Survminer Cheatsheet to Create Easily Survival Plots

March 23, 2017
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(This article was first published on Easy Guides, and kindly contributed to R-bloggers)

We recently released the survminer verion 0.3, which includes many new features to help in visualizing and sumarizing survival analysis results.

In this article, we present a cheatsheet for survminer, created by Przemysław Biecek, and provide an overview of main functions.

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 cheatsheet

survminer overview

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

Survival Curves


  • 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.

Diagnostics of Cox Model


  • 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.

Summary of Cox Model


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

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

Competing Risks


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

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.

Infos

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


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