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event.history(data, survtime.col, surv.col, surv.ind = c(1, 0), subset.rows = NULL, covtime.cols = NULL, cov.cols = NULL, num.colors = 1, cut.cov = NULL, colors = 1, cens.density = 10, mult.end.cens = 1.05, cens.mark.right =FALSE, cens.mark = "-", cens.mark.ahead = 0.5, cens.mark.cutoff = -1e-08, cens.mark.cex = 1, x.lab = "time under observation", y.lab = "estimated survival probability", title = "event history graph", ...)surv.col,
followed by the number for a censored
observation. Default is traditional survival data
represention, i.e., c(1,0).
treatment.arm == "a", if
the data frame, data, has been attached to the search directory;
NA entry in the column for each such change
(along with corresponding cov.cols column entry representing
the value of the covariate or function at that change time).
Default is NULL, meaning no time-dependent covariate information
will be presented in the graph.
NA column entry representing each change in the level
(along with a corresponding covtime.cols column entry representing
the time of the change). Default is NULL, meaning
no time-dependent covariate information will be presented in
the graph.
num.colors) to the number of desired covariate levels.
This will divide the covariate span into roughly equally-sized
intervals, via the S-Plus cut function.
Default is one color, meaning no time-dependent information
will be presented in the graph. Note that this argument will
be ignored/superceded if a non-NULL argument is provided for the
cut.cov parameter.
NULL, meaning that the num.colors argument value
will dictate the number of breakpoints, with the covariate span
defined into roughly equally-sized intervals via the S-Plus cut
function. However, if is.null(cut.cov) == FALSE,
then this argument supercedes any entry for the num.colors argument.
num.colors argument or the number
of ordered points - 1 as defined in the cut.cov argument
(with cut.cov superceding num.colors if
is.null(cut.cov) == FALSE).
The function, as currently written, allows for as much as
twenty distinct colors. This argument effectively feeds
into the col argument for the S-Plus polygon function.
Default is colors = 1. See the col argument for the both the
S-Plus par function and polygon function for more information.
cens.density=10.
cens.mark.right = TRUE. Default is "-".
cens.mark.cutoff = -1e-8) to ensure that
the censoring marks get plotted correctly. See event.history
code in order to see its usage. This argument typically will not
need adjustment.
cens.mark argument above. See more information
by viewing the cex argument for the S-Plus par function.
Default is cens.mark.cex = 1.0.
"time under observation".
"estimated survival probability".
"event history graph".
event.history function.
So, for example, the axes representations can be manipulated
with appropriate arguments, or particular areas of the event.history
graph can be “zoomed”. See the details section for more
comments about zooming.
xlim and ylim
arguments at the end of the event.history function call,
taking advantage of the ... argument link to the plot function.
An example of zooming can be seen
in Plate 4 of the paper referenced below.
Please read the reference below to understand how the
individual covariate and survival information is provided in the plot,
how ties are handled, how right-censoring is handled, etc.
plot,polygon,
event.chart, par
# Code to produce event history graphs for SIM paper
#
# before generating plots, some pre-processing needs to be performed,
# in order to get dataset in proper form for event.history function;
# need to create one line per subject and sort by time under observation,
# with those experiencing event coming before those tied with censoring time;
require('survival')
data(heart)
# creation of event.history version of heart dataset (call heart.one):
heart.one <- matrix(nrow=length(unique(heart$id)), ncol=8)
for(i in 1:length(unique(heart$id)))
{
if(length(heart$id[heart$id==i]) == 1)
heart.one[i,] <- as.numeric(unlist(heart[heart$id==i, ]))
