ibsc.comp {survcomp} | R Documentation |
This function compares two integrated Briers scores (IBSC) through the estimation of the Brier scores (BSC) at some points in time. The statistical test is a Wilcoxon rank sum test for dependent samples.
ibsc.comp(bsc1, bsc2, time)
bsc1 |
vector of BSCs computed from the first predicted probabilities for some points in time |
bsc2 |
vector of BSCs computed from the second predicted probabilities for some points in time |
time |
vector of points in time for which the BSCs are computed |
The two vectors of BSCs must be computed from the same samples (and corresponding survival data) and for the same points in time. The function uses a Wilcoxon rank sum test for dependent samples.
p.value |
p-value from the Wilcoxon rank sum test for the comparison ibsc1 < ibsc2 |
ibsc1 |
value of the IBSC for the first set of BSCs |
ibsc2 |
value of the IBSC for the second set of BSCs |
Benjamin Haibe-Kains
Wilcoxon, F. (1945) "Individual comparisons by ranking methods", Biometrics Bulletin, 1, pages 80–83.
Haibe-Kains, B. and Desmedt, C. and Sotiriou, C. and Bontempi, G. (2008) "A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all?", Bioinformatics, 24, 19, pages 2200–2208.
set.seed(12345) age <- rnorm(30, 50, 10) size <- rexp(30,1) stime <- rexp(30) cens <- runif(30,.5,2) sevent <- as.numeric(stime <= cens) stime <- pmin(stime, cens) ##Brier scores ##size dd <- data.frame("time"=stime, "event"=sevent, "score"=size) bsc1 <- sbrier.score2proba(data.tr=dd, data.ts=dd, method="cox") ##size dd <- data.frame("time"=stime, "event"=sevent, "score"=age) bsc2 <- sbrier.score2proba(data.tr=dd, data.ts=dd, method="cox") if(!all(bsc1$time == bsc2$time)) { stop("the two vector of BSCs must be computed for the same points in time!") } ibsc.comp(bsc1=bsc1$bsc, bsc2=bsc2$bsc, time=bsc1$time)