glioma                 package:coin                 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)

     _t_i_m_e survival times in month.

     _e_v_e_n_t censoring indicator: 'FALSE' censored and 'TRUE' 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, package = "coin")

     par(mfrow=c(1,2))

     ### 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 = "approx", B = 10000)

