IBS greater than 1 issue

[thebasheersj]

I have tried SurvMetrics on many datasets in my work, but in two datasets, It gives me IBS value greater than 1 which can't be so.
The code where I get wrong IBS is below for verification purpose.

`# packages
library(survival)
library(randomForestSRC)
library(glmnet)
library(caTools)
library(tidyverse)
library(SurvMetrics)

data(peakVO2, package = "randomForestSRC")

Data splitting

set.seed(2345)
peak.sample <- sample.split(peakVO2$ttodead, 0.80)
tr.peak<- subset(peakVO2, peak.sample == T)
ts.peak<- subset(peakVO2, peak.sample == F)
dim(ts.peak)

Fit RSF model

set.seed(23456)
peak.rsf<- rfsrc(Surv(ttodead, died)~., ntree = 1000, importance = T, statistics = T, tr.peak)
peak.rsf

peak.rsf.pred<- predict(peak.rsf, ts.peak)$survival

peakful_time<- peak.rsf$time.interest
peakful_time

surv_peakful = Surv(ts.peak$ttodead, ts.peak$died)
surv_peakful

set.seed(75943038)
peakful_IBS<- IBS(surv_peakful, sp_matrix = peak.rsf.pred, peakful_time)
peakful_IBS

Result: 1.203971`

[LauricF]

I had a similar problem and the Brier function would return a value of 134 in my dataset.

I think the mistake comes from the Brier function,

particularly between the line 76 to 92;

The current code is:
Gtstar <- Gt(object, t_star)
for (i in c(1:length(time))) {
if (time[i] < t_star & (status[i] == 1)) {
Gti <- Gt(Surv(time, status), time[i])
if (is.na(Gti)) {
next
}
sum_before_t <- sum_before_t + 1/Gti * (pre_sp[i])^2
next
}
if (time[i] >= t_star) {
if (is.na(Gtstar)) {
next
}
sum_after_t <- sum_after_t + 1/Gt(Surv(time, status),
t_star) * (1 - pre_sp[i])^2
}
}

For a reason that I cannot yet understand Gt(Surv(time, status), t_star) return a different result that Gt(object, t_star). Gt(Surv(time, status), t_star) return a very small value which inflate the Brier score and so the IBS too.
When I corrected the code with the use of Gt(object, t_star) instead of Gt(Surv(time, status), t_star) I had a meaningful result below 1.

corrected code
Gtstar <- Gt(object, t_star)
for (i in c(1:length(time))) {
if (time[i] < t_star & (status[i] == 1)) {
Gti <- Gt(object, time[i])
if (is.na(Gti)) {
next
}
sum_before_t <- sum_before_t + 1/Gti * (pre_sp[i])^2
next
}
if (time[i] >= t_star) {
if (is.na(Gtstar)) {
next
}
sum_after_t <- sum_after_t + 1/Gtstar * (1 - pre_sp[i])^2
}
}

[anlo328]

Hello, any update on this? I'm having the same issue; I gave the exact same data with the same integration interval to two different implementations and got:

SurvMetrics::IBS -> 4.121665
survex::integrated_brier_score -> 0.1830576

Seems the second makes more sense