https://github.com/cran/coin
Revision 058ed94367feed3a1f8ca93d1837d226eada4fc9 authored by Torsten Hothorn on 28 November 2016, 12:04:30 UTC, committed by cran-robot on 28 November 2016, 12:04:30 UTC
1 parent b633b8b
Tip revision: 058ed94367feed3a1f8ca93d1837d226eada4fc9 authored by Torsten Hothorn on 28 November 2016, 12:04:30 UTC
version 1.1-3
version 1.1-3
Tip revision: 058ed94
glioma.Rd
\name{glioma}
\docType{data}
\alias{glioma}
\title{Malignant Glioma Pilot Study}
\description{
A non-randomized pilot study on malignant glioma patients with pretargeted
adjuvant radioimmunotherapy using yttrium-90-biotin.
}
\usage{glioma}
\format{
A data frame with 37 observations on 7 variables.
\describe{
\item{\code{no.}}{
patient number.
}
\item{\code{age}}{
patient age (years).
}
\item{\code{sex}}{
a factor with levels \code{"F"} (Female) and \code{"M"} (Male).
}
\item{\code{histology}}{
a factor with levels \code{"GBM"} (grade IV) and \code{"Grade3"} (grade
III).
}
\item{\code{group}}{
a factor with levels \code{"Control"} and \code{"RIT"}.
}
\item{\code{event}}{
status indicator for \code{time}: \code{FALSE} for censored observations
and \code{TRUE} otherwise.
}
\item{\code{time}}{
survival time (months).
}
}
}
\details{
The primary endpoint of this small pilot study is survival. Since the
survival times are tied, the classical asymptotic logrank test may be
inadequate in this setup. Therefore, a permutation test using Monte Carlo
resampling was computed in the original paper. The data are taken from Tables
1 and 2 of Grana \emph{et al.} (2002).
}
\source{
Grana, C., Chinol, M., Robertson, C., Mazzetta, C., Bartolomei, M., De Cicco,
C., Fiorenza, M., Gatti, M., Caliceti, P. and Paganelli, G. (2002).
Pretargeted adjuvant radioimmunotherapy with Yttrium-90-biotin in malignant
glioma patients: A pilot study. \emph{British Journal of Cancer}
\bold{86}(2), 207--212.
}
\examples{
## Grade III glioma
g3 <- subset(glioma, histology == "Grade3")
## Plot Kaplan-Meier estimates
op <- par(no.readonly = TRUE) # save current settings
layout(matrix(1:2, ncol = 2))
plot(survfit(Surv(time, event) ~ group, data = g3),
main = "Grade III Glioma", lty = 2:1,
ylab = "Probability", xlab = "Survival Time in Month",
xlim = c(-2, 72))
legend("bottomleft", lty = 2:1, c("Control", "Treated"), bty = "n")
## Exact logrank test
logrank_test(Surv(time, event) ~ group, data = g3,
distribution = "exact")
## Grade IV glioma
gbm <- subset(glioma, histology == "GBM")
## Plot Kaplan-Meier estimates
plot(survfit(Surv(time, event) ~ group, data = gbm),
main = "Grade IV Glioma", lty = 2:1,
ylab = "Probability", xlab = "Survival Time in Month",
xlim = c(-2, 72))
legend("topright", lty = 2:1, c("Control", "Treated"), bty = "n")
par(op) # reset
## Exact logrank test
logrank_test(Surv(time, event) ~ group, data = gbm,
distribution = "exact")
## Stratified approximative (Monte Carlo) logrank test
logrank_test(Surv(time, event) ~ group | histology, data = glioma,
distribution = approximate(B = 10000))
}
\keyword{datasets}
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