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.
no.
patient number.
age
patient age (years).
sex
a factor with levels "F" (Female) and "M" (Male).
histology
a factor with levels "GBM" (grade IV) and "Grade3" (grade
III).
group
a factor with levels "Control" and "RIT".
event
status indicator for time: FALSE for censored observations
and TRUE otherwise.
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 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. British Journal of Cancer86(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))