test.for.trend added in
ggsurvplot() to perform a log-rank test for trend. logical value. Default is FALSE. If TRUE, returns the test for trend p-values. Tests for trend are designed to detect ordered differences in survival curves. That is, for at least one group. The test for trend can be only performed when the number of groups is > 2 (#188).
add.all added now in
ggsurvplot() to add he survival curves of (all) pooled patients onto the main survival plot stratified by grouping variables. Alias of the
ggsurvplot_add_all() function (#194).
combine = TRUE is now available in the
ggsurvplot() function to combine a list of survfit objects on the same plot. Alias of the ggsurvplot_combine() function (#195).
The standard convention of ggplot2 is to have the axes offset from the origin. This can be annoying with Kaplan-Meier plots. New argument
axes.offset added non in
ggsurvplot(). logical value. Default is TRUE. If FALSE, set the plot axes to start at the origin (c(0,0)) (#196).
ggsurvplot() can take a list of survfit objects and produces a list of ggsurvplots (#204).
facet.by added now in
ggsurvplot() to draw multi-panel survival curves of a data set grouped by one or two variables. Alias of the
ggsurvplot_facet() function (#205).
group.by added now in
ggsurvplot() to create survival curves of grouped data sets. Alias of the
ggsurvplot(), one can specify pval = TRUE/FALSE as a logical value. Now, it’s also possible to specify the argument
pval as a numeric value (e.g.: pval = 0.002), that will be passed to the plot, so that user can pass any custom p-value to the final plot ([@MarcinKosinski, #189](https://github.com/kassambara/survminer/issues/189)) or one can specify it as a character string (e.g.: pval = “p < 0001”) ([@MarcinKosinski, #193](https://github.com/kassambara/survminer/issues/193)).
ggsurvplot(): numeric or character value specifying x-axis scale.
conf.int.alpha added in
ggsurvplot(). Numeric value specifying fill color transparency. Value should be in [0, 1], where 0 is full transparency and 1 is no transparency.
surv_group_by() added to create a grouped data set for survival analysis.
ggsurvplot_df() added. An extension to ggsurvplot() to plot survival curves from any data frame containing the summary of survival curves as returned the surv_summary() function. Might be useful for a user who wants to use ggsurvplot for visualizing survival curves computed by another method than the standard survfit.formula function. In this case, the user has just to provide the data frame containing the summary of the survival analysis.
surv_median() added to easily extract median survivals from one or a list of survfit objects (#207).
surv_pvalue() added to compute p-value from survfit objects or parse it when provided by the user. Survival curves are compared using the log-rank test (default). Other methods can be specified using the argument method.
surv_fit() added to handle complex situation when computing survival curves (Read more in the doc: ?surv_fit). Wrapper arround the standard
survfit() [survival] function to create survival curves. Compared to the standard survfit() function, it supports also:
ggforest()function has changed a lot. Now presents much more statistics for each level of each variable (extracted with
broom::tidy) and also some statistics for the
coxphmodel, like AIC, p.value, concordance (extracted with
ggcompetingrisks() supports the
conf.int argument. If
fit is an object of class
cuminc then confidence intervals are plotted with
ggsurvplot() supports the
survfit() outputs when used with the argument
Now, the default behaviour of
ggsurvplot() is to round the number at risk using the option
digits = 0 (214).
pairwise_survdiff() has been improved to handle a formula with multiple variables (213).
colorare updated allowing to assign the same color for same groups accross facets (#99 & #185).
For example, in the following script, survival curves are colored by the grouping variable
sex in all facets:
library(survminer) library(survival) fit <- survfit( Surv(time, status) ~ sex + rx + adhere, data = colon ) ggsurv <- ggsurvplot(fit, data = colon, color = "sex", legend.title = "Sex", palette = "jco") ggsurv$plot + facet_grid(rx ~ adhere)
pairwise_survdiff()checks whether the grouping variable is a factor. If this is not the case, the grouping variable is automatically converted into a factor.
ggsurvplot(): Now, log scale is used for x-axis when plotting the complementary log−log function (argument `fun = “cloglog”) (#171).
Now, the argument
ggsurvplot() ccan be also a numeric vector of length(strata); in this case a basic color palette is created using the function
fun added in
ggcoxadjustedcurves() ([@meganli, #202](https://github.com/kassambara/survminer/issues/202)).
Columns/Rows are now correctly labeled in
pairwise_survdiff() display ([@mriffle, #212](https://github.com/kassambara/survminer/issues/212)).
pairwise_survdiff() function works when the data contain NAs ([@emilelatour , #184](https://github.com/kassambara/survminer/issues/184)).
ggsurvplot() fully supports different methods, in the survMisc package, for comparing survival curves (#191).
ggcoxdiagnostics()function and the vignette file
Informative_Survival_Plots.Rmdhave been updated so that
survminercan pass CRAN check under R-oldrelease.
