GBSG2 {ipred}R Documentation

German Breast Cancer Study Group 2

Description

A data frame containing the observations from the GBSG2 study.

Usage

data(GBSG2)

Format

This data frame contains the observations of 686 women:

horTh
hormonal therapy, a factor at two levels no and yes.
age
of the patients in years.
menostat
menopausal status, a factor at two levels pre (premenopausal) and post (postmenopausal).
tsize
tumor size (in mm).
tgrade
tumor grade, a ordered factor at levels I < II < III.
pnodes
number of positive nodes.
progrec
progesterone receptor (in fmol).
estrec
estrogen receptor (in fmol).
time
recurrence free survival time (in days).
cens
censoring indicator (0- censored, 1- event).

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 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.

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)


[Package ipred version 0.8-1 Index]