survminer 0.3.1

Minor changes

  • The example section of the ggcoxdiagnostics() function and the vignette file Informative_Survival_Plots.Rmd have been updated so that survminer can pass CRAN check under R-oldrelease.
  • New example dataset BMT added for competing risk analysis.
  • New data set BRCAOV.survInfo added, used in vignette files

Bug fixes

  • Now, palette argument works in `ggcoxadjustedcurves() (#174)
  • Now ggsurvplot() works when the fun argument is an arbitrary function (#176).

survminer 0.3.0

New features

New options in ggsurvplot()

  • Additional data argument added to the ggsurvplot() function (\@kassambara, #142). Now, it’s recommended to pass to the function, the data used to fit survival curves. This will avoid the error generated when trying to use the ggsurvplot() function inside another functions (\@zzawadz, #125).

  • New argument risk.table.pos, for placing risk table inside survival curves (#69). Allowed options are one of c(“out”, “in”) indicating ‘outside’ or ‘inside’ the main plot, respectively. Default value is “out”.

  • New arguments tables.height, tables.y.text, tables.theme, tables.col: for customizing tables under the main survival plot: (#156).

  • New arguments cumevents and cumcensor: logical value for displaying the cumulative number of events table (#117) and the cumulative number of censored subject (#155), respectively.

  • Now, ggsurvplot() can display both the number at risk and the cumulative number of censored in the same table using the option risk.table = 'nrisk_cumcenor' (#96). It’s also possible to display the number at risk and the cumulative number of events using the option risk.table = 'nrisk_cumevents'.

  • New arguments pval.method and log.rank.weights: New possibilities to compare survival curves. Functionality based on survMisc::comp.

  • New arguments break.x.by and break.y.by, numeric value controlling x and y axis breaks, respectively.

  • Now, ggsurvplot() returns an object of class ggsurvplot which is list containing the following components (#158):
    • plot: the survival plot (ggplot object)
    • table: the number of subjects at risk table per time (ggplot object). Returned only when risk.table = TRUE.
    • cumevents: the cumulative number of events table (ggplot object). Returned only when cumevents = TRUE.
    • ncensor.plot: the number of censoring (ggplot object). Returned only when ncensor.plot = TRUE or cumcensor = TRUE.
    • data.survplot: the data used to plot the survival curves (data.frame).
    • data.survtable: the data used to plot the tables under the main survival curves (data.frame).

Themes

  • New function theme_survminer() to change easily the graphical parameters of plots generated with survminer (#151). A theme similar to theme_classic() with large font size. Used as default theme in survminer functions.

  • New function theme_cleantable() to draw a clean risk table and cumulative number of events table. Remove axis lines, x axis ticks and title (#117 & #156).

# Fit survival curves
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)

# Survival curves
require("survminer")
ggsurvplot(fit, data = lung, risk.table = TRUE,
    tables.theme = theme_cleantable()
    )

New functions

  • New function +.ggsurv() to add ggplot components - theme(), labs() - to an object of class ggsurv, which is a list of ggplots. (#151). For example:
# Fit survival curves
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)

# Basic survival curves
require("survminer")
p <- ggsurvplot(fit, data = lung, risk.table = TRUE)
p

# Customizing the plots
p %+% theme_survminer(
     font.main = c(16, "bold", "darkblue"),
     font.submain = 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.tickslab = c(12, "plain", "darkgreen")
)

Helper functions

New heper functions ggrisktable(), ggcumevents(), ggcumcensor(). Normally, users don’t need to use these function directly. Internally used by the function ggsurvplot().

Major changes

  • New argument sline in the ggcoxdiagnostics() function for adding loess smoothed trend on the residual plots. This will make it easier to spot some problems with residuals (like quadratic relation). (\@pbiecek, #119).

  • The design of ggcoxfunctional() has been changed to be consistent with the other functions in the survminer package. Now, ggcoxfunctional() works with coxph objects not formulas. The arguments formula is now deprecated (\@pbiecek, #115).

  • In the ggcoxdiagnostics() function, it’s now possible to plot Time in the OX axis (\@pbiecek, #124). This is convenient for some residuals like Schoenfeld. The linear.predictions parameter has been replaced with ox.scale = c("linear.predictions", "time", "observation.id").

Minor changes

  • New argument tables.height in ggsurvplot() to apply the same height to all the tables under the main survival plots (#157).

  • It is possible to specify title and caption for ggcoxfunctional (\@MarcinKosinski, #138) (font.main was removed as it was unused.)

  • It is possible to specify title, subtitle and caption for ggcoxdiagnostics (\@MarcinKosinski, #139) and fonts for them.