BMTadded for competing risk analysis.
BRCAOV.survInfoadded, used in vignette files
paletteargument works in `ggcoxadjustedcurves() (#174)
ggsurvplot()works when the
funargument is an arbitrary function (#176).
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).
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”.
tables.height, tables.y.text, tables.theme, tables.col: for customizing tables under the main survival plot: (#156).
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'.
log.rank.weights: New possibilities to compare survival curves. Functionality based on
break.y.by, numeric value controlling x and y axis breaks, respectively.
ggsurvplot()returns an object of class ggsurvplot which is list containing the following components (#158):
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.
# 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() )
+.ggsurv()to add ggplot components -
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") )
arrange_ggsurvplots() to arrange multiple ggsurvplots on the same page (#66).
ggsurvevents() to calculate and plot the distribution for events (both status = 0 and status = 1); with
type parameter one can plot cumulative distribution of locally smooth density; with normalised, distributions are normalised. This function helps to notice when censorings are more common (@pbiecek, #116).
ggforest() for drawing forest plot for the Cox model.
pairwise_survdiff() for multiple comparisons of survival Curves (#97).
ggcompetingrisks() to plot the cumulative incidence curves for competing risks (@pbiecek, #168.
New heper functions
ggcumcensor(). Normally, users don’t need to use these function directly. Internally used by the function
ggrisktable()for plotting number of subjects at risk by time. (#154).
ggcumevents()for plotting the cumulative number of events table (#117).
ggcumcensor()for plotting the cumulative number of censored subjects table (#155).
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).
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").
ggsurvplot() to apply the same height to all the tables under the main survival plots (#157).
It is possible to specify
ggcoxfunctional (@MarcinKosinski, #138) (
font.main was removed as it was unused.)
It is possible to specify
ggcoxdiagnostics (@MarcinKosinski, #139) and
fonts for them.
It is possible to specify global
ggcoxzph (@MarcinKosinski, #140).
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")
ggcoxdiagnostics(), the option
ncol = 1is 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.
Now, risk table align with survival plots when legend = “right” (@jonlehrer, #102).
ggcoxzph() works for univariate Cox analysis (#103).
ggcoxdiagnostics() works properly for schoenfeld residuals (@pbiecek, #119).
ggsurvplot() works properly in the situation where
strata() is included in the cox formula (#109).
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.
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.
ggsurvplot() draws correctly the risk.table (#93).
surv_summary()for creating data frame containing a nice summary of a survival curve (#64).
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)
ggsurvplot()can be used to plot cox model (#67).
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
ggsurvplot(). A logical value. If TRUE, the number of censored subjects at time t is plotted. Default is FALSE (#18).
ggsurvplot()for changing the style of confidence interval bands.
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)
fun. For example, if fun = “event”, then ylab will be “Cumulative event”.
ggsurvplot(), linetypes can now be adjusted by variables used to fit survival curves (#46)
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).
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).
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).
ggsurvplot()to report the right p-value on the subset of the data and not on the whole data sets ([@jseoane, #71](https://github.com/kassambara/survminer/issues/71)).
ggcoxzph()can now produce plots only for specified subset of varibles ([@MarcinKosinski, #75](https://github.com/kassambara/survminer/issues/75))
ggcoxdiagnosticsfunction that plots diagnostic graphs for Cox Proportional Hazards model ([@MarcinKosinski, #16](https://github.com/kassambara/survminer/issues/16)).
Survival plots have never been so informative([@MarcinKosinski, #39](https://github.com/kassambara/survminer/issues/39))
ggsurvplot()documentation. ([@ViniciusBRodrigues, #43](https://github.com/kassambara/survminer/issues/43))
ggcoxzph function that displays a graph of the scaled Schoenfeld residuals, along with a smooth curve using ‘ggplot2’. Wrapper around . ([@MarcinKosinski, #13](https://github.com/kassambara/survminer/issues/13))
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](https://github.com/kassambara/survminer/issues/14))
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)
risk.table.y.text.colis now TRUE.
ggsurvplot. logical argument. Default is TRUE. If FALSE, risk table y axis tick labels will be hidden ([@MarcinKosinski, #28](https://github.com/kassambara/survminer/issues/28)).
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](https://github.com/kassambara/survminer/issues/8)).
print.ggsurvplot() function added: S3 method for class ‘ggsurvplot’.
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](https://github.com/kassambara/survminer/issues/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)
ggtheme now affects risk.table ([@MarcinKosinski, #1](https://github.com/kassambara/survminer/issues/1))
xlim changed to cartesian coordinates mode ([@MarcinKosinski, #4](https://github.com/kassambara/survminer/issues/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](https://github.com/kassambara/survminer/issues/7)).