predict.cox: Prediction from a Penalized Cox Regression

In RISCA: Causal Inference and Prediction in Cohort-Based Analyses

Prediction from a Penalized Cox Regression

Description

Predict the survival of new observations based on a penalized Cox regression estimated by unsing a model of the class cox.

Usage

## S3 method for class 'cox'
predict(object, ..., newdata, newtimes)

Arguments

object	An object returned by one of the following functions: cox.lasso, cox.ridge, or cox.en.
...	Further arguments passed.
newdata	An optional data frame containing covariate values at which to produce predicted values. There must be a column for every covariate included in cov.quanti and cov.quali included in the training sample. 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 isNULL, the predicted values are computed for the observed times in the training data frame.

Value

times	A vector of numeric values with the times of the predictions.
predictions	A matrix with the predictions of survivals of each subject (lines) for each observed times (columns).

Author(s)

Yohann Foucher <[e-mail]>

Camille Sabathe <[e-mail]>

Examples

data(dataDIVAT2)

# The estimation of the training model
model<-cox.lasso(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), lambda=.01)

# Predicted survival from the validation sample
pred <- predict(model,
  newdata=data.frame(age=c(52,52), hla=c(0,1), retransplant=c(1,1), ecd=c(0,1)))

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

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

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

RISCA documentation built on March 31, 2023, 11:06 p.m.