  • It is possible to specify global caption for ggcoxzph (\@MarcinKosinski, #140).

  • In ggsurvplot(), more information, about color palettes, have been added in the details section of the documentation (#100).

  • The R package maxstat doesn’t support very well an object of class tbl_df. To fix this issue, now, in the surv_cutpoint() function, the input data is systematically transformed into a standard data.frame format (\@MarcinKosinski, #104).

  • It’s now possible to print the output of the survminer packages in a powerpoint created with the ReporteRs package. You should use the argument newpage = FALSE in the print() function when printing the output in the powerpoint. Thanks to (\@abossenbroek, #110) and (\@zzawadz, #111). For instance:

require(survival)
require(ReporteRs)
require(survminer)

fit <- survfit(Surv(time, status) ~ rx + adhere, data =colon)
survplot <- ggsurvplot(fit, pval = TRUE,
                       break.time.by = 400,
                       risk.table = TRUE,
                       risk.table.col = "strata",
                       risk.table.height = 0.5, # Useful when you have multiple groups
                       palette = "Dark2")


require(ReporteRs)
doc = pptx(title = "Survival plots")
doc = addSlide(doc, slide.layout = "Title and Content")
doc = addTitle(doc, "First try")
doc = addPlot(doc, function() print(survplot, newpage = FALSE), vector.graphic = TRUE)
writeDoc(doc, "test.pptx")
  • Now, in ggcoxdiagnostics(), the option ncol = 1 is removed from the function facet_wrap(). By default, ncol = NULL. In this case, the number of columns and rows in the plot panels is defined automatically based on the number of covariates included in the cox model.

Bug fixes

Vignettes and examples

  • A new vignette and a ggsurvplot example was added to present new functionalities of possible texts and fonts customizations.

  • A new vignette and a ggsurvplot example was added to present new functionalities of possible weights specification in a Log-rank test.

survminer 0.2.4

Bug fixes

  • surv_summary() (v0.2.3) generated an error when the name of the variable used in survfit() can be found multiple times in the levels of the same variable. For example, variable = therapy; levels(therapy) –> “therapy” and “hormone therapy” (#86). This has been now fixed.

  • To extract variable names used in survival::survfit(), the R code strsplit(strata, "=|,\\s+", perl=TRUE) was used in the surv_summary() function [survminer v0.2.3]. The splitting was done at any “=” symbol in the string, causing an error when special characters (=, <=, >=) are used for the levels of a categorical variable (#91). This has been now fixed.

  • Now, ggsurvplot() draws correctly the risk.table (#93).

survminer 0.2.3

New features

  • New function surv_summary() for creating data frame containing a nice summary of a survival curve (#64).
  • It’s possible now to facet the output of ggsurvplot() by one or more factors (#64):
# Fit complexe survival curves
require("survival")
fit3 <- survfit( Surv(time, status) ~ sex + rx + adhere,
                data = colon )
                
# Visualize by faceting
# Plots are survival curves by sex faceted by rx and adhere factors.
require("survminer")  
ggsurv$plot +theme_bw() + facet_grid(rx ~ adhere)
  • Now, ggsurvplot() can be used to plot cox model (#67).
  • New ‘myeloma’ data sets added.
  • New functions added for determining and visualizing the optimal cutpoint of continuous variables for survival analyses:
  • surv_cutpoint(): Determine the optimal cutpoint for each variable using ‘maxstat’. Methods defined for surv_cutpoint object are summary(), print() and plot().
  • surv_categorize(): Divide each variable values based on the cutpoint returned by surv_cutpoint() (#41).
  • New argument ‘ncensor.plot’ added to ggsurvplot(). A logical value. If TRUE, the number of censored subjects at time t is plotted. Default is FALSE (#18).

Minor changes

  • New argument ‘conf.int.style’ added in ggsurvplot() for changing the style of confidence interval bands.
  • Now, ggsurvplot() plots a stepped confidence interval when conf.int = TRUE (#65).
  • ggsurvplot() updated for compatibility with the future version of ggplot2 (v2.2.0) (#68)
  • ylab is now automatically adapted according to the value of the argument fun. For example, if fun = “event”, then ylab will be “Cumulative event”.
  • In ggsurvplot(), linetypes can now be adjusted by variables used to fit survival curves (#46)
  • In ggsurvplot(), the argument risk.table can be either a logical value (TRUE|FALSE) or a string (“absolute”, “percentage”). If risk.table = “absolute”, ggsurvplot() displays the absolute number of subjects at risk. If risk.table = “percentage”, the percentage at risk is displayed. Use “abs_pct” to show both the absolute number and the percentage of subjects at risk. (#70).
  • New argument surv.median.line in ggsurvplot(): character vector for drawing a horizontal/vertical line at median (50%) survival. Allowed values include one of c(“none”, “hv”, “h”, “v”). v: vertical, h:horizontal (#61).
  • Now, default theme of ggcoxdiagnostics() is ggplot2::theme_bw().

