swh:1:snp:813359ba77493c9d5dd1abad9a1f53490a8abf57
Tip revision: 2b221e4e7ade59d5c33d4f9f277e802c90a7656c authored by Torsten Hothorn on 26 April 2013, 00:00:00 UTC
version 1.0-22
version 1.0-22
Tip revision: 2b221e4
glioma.Rd
\name{glioma}
\alias{glioma}
\docType{data}
\title{ Malignant Glioma Pilot Study}
\description{
A non-randomized pilot study on malignant glioma patients with
pretargeted adjuvant radioimmunotherapy using Yttrium-90-biotin.
}
\usage{data("glioma")}
\format{
A data frame with 37 observations on the following 7 variables.
\describe{
\item{no.}{patient number.}
\item{age}{patients ages in years.}
\item{sex}{a factor with levels \code{F}(emale) and \code{M}(ale). }
\item{histology}{a factor with levels \code{GBM} (grade IV) and
\code{Grade3} (grade III)}
\item{time}{survival times in month.}
\item{event}{censoring indicator: \code{FALSE} censored and \code{TRUE} dead.}
\item{group}{a factor with levels \code{Control} and \code{RIT}.}
}
}
\details{
The primary endpoint of this small pilot study is survival.
Survival times are tied, the usual asymptotic log-rank test may be
inadequate in this setup. Therefore, a permutation test (via Monte-Carlo sampling)
was conducted in the original paper. The data are taken from Tables 1 and 2 of
Grana et al. (2002).
}
\source{
C. Grana, M. Chinol, C. Robertson, C. Mazzetta, M. Bartolomei, C. De
Cicco, M. Fiorenza, M. Gatti, P. Caliceti \& G. Paganelli (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{
layout(matrix(1:2, ncol = 2))
### Grade III glioma
g3 <- subset(glioma, histology == "Grade3")
### Plot Kaplan-Meier curves
plot(survfit(Surv(time, event) ~ group, data = g3),
main = "Grade III Glioma", lty = c(2,1),
legend.text = c("Control", "Treated"),
legend.bty = 1, ylab = "Probability",
xlab = "Survival Time in Month")
### logrank test
surv_test(Surv(time, event) ~ group, data = g3,
distribution = "exact")
### Grade IV glioma
gbm <- subset(glioma, histology == "GBM")
### Plot Kaplan-Meier curves
plot(survfit(Surv(time, event) ~ group, data = gbm),
main = "Grade IV Glioma", lty = c(2,1),
legend.text = c("Control", "Treated"),
legend.bty = 1, legend.pos = 1, ylab = "Probability",
xlab = "Survival Time in Month")
### logrank test
surv_test(Surv(time, event) ~ group, data = gbm,
distribution = "exact")
### stratified logrank test
surv_test(Surv(time, event) ~ group | histology, data = glioma,
distribution = approximate(B = 10000))
}
\keyword{datasets}