In this article, we present a cheatsheet for survminer, created by Przemysław Biecek, and provide an overview of main functions.
- 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.
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.
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.
- 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.
This analysis has been performed using R software (ver. 3.3.2).