Bug fixes

  • ggcoxdiagnostics() can now handle a multivariate Cox model (#62)
  • ggcoxfunctional() now displays graphs of continuous variable against martingale residuals of null cox proportional hazards model (#63).
  • When subset is specified in the survfit() model, it’s now considered in ggsurvplot() to report the right p-value on the subset of the data and not on the whole data sets (@jseoane, #71).
  • ggcoxzph() can now produce plots only for specified subset of varibles (@MarcinKosinski, #75)

survminer 0.2.2

New features

  • New ggcoxdiagnostics function that plots diagnostic graphs for Cox Proportional Hazards model (@MarcinKosinski, #16).
  • Vignette added: Survival plots have never been so informative (@MarcinKosinski, #39)
  • New argument linetype in ‘ggsurvplot’ (@MarcinKosinski, #45). Allowed values includes i) “strata” for changing linetypes by strata (i.e. groups); ii) a numeric vector (e.g., c(1, 2)) or a character vector c(“solid”, “dashed”).

Bug fixes

survminer 0.2.1

New features

  • New ggcoxzph function that displays a graph of the scaled Schoenfeld residuals, along with a smooth curve using ‘ggplot2’. Wrapper around \link{plot.cox.zph}. (@MarcinKosinski, #13)

  • New ggcoxfunctional function that displays graphs of continuous explanatory variable against martingale residuals of null cox proportional hazards model, for each term in of the right side of input formula. This might help to properly choose the functional form of continuous variable in cox model, since fitted lines with lowess function should be linear to satisfy cox proportional hazards model assumptions. (@MarcinKosinski, #14)

  • New function theme_classic2: ggplot2 classic theme with axis line. This function replaces ggplot2::theme_classic, which does no longer display axis lines (since ggplot2 v2.1.0)

Minor changes

  • post-customization of color and fill no longer shows warnings like “Scale for ‘fill’ is already present. Adding another scale for ‘fill’, which will replace the existing scale” (@MarcinKosinski, #11).
  • now, post-customization of survival curve colors will automatically affect the risk table y axis text colors (@MarcinKosinski, #11).
  • Default value for the argument risk.table.y.text.col is now TRUE.
  • New argument risk.table.y.text for the function ggsurvplot. logical argument. Default is TRUE. If FALSE, risk table y axis tick labels will be hidden (@MarcinKosinski, #28).

Bug fixes

survminer 0.2.0

New features

  • New arguments in ggsurvplot for changing font style, size and color of main title, axis labels, axis tick labels and legend labels: font.main, font.x, font.y, font.tickslab, font.legend.
  • New arguments risk.table.title, risk.table.fontsize in ggsurvplot
  • New argument risk.table.y.text.col: logical value. Default value is FALSE. If TRUE, risk table tick labels will be colored by strata (@MarcinKosinski, #8).

  • print.ggsurvplot() function added: S3 method for class ‘ggsurvplot’.

  • ggsurvplot returns an object of class ggsurvplot which is list containing two ggplot objects:
    • plot: the survival plot
    • table: the number at risk table per time
  • It’s now possible to customize the output survival plot and the risk table returned by ggsurvplot, and to print again the final plot. (@MarcinKosinski, #2):

# Fit survival curves
require("survival")
fit<- survfit(Surv(time, status) ~ sex, data = lung)

# visualize
require(survminer)
ggsurvplot(fit, pval = TRUE, conf.int = TRUE,
          risk.table = TRUE)

# Customize the output and then print
res <- ggsurvplot(fit, pval = TRUE, conf.int = TRUE,
           risk.table = TRUE)
res$table <- res$table + theme(axis.line = element_blank())
res$plot <- res$plot + labs(title = "Survival Curves")
print(res)

Minor changes

  • p < 0.0001 is used (when pvalue < 0.0001).

Bug fixes

  • ggtheme now affects risk.table (@MarcinKosinski, #1)

  • xlim changed to cartesian coordinates mode (@MarcinKosinski, #4). The Cartesian coordinate system is the most common type of coordinate system. It will zoom the plot (like you’re looking at it with a magnifying glass), without clipping the data.

  • Risk table and survival curves have now the same color and the same order

  • Plot width is no longer too small when legend position = “left” (@MarcinKosinski, #7).

survminer 0.1.1

New features

  • ggsurvplot(): Drawing survival curves using ggplot2