GBSG2 {ipred} | R Documentation |
A data frame containing the observations from the GBSG2 study.
data(GBSG2)
This data frame contains the observations of 686 women:
no
and
yes
.pre
(premenopausal) and post
(postmenopausal).I < II <
III
.http://www.blackwellpublishers.com/rss/Volumes/A162p1.htm
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.
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$horTh) # pooled Kaplan-Meier mod <- survfit(Surv(time, cens) ~ 1, data=GBSG2) # integrated Brier score sbrier(Surv(GBSG2$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)