GBSG2                 package:ipred                 R Documentation

_G_e_r_m_a_n _B_r_e_a_s_t _C_a_n_c_e_r _S_t_u_d_y _G_r_o_u_p _2

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

     A data frame containing the observations from the  GBSG2 study.

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

     data(GBSG2)

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

     This data frame contains the observations of 686 women:

     _h_o_r_T_h hormonal therapy, a factor at two levels 'no' and 'yes'.

     _a_g_e of the patients in years.

     _m_e_n_o_s_t_a_t menopausal status, a factor at two levels 'pre' 
          (premenopausal) and 'post' (postmenopausal).

     _t_s_i_z_e tumor size (in mm).

     _t_g_r_a_d_e tumor grade, a ordered factor at levels 'I < II < III'.

     _p_n_o_d_e_s number of positive nodes.

     _p_r_o_g_r_e_c progesterone receptor (in fmol).

     _e_s_t_r_e_c estrogen receptor (in fmol).

     _t_i_m_e recurrence free survival time (in days).

     _c_e_n_s censoring indicator (0- censored, 1- event).

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

     <URL: http://www.blackwellpublishers.com/rss/Volumes/A162p1.htm>

_R_e_f_e_r_e_n_c_e_s:

     M. Schumacher, G. Basert, H. Bojar,  K. Huebner, M. Olschewski, 
     W. Sauerbrei, C. Schmoor, C. Beyerle, R.L.A. Neumann and H.F.
     Rauschecker for the German Breast Cancer Study Group (1994),
     Randomized 2times2 trial evaluating hormonal treatment and the
     duration of chemotherapy in node-positive breast cancer patients.
     _Journal of Clinical Oncology_, *12*, 2086-2093.

     W. Sauerbrei and P. Royston (1999). Building multivariable
     prognostic and diagnostic models: transformation of the predictors
     by using  fractional polynomials. _Journal of the Royal Statistics
     Society Series A_, Volume *162*(1), 71-94.

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

     data(GBSG2)

     thsum <- function(x) {
       ret <- c(median(x), quantile(x, 0.25), quantile(x,0.75))
       names(ret)[1] <- "Median"
       ret
     }

     t(apply(GBSG2[,c("age", "tsize", "pnodes", 
                      "progrec", "estrec")], 2, thsum))

     table(GBSG2$menostat)
     table(GBSG2$tgrade)
     table(GBSG2$horTh)

     # pooled Kaplan-Meier

     mod <- survfit(Surv(time, cens), data=GBSG2)
     # integrated Brier score
     sbrier(Surv(GBSG2$time, GBSG2$cens), mod)
     # Brier score at 5 years
     sbrier(Surv(GBSG2$time, GBSG2$cens), mod, btime=1825)

     # Nottingham prognostic index

     GBSG2 <- GBSG2[order(GBSG2$time),]

     NPI <- 0.2*GBSG2$tsize/10 + 1 + as.integer(GBSG2$tgrade)
     NPI[NPI < 3.4] <- 1
     NPI[NPI >= 3.4 & NPI <=5.4] <- 2
     NPI[NPI > 5.4] <- 3

     mod <- survfit(Surv(time, cens) ~ NPI, data=GBSG2)
     plot(mod)

     pred <- c()
     survs <- c()
     for (i in sort(unique(NPI)))
         survs <- c(survs, getsurv(mod[i], 1825))

     for (i in 1:nrow(GBSG2))
        pred <- c(pred, survs[NPI[i]])

     # Brier score of NPI at t=5 years
     sbrier(Surv(GBSG2$time, GBSG2$cens), pred, btime=1825)

