plot.prodlim: Plotting event probabilities over time
In prodlim: Product-Limit Estimation for Censored Event History Analysis

plot.prodlim

R Documentation

Plotting event probabilities over time

Description

Function to plot survival probabilities or absolute risks (cumulative incidence function) against time.

Usage

## S3 method for class 'prodlim'
plot(
  x,
  type,
  cause,
  select,
  newdata,
  add = FALSE,
  col,
  lty,
  lwd,
  ylim,
  xlim,
  ylab,
  xlab = "Time",
  num.digits = 2,
  timeconverter,
  legend = TRUE,
  short.labels = TRUE,
  logrank = FALSE,
  marktime = FALSE,
  confint = TRUE,
  automar,
  atrisk = ifelse(add, FALSE, TRUE),
  timeOrigin = 0,
  axes = TRUE,
  background = TRUE,
  percent = TRUE,
  minAtrisk = 0,
  limit = 10,
  ...
)

Arguments

`x`	an object of class ‘prodlim’ as returned by the `prodlim` function.
`type`	Either `"surv"` or `"risk"` AKA `"cuminc"`. Controls what part of the object is plotted. Defaults to `object$type`.
`cause`	For competing risk models. Character (other classes are converted with `as.character`). The argument `cause` determines the event of interest. Currently one cause is allowed at a time, but you can call the function again with `add=TRUE` to add the lines of the other causes. Also, if `cause="stacked"` is specified the absolute risks of all causes are stacked.
`select`	Select which lines to plot. This can be used when there are many strata or many competing risks to select a subset of the lines. However, a more clean way to select covariate strata is to use the argument `newdata`. Another application is when there are several competing risks and the stacked plot (`cause="stacked"`) should only show a selected subset of the available causes.
`newdata`	a data frame containing covariate strata for which to show curves. When omitted element `X` of object `x` is used.
`add`	if `TRUE` curves are added to an existing plot.
`col`	color for curves. Default is `1:number(curves)`
`lty`	line type for curves. Default is 1.
`lwd`	line width for all curves. Default is 3.
`ylim`	limits of the y-axis
`xlim`	limits of the x-axis
`ylab`	label for the y-axis
`xlab`	label for the x-axis
`num.digits`	Number of digits when rounding off numerical values for legend and at-risk tables.
`timeconverter`	The following options are supported: "days2years" (conversion factor: 1/365.25) "months2years" (conversion factor: 1/12) "days2months" (conversion factor 1/30.4368499) "years2days" (conversion factor 365.25) "years2months" (conversion factor 12) "months2days" (conversion factor 30.4368499)
`legend`	if TRUE a legend is plotted by calling the function legend. Optional arguments of the function `legend` can be given in the form `legend.x=val` where x is the name of the argument and val the desired value. See also Details.
`short.labels`	Logical. When `FALSE` construct labels as cause=1, var1=v1, var2=v2 else as 1, v1, v2.
`logrank`	If TRUE, the logrank p-value will be extracted from a call to `survdiff` and added to the legend. This works only for survival models, i.e. Kaplan-Meier with discrete predictors.
`marktime`	if TRUE the curves are tick-marked at right censoring times by invoking the function `markTime`. Optional arguments of the function `markTime` can be given in the form `confint.x=val` as with legend. See also Details.
`confint`	if TRUE pointwise confidence intervals are plotted by invoking the function `confInt`. Optional arguments of the function `confInt` can be given in the form `confint.x=val` as with legend. See also Details.
`automar`	If TRUE the function trys to find suitable values for the figure margins around the main plotting region.
`atrisk`	if TRUE display numbers of subjects at risk by invoking the function `atRisk`. Optional arguments of the function `atRisk` can be given in the form `atrisk.x=val` as with legend. See also Details.
`timeOrigin`	Start of the time axis
`axes`	If true axes are drawn. See details.
`background`	If `TRUE` the background color and grid color can be controlled using smart arguments SmartControl, such as background.bg="yellow" or background.bg=c("gray66","gray88"). The following defaults are passed to `background` by `plot.prodlim`: horizontal=seq(0,1,.25), vertical=NULL, bg="gray77", fg="white". See `background` for all arguments, and the examples below.
`percent`	If true the y-axis is labeled in percent.
`minAtrisk`	Integer. Show the curve only until the number at-risk is at least `minAtrisk`
`limit`	When newdata is not specified and the number of lines in element `X` of object `x` exceeds limits, only the results for covariate constellations of the first, the middle and the last row in `X` are shown. Otherwise all lines of `X` are shown.
`...`	Parameters that are filtered by `SmartControl` and then passed to the functions `plot`, `legend`, `axis`, `atRisk`, `confInt`, `markTime`, `backGround`

