R: Malignant Glioma Pilot Study

glioma {exactRankTests} | 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

    data(glioma)

### Format

A data frame with 37 observations on the following 7 variables.

No.

  patient number.

Age

  patients ages in years.

Sex

  a factor with levels `F`(emale) and `M`(ale).

Histology

  a factor with levels `GBM` (grade IV) and `Grade3` (grade III)

Survival

  survival times in month.

Cens

  censoring indicator: `0` censored and `1` dead.

Group

  a factor with levels `Control` and `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. British Journal of Cancer, 86(2), 207–212.

### Examples

    data(glioma)

    if(require(survival, quietly = TRUE)) {

      par(mfrow=c(1,2))

      # Grade III glioma
      g3 <- glioma[glioma$Histology == "Grade3",]

      # Plot Kaplan-Meier curves
      plot(survfit(Surv(Survival, Cens) ~ 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")

      # log-rank test
      survdiff(Surv(Survival, Cens) ~ Group, data=g3)

      # permutation test with integer valued log-rank scores
      lsc <- cscores(Surv(g3$Survival, g3$Cens), int=TRUE)
      perm.test(lsc ~ Group, data=g3)

      # permutation test with real valued log-rank scores
      lsc <- cscores(Surv(g3$Survival, g3$Cens), int=FALSE)
      tr <- (g3$Group == "RIT")
      T <- sum(lsc[tr])
      pperm(T, lsc, sum(tr), alternative="tw")
      pperm(T, lsc, sum(tr), alternative="tw", simulate=TRUE)

      # Grade IV glioma
      gbm <- glioma[glioma$Histology == "GBM",]

      # Plot Kaplan-Meier curves
      plot(survfit(Surv(Survival, Cens) ~ 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")

      # log-rank test
      survdiff(Surv(Survival, Cens) ~ Group, data=gbm)

      # permutation test with integer valued log-rank scores
      lsc <- cscores(Surv(gbm$Survival, gbm$Cens), int=TRUE)
      perm.test(lsc ~ Group, data=gbm)

      # permutation test with real valued log-rank scores
      lsc <- cscores(Surv(gbm$Survival, gbm$Cens), int=FALSE)
      tr <- (gbm$Group == "RIT")
      T <- sum(lsc[tr])
      pperm(T, lsc, sum(tr), alternative="tw")
      pperm(T, lsc, sum(tr), alternative="tw", simulate=TRUE)
    }

[Package exactRankTests version 0.8-29 Index]