| glioma {coin} | R Documentation |
A non-randomized pilot study on malignant glioma patients with pretargeted adjuvant radioimmunotherapy using Yttrium-90-biotin.
data("glioma")
A data frame with 37 observations on the following 7 variables.
patient number.
patients ages in years.
a factor with levels F(emale) and M(ale).
a factor with levels GBM (grade IV) and
Grade3 (grade III)
survival times in month.
censoring indicator: FALSE censored and TRUE dead.
a factor with levels Control and RIT.
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).
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. British Journal of Cancer 86(2), 207–212.
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))