Details

From version 1.1.3 on the arguments legend.args, atrisk.args, confint.args are obsolete and only available for backward compatibility. Instead arguments for the invoked functions atRisk, legend, confInt, markTime, axis are simply specified as atrisk.cex=2. The specification is not case sensitive, thus atRisk.cex=2 or atRISK.cex=2 will have the same effect. The function axis is called twice, and arguments of the form axis1.labels, axis1.at are used for the time axis whereas axis2.pos, axis1.labels, etc. are used for the y-axis.

These arguments are processed via ...{} of plot.prodlim and inside by using the function SmartControl. Documentation of these arguments can be found in the help pages of the corresponding functions.

Value

The (invisible) object.

Author(s)

Thomas Alexander Gerds <tag@biostat.ku.dk>

Examples

## simulate right censored data from a two state model 
set.seed(100)
dat <- SimSurv(100)
# with(dat,plot(Hist(time,status)))

### marginal Kaplan-Meier estimator
kmfit <- prodlim(Hist(time, status) ~ 1, data = dat)
plot(kmfit)
plot(kmfit,atrisk.show.censored=1L,atrisk.at=seq(0,12,3))
plot(kmfit,timeconverter="years2months")

# change time range
plot(kmfit,xlim=c(0,4))

# change scale of y-axis
plot(kmfit,percent=FALSE)

# mortality instead of survival
plot(kmfit,type="risk")

# change axis label and position of ticks
plot(kmfit,
     xlim=c(0,10),
     axis1.at=seq(0,10,1),
     axis1.labels=0:10,
     xlab="Years",
     axis2.las=2,
     atrisk.at=seq(0,10,2.5),
     atrisk.title="")

# change background color
plot(kmfit,
     xlim=c(0,10),
     confint.citype="shadow",
     col=1,
     axis1.at=0:10,
     axis1.labels=0:10,
     xlab="Years",
     axis2.las=2,
     atrisk.at=seq(0,10,2.5),
     atrisk.title="",
     background=TRUE,
     background.fg="white",
     background.horizontal=seq(0,1,.25/2),
     background.vertical=seq(0,10,2.5),
     background.bg=c("gray88"))

# change type of confidence limits
plot(kmfit,
     xlim=c(0,10),
     confint.citype="dots",
     col=4,
     background=TRUE,
     background.bg=c("white","gray88"),
     background.fg="gray77",
     background.horizontal=seq(0,1,.25/2),
     background.vertical=seq(0,10,2))


### Kaplan-Meier in discrete strata
kmfitX <- prodlim(Hist(time, status) ~ X1, data = dat)
plot(kmfitX,atrisk.show.censored=1L)
# move legend
plot(kmfitX,legend.x="bottomleft",atRisk.cex=1.3,
     atrisk.title="No. subjects")

## Control the order of strata
## since version 1.5.1 prodlim does  obey the order of
## factor levels
dat$group <- factor(cut(dat$X2,c(-Inf,0,0.5,Inf)),
                    labels=c("High","Intermediate","Low"))
kmfitG <- prodlim(Hist(time, status) ~ group, data = dat)
plot(kmfitG)