else if(length(heart$id[heart$id==i]) == 2)
heart.one[i,] <- as.numeric(unlist(heart[heart$id==i,][2,]))
}
heart.one[,3][heart.one[,3] == 0] <- 2 ## converting censored events to 2, from 0
if(is.factor(heart$transplant))
heart.one[,7] <- heart.one[,7] - 1
## getting back to correct transplantation coding
heart.one <- as.data.frame(heart.one[order(unlist(heart.one[,2]), unlist(heart.one[,3])),])
names(heart.one) <- names(heart)
# back to usual censoring indicator:
heart.one[,3][heart.one[,3] == 2] <- 0
# note: transplant says 0 (for no transplants) or 1 (for one transplant)
# and event = 1 is death, while event = 0 is censored
# plot single Kaplan-Meier curve from heart data, first creating survival object
heart.surv <- survfit(Surv(stop, event) ~ 1, data=heart.one, conf.int = FALSE)
# figure 3: traditional Kaplan-Meier curve
# postscript('ehgfig3.ps', horiz=TRUE)
# omi <- par(omi=c(0,1.25,0.5,1.25))
plot(heart.surv, ylab='estimated survival probability',
xlab='observation time (in days)')
title('Figure 3: Kaplan-Meier curve for Stanford data', cex=0.8)
# dev.off()
## now, draw event history graph for Stanford heart data; use as Figure 4
# postscript('ehgfig4.ps', horiz=TRUE, colors = seq(0, 1, len=20))
# par(omi=c(0,1.25,0.5,1.25))
event.history(heart.one,
survtime.col=heart.one[,2], surv.col=heart.one[,3],
covtime.cols = cbind(rep(0, dim(heart.one)[1]), heart.one[,1]),
cov.cols = cbind(rep(0, dim(heart.one)[1]), heart.one[,7]),
num.colors=2, colors=c(6,10),
x.lab = 'time under observation (in days)',
title='Figure 4: Event history graph for\nStanford data',
cens.mark.right =TRUE, cens.mark = '-',
cens.mark.ahead = 30.0, cens.mark.cex = 0.85)
# dev.off()
# now, draw age-stratified event history graph for Stanford heart data;
# use as Figure 5
# two plots, stratified by age status
# postscript('c:\temp\ehgfig5.ps', horiz=TRUE, colors = seq(0, 1, len=20))
# par(omi=c(0,1.25,0.5,1.25))
par(mfrow=c(1,2))
event.history(data=heart.one, subset.rows = (heart.one[,4] < 0),
survtime.col=heart.one[,2], surv.col=heart.one[,3],
covtime.cols = cbind(rep(0, dim(heart.one)[1]), heart.one[,1]),
cov.cols = cbind(rep(0, dim(heart.one)[1]), heart.one[,7]),
num.colors=2, colors=c(6,10),
x.lab = 'time under observation\n(in days)',
title = 'Figure 5a:\nStanford data\n(age < 48)',
cens.mark.right =TRUE, cens.mark = '-',
cens.mark.ahead = 40.0, cens.mark.cex = 0.85,
xlim=c(0,1900))
event.history(data=heart.one, subset.rows = (heart.one[,4] >= 0),
survtime.col=heart.one[,2], surv.col=heart.one[,3],
covtime.cols = cbind(rep(0, dim(heart.one)[1]), heart.one[,1]),
cov.cols = cbind(rep(0, dim(heart.one)[1]), heart.one[,7]),
num.colors=2, colors=c(6,10),
x.lab = 'time under observation\n(in days)',
title = 'Figure 5b:\nStanford data\n(age >= 48)',
cens.mark.right =TRUE, cens.mark = '-',
cens.mark.ahead = 40.0, cens.mark.cex = 0.85,
xlim=c(0,1900))
# dev.off()
# par(omi=omi)
# we will not show liver cirrhosis data manipulation, as it was
# a bit detailed; however, here is the
# event.history code to produce Figure 7 / Plate 1
# Figure 7 / Plate 1 : prothrombin ehg with color
## Not run:
# second.arg <- 1 ### second.arg is for shading
# third.arg <- c(rep(1,18),0,1) ### third.arg is for intensity
#
# # postscript('c:\temp\ehgfig7.ps', horiz=TRUE,
# # colors = cbind(seq(0, 1, len = 20), second.arg, third.arg))
# # par(omi=c(0,1.25,0.5,1.25), col=19)
# event.history(cirrhos2.eh, subset.rows = NULL,
# survtime.col=cirrhos2.eh$time, surv.col=cirrhos2.eh$event,
# covtime.cols = as.matrix(cirrhos2.eh[, ((2:18)*2)]),
# cov.cols = as.matrix(cirrhos2.eh[, ((2:18)*2) + 1]),
# cut.cov = as.numeric(quantile(as.matrix(cirrhos2.eh[, ((2:18)*2) + 1]),
# c(0,.2,.4,.6,.8,1), na.rm=TRUE) + c(-1,0,0,0,0,1)),
# colors=c(20,4,8,11,14),
# x.lab = 'time under observation (in days)',
# title='Figure 7: Event history graph for liver cirrhosis data (color)',
# cens.mark.right =TRUE, cens.mark = '-',
# cens.mark.ahead = 100.0, cens.mark.cex = 0.85)
# # dev.off()
# ## End(Not run)
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