When You Went too Far with Survival Plots During the survminer 1st Anniversary

We are celebrating the 1st anniversary of the survminer’s release on CRAN! Due to that fact I have prepared the most (uber platinum) customized survival plot that I could imagine. I went too far because it took over 30 parameters to create a graph..

survminer, a package for drawing easily beautiful and ‘ready-to-publish’ survival curves with the ‘number at risk’ table and ‘censoring count plot’, has became very popular at the end of a previous year. It happend due to 3 great methodology posts about Cox Proportional Hazards model and Survival Analysis basics by Alboukadel Kassambara (kassambara) published on STDHA

• Survival Analysisc Basics
• Cox Proportional Hazards Model
• Cox Model Assumptions

If you are working in the survival analysis field, you might be interested in reading the post covering the newest features in survminer: survminer 0.2.4

Right now, after a year of the first CRAN release, survminer has been downloaded 11k times and has been used and cited in several research articles:

• Primary resistance to PD-1 blockade mediated by JAK1/2 mutations
• Methods for Evaluating Respondent Attrition in Web-Based Surveys
• Antiretroviral therapy improves survival among TB-HIV co-infected patients who have CD4+ T-cell count above 350cells/mm3
• The serological response against foot and mouth disease virus elicited by repeated vaccination of dairy cattl
• The serological response against foot and mouth disease virus elicited by repeated vaccination of dairy cattle
• ActiviTeen: A Protocol for Deployment of a Consumer Wearable Device in an Academic Setting

Due to the celebration of it’s 1st anniversary on CRAN, I have prepared a long survival graph of Kaplan-Meier estimates of survival curves, where each option is described below. I think I went too far to present the possible customization of the graph because this example takes over 30 parameters!

       
   ggsurvplot(
   fit,                     # survfit object with calculated statistics.
   risk.table = TRUE,       # show risk table.
   pval = TRUE,             # show p-value of log-rank test.
   conf.int = TRUE,         # show confidence intervals for 
                            # point estimaes 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.
  ncensor.plot = TRUE,      # plot the number of censored subjects at time t
  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.
  palette = 
    c("#E7B800", "#2E9FDF"),# custom color palettes.
  main = "Survival curves",                       # specify the title of the plot
  submain = "Based on Kaplan-Meier estimates",    # the subtitle of the plot 
  caption = "created with survminer",             # the caption of the plot
  .main = c(16, "bold", "darkblue"),          #  for titles of the plot, the table and censor part
  .submain = c(15, "bold.italic", "purple"),  #  for subtitles in the plot, the table and censor part
  .caption = c(14, "plain", "orange"),        #  for captions in the plot, the table and censor part
  .x = c(14, "bold.italic", "red"),           #  for x axises in the plot, the table and censor part
  .y = c(14, "bold.italic", "darkred"),       #  for y axises in the plot, the table and censor part
  .tickslab = c(12, "plain", "darkgreen"),    #  for ticklabs in the plot, the table and censor part
  ########## risk table #########,
  risk.table.title = "Note the risk set sizes",          # the title of the risk table
  risk.table.subtitle = "and remember about censoring.", # the subtitle of the risk table
  risk.table.caption = "source code: website.com",       # the caption of the risk table
  risk.table.height = 0.35,                              # the height of the risk table
  ########## ncensor plot ######
  ncensor.plot.title = "Number of censorings",           # as above but for the censoring plot
  ncensor.plot.subtitle = "over the time.",
  ncensor.plot.caption = "data available at data.com",
  ncensor.plot.height = 0.35)