kaplan.plot: Kaplan-Meier plots of (repeated) time-to-event data

In xpose4: Diagnostics for Nonlinear Mixed-Effect Models

Kaplan-Meier plots of (repeated) time-to-event data

Description

Kaplan-Meier plots of (repeated) time-to-event data. Includes VPCs.

Usage

kaplan.plot(
  x = "TIME",
  y = "DV",
  id = "ID",
  data = NULL,
  evid = "EVID",
  by = NULL,
  xlab = "Time",
  ylab = "Default",
  object = NULL,
  events.to.plot = "All",
  sim.data = NULL,
  sim.zip.file = NULL,
  VPC = FALSE,
  nsim.lab = "simNumber",
  sim.evct.lab = "counter",
  probs = c(0.025, 0.975),
  add.baseline = T,
  add.last.area = T,
  subset = NULL,
  main = "Default",
  main.sub = "Default",
  main.sub.cex = 0.8,
  nbins = NULL,
  real.type = "l",
  real.lty = 1,
  real.lwd = 1,
  real.col = "blue",
  real.se = if (!is.null(sim.data)) F else T,
  real.se.type = "l",
  real.se.lty = 2,
  real.se.lwd = 0.5,
  real.se.col = "red",
  cens.type = "l",
  cens.lty = 1,
  cens.col = "black",
  cens.lwd = 1,
  cens.rll = 0.02,
  inclZeroWRES = TRUE,
  onlyfirst = FALSE,
  samp = NULL,
  poly.alpha = 1,
  poly.fill = "lightgreen",
  poly.line.col = "darkgreen",
  poly.lty = 2,
  censor.lines = TRUE,
  ylim = c(-5, 105),
  cov = NULL,
  cov.fun = "mean",
  ...
)

Arguments

x	The independent variable.
y	The dependent variable. event (>0) or no event (0).
id	The ID variable in the dataset.
data	A dataset can be used instead of the data in an Xpose object. Must have the same form as an xpose data object xpdb@Data.
evid	The EVID data item. If not present then all rows are considered events (can be censored or an event). Otherwise, EVID!=0 are dropped from the data set.
by	A vector of conditioning variables.
xlab	X-axis label
ylab	Y-axis label
object	An Xpose object. Needed if no data is supplied.
events.to.plot	Vector of events to be plotted. "All" means that all events are plotted.
sim.data	The simulated data file. Should be a table file with one header row and have, at least, columns with headers corresponding to x, y, id, by (if used), nsim.lab and sim.evct.lab.
sim.zip.file	The sim.data can be in \.zip format and xpose will unzip the file before reading in the data. Must have the same structure as described above in sim.data.
VPC	TRUE or FALSE. If TRUE then Xpose will search for a zipped file with name paste("simtab",object@Runno,".zip",sep=""), for example "simtab42.zip".
nsim.lab	The column header for sim.data that contains the simulation number for that row in the data.
sim.evct.lab	The column header for sim.data that contains the individual event counter information. For each individual the event counter should increase by one for each event (or censored event) that occurs.
probs	The probabilities (non-parametric percentiles) to use in computation of the prediction intervals for the simulated data.
add.baseline	Should a (x=0,y=1) baseline measurement be added to each individual in the dataset. Otherwise each plot will begin at the first event in the dataset.
add.last.area	Should an area be added to the VPC extending the last PI?
subset	The subset of the data and sim.data to use.
main	The title of the plot. Can also be NULL or "Default".
main.sub	The title of the subplots. Must be a list, the same length as the number of subplots (actual graphs), or NULL or "Default".
main.sub.cex	The size of the title of the subplots.
nbins	The number of bins to use in the VPC. If NULL, the the number of unique x values in sim.data is used.
real.type	Type for the real data.
real.lty	Line type (lty) for the curve of the original (or real) data.
real.lwd	Line width (lwd) for the real data.
real.col	Color for the curve of the original (or real) data.
real.se	Should the standard errors of the real (non simulated) data be plotted? Calculated using survfit.
real.se.type	Type for the standard errors.
real.se.lty	Line type (lty) for the standard error lines.
real.se.lwd	Line width (lwd) for the standard error lines.
real.se.col	Color for the standard error lines.
cens.type	Type for the censored lines.
cens.lty	Line type (lty) for the censored lines.
cens.col	Color for the censored lines.
cens.lwd	Line width for the censored lines.
cens.rll	The relative line length of the censored line compared to the limits of the y-axis.
inclZeroWRES	Include WRES=0 rows from the real data set in the plots?
onlyfirst	Include only the first measurement for the real data in the plots?
samp	Simulated data in the xpose data object can be used as the "real" data. samp is a number selecting which simulated data set to use.
poly.alpha	The transparency of the VPC shaded region.
poly.fill	The fill color of the VPC shaded region.
poly.line.col	The line colors for the VPC region.
poly.lty	The line type for the VPC region.
censor.lines	Should censored observations be marked on the plot?
ylim	Limits for the y-axes
cov	The covariate in the dataset to plot instead of the survival curve.
cov.fun	The summary function for the covariate in the dataset to plot instead of the survival curve.
...	Additional arguments passed to the function.

