cox.lasso: Library of the Super Learner for Lasso Cox Regression

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

Library of the Super Learner for Lasso Cox Regression

Description

Fit a Lasso Cox regression for a given value of the regularization parameter.

Usage

cox.lasso(times, failures, group, cov.quanti, cov.quali,
data, 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).
lambda	The value of the regularization parameter lambda for penalizing the partial likelihood.

Details

The Lasso 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 cummulative baseline hazard function at the prediction times.
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]>

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<-cox.lasso(times="times", failures="failures", data=dataDIVAT2,
  cov.quanti=c("age"),  cov.quali=c("hla", "retransplant", "ecd"), lambda=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))

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