glioma {coin} | R Documentation

## 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))

[Package coin version 1.3-0 Index]