## relevel 
dat$group2 <- factor(cut(dat$X2,c(-Inf,0,0.5,Inf)),
                     levels=c("(0.5, Inf]","(0,0.5]","(-Inf,0]"),
                     labels=c("Low","Intermediate","High"))
kmfitG2 <- prodlim(Hist(time, status) ~ group2, data = dat)
plot(kmfitG2)

# add log-rank test to legend
plot(kmfitX,
     atRisk.cex=1.3,
     logrank=TRUE,
     legend.x="topright",
     atrisk.title="at-risk")

# change atrisk labels
plot(kmfitX,
     legend.x="bottomleft",
     atrisk.title="Patients",
     atrisk.cex=0.9,
     atrisk.labels=c("X1=0","X1=1"))

# multiple categorical factors

kmfitXG <- prodlim(Hist(time,status)~X1+group2,data=dat)
plot(kmfitXG,select=1:2)

### Kaplan-Meier in continuous strata
kmfitX2 <- prodlim(Hist(time, status) ~ X2, data = dat)
plot(kmfitX2,xlim=c(0,10))

# specify values of X2 for which to show the curves 
plot(kmfitX2,xlim=c(0,10),newdata=data.frame(X2=c(-1.8,0,1.2)))

### Cluster-correlated data
library(survival)
cdat <- cbind(SimSurv(20),patnr=sample(1:5,size=20,replace=TRUE))
kmfitC <- prodlim(Hist(time, status) ~ cluster(patnr), data = cdat)
plot(kmfitC)
plot(kmfitC,atrisk.labels=c("Units","Patients"))

kmfitC2 <- prodlim(Hist(time, status) ~ X1+cluster(patnr), data = cdat)
plot(kmfitC2)
plot(kmfitC2,atrisk.labels=c("Teeth","Patients","Teeth","Patients"),
     atrisk.col=c(1,1,2,2))


### Cluster-correlated data with strata
n = 50
foo = runif(n)
bar = rexp(n)
baz = rexp(n,1/2)
d = stack(data.frame(foo,bar,baz))
d$cl = sample(10, 3*n, replace=TRUE)
fit = prodlim(Surv(values) ~ ind + cluster(cl), data=d)
plot(fit)


## simulate right censored data from a competing risk model 
datCR <- SimCompRisk(100)
with(datCR,plot(Hist(time,event)))

### marginal Aalen-Johansen estimator
ajfit <- prodlim(Hist(time, event) ~ 1, data = datCR)
plot(ajfit) # same as plot(ajfit,cause=1)
plot(ajfit,atrisk.show.censored=1L)

# cause 2
plot(ajfit,cause=2)

# both in one
plot(ajfit,cause=1)
plot(ajfit,cause=2,add=TRUE,col=2)

### stacked plot

plot(ajfit,cause="stacked",select=2)

### stratified Aalen-Johansen estimator
ajfitX1 <- prodlim(Hist(time, event) ~ X1, data = datCR)
plot(ajfitX1)

## add total number at-risk to a stratified curve
ttt = 1:10
plot(ajfitX1,atrisk.at=ttt,col=2:3)
plot(ajfit,add=TRUE,col=1)
atRisk(ajfit,newdata=datCR,col=1,times=ttt,line=3,labels="Total")


## stratified Aalen-Johansen estimator in nearest neighborhoods
## of a continuous variable
ajfitX <- prodlim(Hist(time, event) ~ X1+X2, data = datCR)
plot(ajfitX,newdata=data.frame(X1=c(1,1,0),X2=c(4,10,10)))
plot(ajfitX,newdata=data.frame(X1=c(1,1,0),X2=c(4,10,10)),cause=2)

## stacked plot

plot(ajfitX,
     newdata=data.frame(X1=0,X2=0.1),
     cause="stacked",
     legend.title="X1=0,X2=0.1",
     legend.legend=paste("cause:",getStates(ajfitX$model.response)),
     plot.main="Subject specific stacked plot")

prodlim documentation built on June 24, 2024, 5:08 p.m.