Function plotCIF
plots, for one or more groups, the
cumulative incidence curves for a selected event out of two or more
competing events. Function stackedCIF
plots, for one group or
population, the cumulative incidence curves for two or more competing
events such that the cumulative incidences are stacked upon each
other. The CIFs are are estimated by the Aalen-Johansen method.
## S3 method for class 'survfit'
plotCIF( x, event = 1,
xlab = "Time",
ylab = "Cumulative incidence",
ylim = c(0, 1),
lty = 1,
col = "black", ... )## S3 method for class 'survfit'
stackedCIF( x, group = 1,
col = "black",
fill = "white",
ylim = c(0,1),
xlab = "Time",
ylab = "Cumulative incidence", ... )
An object of class survfit
, the type
of
event
in Surv()
being "mstate
"; the first level
of the event factor represents censoring and the remaining ones the
alternative competing events.
Determines the event for which the cumulative incidence
curve is plotted by plotCIF
.
An integer showing the selected level of a possible
grouping factor appearing in the model formula in survfit
when
plotting by stackedCIF
A vector specifying the plotting color(s) of the curve(s) for
the different groups in plotCIF
-- default: all "black".
A vector indicating the colours to be used for shading the
areas pertinent to the separate outcomes in stackedCIF
- default: all "white"
.
Label for the $x$-axis.
Label for the $y$-axis.
Limits of the $y$-axis.
A vector specifying the line type(s) of the curve(s) for the different groups - default: all 1 (=solid).
Further graphical parameters to be passed.
No value is returned but a plot is produced as a side-effect.
The order in which the curves with stackedCIF
are piled
upon each other is the same as the ordering of the values or levels of
the competing events in the pertinent event variable. The ordering can
be changed by permuting the levels as desired using function
Relevel
, after which survfit
is called with the relevelled
event
variable in Surv()
Putter, H., Fiocco, M., Geskus, R.B. (2007). Tutorial in biostatistics: competing risks and multi-state models. Statistics in Medicine, 26: 2389--2430.
# NOT RUN {
library(survival) # requires version 2.39-4 or later
head(mgus1)
# Aalen-Johansen estimates of CIF are plotted by sex for two
# competing events: (1) progression (pcm), and (2) death, in
# a cohort of patients with monoclonal gammopathy.
# The data are actually covering transitions from pcm to death, too,
# for those entering the state of pcm. Such patients have two rows
# in the data frame, and in their 2nd row the 'start' time is
# the time to pcm (in days).
# In our analysis we shall only include those time intervals with value 0
# for variable 'start'. Thus, the relevant follow-up time is represented
# by variable 'stop' (days). For convenience, days are converted to years.
fitCI <- survfit(Surv(stop/365.25, event, type="mstate") ~ sex,
data= subset(mgus1, start==0) )
par(mfrow=c(1,2))
plotCIF(fitCI, event = 1, col = c("red", "blue"),
main = "Progression", xlab="Time (years)" )
text( 38, 0.15, "Men", pos = 2)
text( 38, 0.4, "Women", pos = 2)
plotCIF(fitCI, event = 2, col = c("red", "blue"),
main = "Death", xlab="Time (years)" )
text( 38, 0.8, "Men", pos = 2)
text( 38, 0.5, "Women", pos = 2)
par(mfrow=c(1,2))
stackedCIF(fitCI, group = 1, colour = c("gray80", "gray90"),
main = "Women", xlab="Time (years)" )
text( 36, 0.15, "PCM", pos = 2)
text( 36, 0.6, "Death", pos = 2)
stackedCIF(fitCI, group = 2, colour = c("gray80", "gray90"),
main = "Men", xlab="Time (years)" )
text( 39, 0.10, "PCM", pos = 2)
text( 39, 0.6, "Death", pos = 2)
# }
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