The ggsurvfit() function calls tidy_survfit() to create the data frame that is used to create the figure. In the data frame, there is a column named "strata", which we will facet over.

survfit2(Surv(time, status) ~ surg, data = df_colon) %>%
  ggsurvfit() +
  add_confidence_interval() +
  facet_wrap(~strata, nrow = 1) +
  theme(legend.position = "none") +
  scale_x_continuous(n.breaks = 6) +
  labs(title = "PFS by Duration between Surgery and Treatment")