survivalSL (version 0.98)

predict.sltime: Prediction from a Super Learner for Censored Outcomes

Description

Predict the survival of new observations based on an SL by using the survivalSL function.

Usage

# S3 method for sltime
predict(object, newdata, newtimes, ...)

Value

predictions

A list of matrix with the predictions of survivals of each subject (lines) for each observed time (columns) for each model used for the superlearner construction and the superlearner itself.

times

A vector of numeric values with the times of the predictions.

Arguments

object

An object returned by the function survivalSL.

newdata

An optional data frame containing covariate values at which to produce predicted values. The default value is NULL, the predicted values are computed for the subjects of the training sample.

newtimes

The times at which to produce predicted values. The default value is NULL, the predicted values are computed for the observed times in the training data frame.

...

For future methods.

See Also

survivalSL.

Examples

data("dataDIVAT2")

# The training of the super learner from the first 150 individuals of the data base


formula<-Surv(times,failures) ~ age + hla + retransplant + ecd

sl1 <- survivalSL(formula, data=dataDIVAT2[1:150,],
                  method=c("LIB_COXridge", "LIB_AFTggamma"), metric="auc", pro.time = 12, cv=3)

# Individual prediction for 2 new subjects
pred <- predict(sl1,
  newdata=data.frame(age=c(52,52),
  hla=c(0,1), retransplant=c(1,1), ecd=c(0,1)))

plot(y=pred$predictions$sl[1,], x=pred$times,
xlab="Time (years)",
ylab="Predicted survival",
col=1, type="l", lty=1, lwd=2, ylim=c(0,1))

lines(y=pred$predictions$sl[2,], x=pred$times,
col=2, type="l", lty=1, lwd=2)

legend("bottomright", col=c(1,2), lty=1, lwd=2,
c("Subject #1", "Subject #2"))