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 , #393
anova()
as requested (@pbiecek, #391
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, #264
New 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).
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.
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).
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)
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.
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).
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 \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)
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 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’.
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).