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glioma

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.

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 right-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 Cancer 86(2), 207–212. doi: 10.1038/sj.bjc.6600047

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(nresample = 10000))

coin

Conditional Inference Procedures in a Permutation Test Framework

v1.4-1

GPL-2

Authors

Torsten Hothorn [aut, cre] (<https://orcid.org/0000-0001-8301-0471>), Henric Winell [aut] (<https://orcid.org/0000-0001-7995-3047>), Kurt Hornik [aut] (<https://orcid.org/0000-0003-4198-9911>), Mark A. van de Wiel [aut] (<https://orcid.org/0000-0003-4780-8472>), Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>)

Initial release

2021-02-08