使用randomForestSRC在特定时间点生存的概率

Question

I'm using rfsrc to model a survival problem, like this:

library(OIsurv)
library(survival)
library(randomForestSRC)

data(burn)
attach(burn)

library(randomForestSRC)

fit <- rfsrc(Surv(T1, D1) ~  ., data=burn)

# predict on the train set
pred <- predict(fit, burn, OOB=TRUE, type=response)
pred$predicted

this gives me the overall survival probability of all patients.

How do I get the survival probability for each person for different timepoints, say 0-5 months or 0-10 months?

Answer 1

The documentation on this isn't immediately obvious if you aren't familiar with the package, but it is possible.

Load data

data(pbc, package = "randomForestSRC")

Create trial and test datasets

pbc.trial <- pbc %>% filter(!is.na(treatment))
pbc.test <- pbc %>% filter(is.na(treatment))

Build our model

rfsrc_pbc <- rfsrc(Surv(days, status) ~ .,
                   data = pbc.trial,
                   na.action = "na.impute")

Test out model

test.pred.rfsrc <- predict(rfsrc_pbc, 
                           pbc.test,
                           na.action="na.impute")

All of the good stuff is held within our prediction object. The $survival object is a matrix of n rows (1 per patient) and n columns (one per time.interest - these are automatically chosen though you can constrain them using the ntime argument. Our matrix is 106x122)

test.pred.rfsrc$survival

The $time.interest object is a list of the different "time.interests" (122, same as the number of columns in our matrix from $surival )

test.pred.rfsrc$time.interest

Let's say we wanted to see our predicted status at 5 years, we would
need to figure out which time interest was closest to 1825 days (since our measurement period is days) when we look at our $time.interest object, we see that row 83 = 1827 days or roughly 5 years. row 83 in $time.interest corresponds to column 83 in our $survival matrix. Thus to see the predicted probability of survival at 5 years we would just look at column 83 of our matrix.

test.pred.rfsrc$survival[,83]

You could then do this for whichever timepoints you're interested in.