glioma            package:exactRankTests            R Documentation

_M_a_l_i_g_n_a_n_t _G_l_i_o_m_a _P_i_l_o_t _S_t_u_d_y

_D_e_s_c_r_i_p_t_i_o_n:

     A non-randomized pilot study on malignant glioma patients with 
     pretargeted adjuvant radioimmunotherapy using Yttrium-90-biotin.

_U_s_a_g_e:

     data(glioma)

_F_o_r_m_a_t:

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

     _N_o. patient number.

     _A_g_e patients ages in years.

     _S_e_x a factor with levels 'F'(emale) and 'M'(ale). 

     _H_i_s_t_o_l_o_g_y a factor with levels 'GBM' (grade IV) and  'Grade3'
          (grade III)

     _S_u_r_v_i_v_a_l survival times in month.

     _C_e_n_s censoring indicator: '0' censored and '1' dead.

     _G_r_o_u_p a factor with levels 'Control' and 'RIT'.

_D_e_t_a_i_l_s:

     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).

_S_o_u_r_c_e:

     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.

_E_x_a_m_p_l_e_s:

     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)
     }

