\link{} targets missing package added in the
following Rd files:
ggcoxzph()
(#534 and #535)element_text()
issues a warning when vectorized arguments are provided, as in colour =
c(“red”, “green”, “blue”). This is a breaking change affecting the
function ggsurvtable(). To fix this, the function
ggtext::element_markdown() is now used in place of
element_text() to handle vectorized colors (issue #455
fixed by pull #503).log.rank.weights = "n" is specified in the function
ggsurvplot() (#453)ggsurvplot() examples, the function
gridExtra::rbind.gtable() is now replaced by
gridExtra::gtable_rbind() (@jan-imbi, pull #493).ggforest() (pull 485).survfit(res.cox) returned an object of class survfit.cox.
The class has been changed to survfitcox in the current
survival package version. The survminer package has been now updated to
take this change into account (@edvbb, #441).Fixes to adapt to dplyr 1.0.0 (@romainfrancois, #460):
conf.int in the
ggsurvplot() function. To fix this issue, Now, NAs are
removed by default when drawing the confidence interval (#443 and
#315).surv_adjustedcurves is extracted from
ggadjustedcurves. This function calculates adjusted
survival curves but do not plot them. Its results may be useful for
calculation of median survival or some other statistics. (@pbiecek, #423).cmprsk is no longer needed for survminer installation.
The package has been moved from Imports to Suggests. It’s only used in
documentations (@massimofagg,
#394.ggflexsurvplot(), the grouping variable can be
factor or character vector (@andersbergren ,
#393anova() as requested (@pbiecek, #391When a factor variable name is the same as one of its level,
ggsurvplot() failed (@KohSoonho, #387).
Fixed now.
ggsurvplot() can now create correctly faceted
survival curves (@uraniborg, #254, @BingxinS,
#363)
A typo fixed in the formula for weightened log-rank test (@MarcinKosinski, #336.
surv_summary() can now handle the output of
survfit(cox.model, newdata) when the option
conf.type = "none" is specified by users (@HeidiSeibold,
#335.
ggadjustedcurves() has now flipped labels for
conditional/marginal to mach names from
’Adjusted Survival Curves’ by Terry Therneau, Cynthia Crowson, Elizabeth
Atkinson (2015) (@pbiecek,
#335.
ggsurvplot() can be used to plot survreg model (@HeidiSeibold,
#276, #325 ).ggforest() simply returns a ggplot instead of
drawing automatically the plot (@grvsinghal,
#267).axes.offset argument is also applied to risk table
(@dmartinffm,
#243).ggsurvplot to powerpoint
document using ReporteRs even if there is no risk table (@DrRZ, #314).size added in
ggadjustedcurves() to change the curve size (@MaximilianTscharre,
#267).ggtheme is supported when combining a
list of survfit objects in ggsurvplot() (@PhonePong,
#278).New function ggflexsurvplot() to create
ggplot2-based graphs for flexible survival models.
The function ggadjustedcurves() handles now argument
method that defines how adjusted curves shall be
calculated. With method='conditional'|'marginal'
subpopulations are balanced with respect to variables present in the
model formula. With method='single'|'average' the curve
represents just the expected survival curves.
ggcoxadjustedcurves() is replaced by
ggadjustedcurves() (#229).The grouping variable to the ggadjustedcurves()
function is now passed as a name (character) of grouping variable not as
a vector with values of grouping variable.
New argument font.family in
ggsurvtable() to change the font family in the survival
tables - such as risk, cummulative events and censoring tables. For
example font.family = “Courier New” (@Swechhya,
#245).
Now, in ggsurvplot() the data argument should be
strictly provided (@dnzmarcio,
#235)
ggforest() no longer tries to bolt a table full of text
to the coefficient plot (@mmoisse, #241),
instead the annotations are done via ggplot2::annotate, see example at:
@fabian-s, #264New argument 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).
New argument 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).
New argument 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).
The function ggsurvplot() can take a list of survfit
objects and produces a list of ggsurvplots (#204).