Value

returns an object of class "xpose.multiple.plot".

Author(s)

Andrew C. Hooker

See Also

survfit, Surv, xpose.multiple.plot.

Other specific functions: absval.cwres.vs.cov.bw(), absval.cwres.vs.pred(), absval.cwres.vs.pred.by.cov(), absval.iwres.cwres.vs.ipred.pred(), absval.iwres.vs.cov.bw(), absval.iwres.vs.idv(), absval.iwres.vs.ipred(), absval.iwres.vs.ipred.by.cov(), absval.iwres.vs.pred(), absval.wres.vs.cov.bw(), absval.wres.vs.idv(), absval.wres.vs.pred(), absval.wres.vs.pred.by.cov(), absval_delta_vs_cov_model_comp, addit.gof(), autocorr.cwres(), autocorr.iwres(), autocorr.wres(), basic.gof(), basic.model.comp(), cat.dv.vs.idv.sb(), cat.pc(), cov.splom(), cwres.dist.hist(), cwres.dist.qq(), cwres.vs.cov(), cwres.vs.idv(), cwres.vs.idv.bw(), cwres.vs.pred(), cwres.vs.pred.bw(), cwres.wres.vs.idv(), cwres.wres.vs.pred(), dOFV.vs.cov(), dOFV.vs.id(), dOFV1.vs.dOFV2(), data.checkout(), dv.preds.vs.idv(), dv.vs.idv(), dv.vs.ipred(), dv.vs.ipred.by.cov(), dv.vs.ipred.by.idv(), dv.vs.pred(), dv.vs.pred.by.cov(), dv.vs.pred.by.idv(), dv.vs.pred.ipred(), gof(), ind.plots(), ind.plots.cwres.hist(), ind.plots.cwres.qq(), ipred.vs.idv(), iwres.dist.hist(), iwres.dist.qq(), iwres.vs.idv(), par_cov_hist, par_cov_qq, parm.vs.cov(), parm.vs.parm(), pred.vs.idv(), ranpar.vs.cov(), runsum(), wres.dist.hist(), wres.dist.qq(), wres.vs.idv(), wres.vs.idv.bw(), wres.vs.pred(), wres.vs.pred.bw(), xpose.VPC(), xpose.VPC.both(), xpose.VPC.categorical(), xpose4-package

Examples

## Not run: 
library(xpose4)

## Read in the data
runno <- "57"
xpdb <- xpose.data(runno)

####################################
# here are the real data plots
####################################

kaplan.plot(x="TIME",y="DV",object=xpdb)
kaplan.plot(x="TIME",y="DV",object=xpdb,
            events.to.plot=c(1,2),
            by=c("DOSE==0","DOSE!=0"))
kaplan.plot(x="TIME",y="DV",object=xpdb,
            events.to.plot=c(1,2),
            by=c("DOSE==0","DOSE==10",
            "DOSE==50","DOSE==200"))

## make a PDF of the plots
pdf(file=paste("run",runno,"_kaplan.pdf",sep=""))
kaplan.plot(x="TIME",y="DV",object=xpdb,
            by=c("DOSE==0","DOSE==10",
            "DOSE==50","DOSE==200"))
dev.off()

####################################
## VPC plots
####################################

kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,events.to.plot=c(1))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            events.to.plot=c(1,2,3),
            by=c("DOSE==0","DOSE!=0"))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            events.to.plot=c(1),
            by=c("DOSE==0","DOSE==10","DOSE==50","DOSE==200"))

## make a PDF of all plots
pdf(file=paste("run",runno,"_kaplan.pdf",sep=""))
kaplan.plot(x="TIME",y="DV",object=xpdb,VPC=T,
            by=c("DOSE==0","DOSE==10","DOSE==50","DOSE==200"))
dev.off()

## End(Not run)

xpose4 documentation built on May 29, 2024, 7:56 a.m.