sibipx
commented
10 months ago Small update: not only in terms of computational performance, but also in terms on functional behaviour, ntime does not seem to make a difference when passed as a vector of discrete values. It looks like it is parsed correctly in the R code but it seems that the trees are grown on all times (I get the exact same splits and the exact same predictions no matter how I set ntime).
I think I can get the behaviour I want (and a decent computational time) by manually manipulating the eventtime (ceiling) and manually applying administrative censoring in the data), but I would appreciate a clarification on the expected behaviour of ntime. Thanks!
data(pbc, package = "randomForestSRC")
pbc <- pbc[,c("days", "status", "copper")]
pbc <- na.omit(pbc)
# manipulate the event times to simplify matters
pbc$days <- ceiling(pbc$days/100) * 100
sort(unique(pbc$days))
# survival ntime 100 to 700
set.seed(2023)
RF_model_surv_7 <- rfsrc(Surv(days, status) ~ ., pbc,
ntree = 1,
nodesize = 30,
ntime = (1:7) * 100)
plot(get.tree(RF_model_surv_7, 1))
RF_model_surv_7$time.interest
# survival ntime 100 to 3000
set.seed(2023)
RF_model_surv_30 <- rfsrc(Surv(days, status) ~ ., pbc,
ntree = 1,
nodesize = 30,
ntime = (1:30) * 100)
plot(get.tree(RF_model_surv_30, 1))
RF_model_surv_30$time.interest
identical(RF_model_surv_7$survival.oob[,1:7],
RF_model_surv_30$survival.oob[,1:7])
# survival with no timepoints
set.seed(2023)
RF_model_surv_notime <- rfsrc(Surv(days, status) ~ ., pbc,
ntree = 1,
nodesize = 30)
plot(get.tree(RF_model_surv_notime, 1))
RF_model_surv_notime$time.interest
identical(RF_model_surv_7$survival.oob[,1:7],
RF_model_surv_notime$survival.oob[,1:7])
# adm censoring at 7
pbc_admcens <- pbc
pbc_admcens$status <- ifelse(pbc_admcens$days > 700, 0, pbc_admcens$status)
set.seed(2023)
RF_model_surv_7_admcens <- rfsrc(Surv(days, status) ~ ., pbc_admcens,
ntree = 1,
nodesize = 30,
ntime = (1:7) * 100)
plot(get.tree(RF_model_surv_7_admcens, 1))
RF_model_surv_7_admcens$time.interest
identical(RF_model_surv_7$survival.oob[,1:7],
RF_model_surv_7_admcens$survival.oob[,1:7])
To speed up calculations, and as I am not interested in predictions at all event times in the dataset, I specify ntime for both survival and competing risks models. (ntime = 1:7)
I would expect that the logrank split statistic is calculated by summing over these 7 ntimes (or closeby proxys from the provided eventimes). I would expect it to reduce the runtime as in the situation when I provide discrete eventimes in the dataset (1,2,3,4,5,6,7).
There is though a big difference between the two situations. The difference is larger for competing risks.
As I am working with large datasets and running a lot of models, working only with ntime (without rounding the eventtimes with ceiling) is not feasible at all for me.
Of course, I might have wrong expectations about what ntime does; my expectation that it would run as fast as in the situation of discrete times might be wrong. I would appreciate an explanation.
See below an example with the runtime on my Windows machine in comments.
Thank you!