tuneCOXen {survivalSL}

Tune Elastic Net Cox Regression

Description

This function finds the optimal lambda and alpha parameters for an elastic net Cox regression.

Usage

tuneCOXen(times, failures, group=NULL, cov.quanti=NULL, cov.quali=NULL,
data, cv=10, parallel=FALSE, 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).

cv

The value of the number of folds. The default value is 10.

parallel

If TRUE, use parallel foreach to fit each fold. The default is FALSE.

alpha

The values of the regularization parameter alpha optimized over.

lambda

The values of the regularization parameter lambda optimized over.

Details

The function runs the cv.glmnet function of the glmnet package.

Value

optimal

The value of lambda that gives the minimum mean cross-validated error.

results

The data frame with the mean cross-validated errors for each lambda values.

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)

tune.model <- tuneCOXen(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), cv=5,
  alpha=seq(.1, 1, by=.1), lambda=seq(.1, 1, by=.1))

tune.model$optimal$lambda # the estimated lambda value

# The estimation of the training modelwith the corresponding lambda value
model <- LIB_COXen(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"),
  alpha=tune.model$optimal$alpha,
  lambda=tune.model$optimal$lambda)

# The resulted 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))