ipred (version 0.6-14)

GBSG2: German Breast Cancer Study Group 2

Description

A data frame containing the observations from the GBSG2 study.

Usage

data(GBSG2)

source

http://www.blackwellpublishers.com/rss/Volumes/A162p1.htm

References

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 $2\times2$ 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.

Examples

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)