New argument 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).
New argument group.by added now in
ggsurvplot() to create survival curves of grouped data
sets. Alias of the ggsurvplot_group_by() function.
In 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) or one can specify it as a character string (e.g.: pval = “p
< 0001”) (@MarcinKosinski,
#193).
New argument xscale in ggsurvplot():
numeric or character value specifying x-axis scale.
New arguments censor.shape and
censor.size to change the shape and the shape of censors
(#186
& #187).
New argument 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.
New function surv_group_by() added to create a
grouped data set for survival analysis.
New function 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.
New function surv_median() added to easily extract
median survivals from one or a list of survfit objects (#207).
New function 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.
New function 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
coxph model, like AIC, p.value, concordance (extracted with
broom::glance) (#178)Now, ggcompetingrisks() supports the
conf.int argument. If conf.int=TRUE and
fit is an object of class cuminc then
confidence intervals are plotted with geom_ribbon.
Now, ggsurvplot() supports the
survfit() outputs when used with the argument
start.time.
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).
The argument color are 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)Now, the function 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 palette in
ggsurvplot() ccan be also a numeric vector of
length(strata); in this case a basic color palette is created using the
function grDevices::palette().
The %+% function in survminer has been
replaced by %++% to avoid breaking the
ggplot2::%+% function behavior when using survminer (#199
and #200).
New argument fun added in
ggcoxadjustedcurves() (@meganli,
#202).
The function theme_classic2() removed.
Columns/Rows are now correctly labeled in
pairwise_survdiff() display (@mriffle,
#212).
Now, the pairwise_survdiff() function works when the
data contain NAs (@emilelatour ,
#184).
Now, ggsurvplot() fully supports different methods,
in the survMisc package, for comparing survival curves (#191).
ggcoxdiagnostics() function
and the vignette file Informative_Survival_Plots.Rmd have
been updated so that survminer can pass CRAN check under
R-oldrelease.BMT added for competing risk
analysis.BRCAOV.survInfo added, used in vignette
filespalette argument works in `ggcoxadjustedcurves()
(#174)ggsurvplot() works when the fun
argument is an arbitrary function (#176).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):
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()
)+.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")
)New function arrange_ggsurvplots() to arrange
multiple ggsurvplots on the same page (#66).
New function 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).
New function ggcoxadjustedcurves() to plot adjusted
survival curves for Cox proportional hazards model (@pbiecek,
#133 & @markdanese, #67).
New function ggforest() for drawing forest plot for
the Cox model.
New function pairwise_survdiff() for multiple
comparisons of survival Curves (#97).
New function ggcompetingrisks() to plot the
cumulative incidence curves for competing risks (@pbiecek,
#168.
New heper functions ggrisktable(),
ggcumevents(), ggcumcensor(). Normally, users
don’t need to use these function directly. Internally used by the
function ggsurvplot().
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).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").
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")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.Now, risk table align with survival plots when legend = “right” (@jonlehrer, #102).
Now, ggcoxzph() works for univariate Cox analysis
(#103).
Now, ggcoxdiagnostics() works properly for
schoenfeld residuals (@pbiecek,
#119).
Now, 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.
Now, 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 surv_cutpoint() (#41).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).ggcoxzph() can now produce plots only for specified
subset of varibles (@MarcinKosinski,
#75)ggcoxdiagnostics function that plots diagnostic
graphs for Cox Proportional Hazards model (@MarcinKosinski,
#16).Survival plots have never been so informative (@MarcinKosinski,
#39)ggsurvplot()
documentation. (@ViniciusBRodrigues,
#43)New ggcoxzph function that displays a graph of the
scaled Schoenfeld residuals, along with a smooth curve using ‘ggplot2’.
Wrapper around . (@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)
risk.table.y.text.col is
now TRUE.ggsurvplot. logical argument. Default is TRUE. If FALSE,
risk table y axis tick labels will be hidden (@MarcinKosinski, #28).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:
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)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).