LIB_COXen {survivalSL}

Library of the Super Learner for Elastic Net Cox Regression

Description

Fit an elastic net Cox regression for fixed values of the regularization parameters.

Usage

LIB_COXen(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, alpha, lambda)

Arguments

times

The name of the variable related the numeric vector with the follow-up times.

failures

The name of the variable related the numeric vector with the event indicators (0=right censored, 1=event).

group

The name of the variable related to the exposure/treatment. This variable shall have only two modalities encoded 0 for the untreated/unexposed patients and 1 for the treated/exposed ones. The default value is NULL: no specific exposure/treatment is considered. When a specific exposure/treatment is considered, it will be forced in the algorithm or related interactions will be tested when possible.

cov.quanti

The name(s) of the variable(s) related to the possible quantitative covariates. These variables must be numeric.

cov.quali

The name(s) of the variable(s) related to the possible qualitative covariates. These variables must be numeric with two levels: 0 and 1. A complete disjunctive form must be used for covariates with more levels.

data

A data frame for training the model in which to look for the variables related to the status of the follow-up time (times), the event (failures), the optional treatment/exposure (group) and the covariables included in the previous model (cov.quanti and cov.quali).

alpha

The value of the regularization parameter alpha for penalizing the partial likelihood.

lambda

The value of the regularization parameter lambda for penalizing the partial likelihood.

Details

The elastic net Cox regression is obtained by using the glmnet package.

Value

model

The estimated model.

group

The name of the variable related to the exposure/treatment.

cov.quanti

The name(s) of the variable(s) related to the possible quantitative covariates.

cov.quali

The name(s) of the variable(s) related to the possible qualitative covariates.

data

The data frame used for learning. The first column is entitled times and corresponds to the observed follow-up times. The second column is entitled failures and corresponds to the event indicators. The other columns correspond to the predictors.

times

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

hazard

A vector of numeric values with the values of the cumulative baseline hazard function at the prediction times.

predictions

A matrix with the predictions of survivals of each subject (lines) for each observed time (columns).

References

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, https://www.jstatsoft.org/v39/i05/

Examples

data(dataDIVAT2)

# The estimation of the model
model <- LIB_COXen(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), lambda=.1, alpha=.1)

# The predicted survival of the first subject of the training sample
plot(y=model$predictions[1,], x=model$times, xlab="Time (years)",
ylab="Predicted survival", col=1, type="l", lty=1, lwd=2, ylim=c(0,1))