Adaptive Lasso Model Selection for High-Dimensional Cox Models

Description

Automatic adaptive lasso model selection for high-dimensional Cox models, evaluated by penalized partial-likelihood.

Usage

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hdcox.alasso(x, y, nfolds = 5L, rule = c("lambda.min", "lambda.1se"),
  seed = c(1001, 1002))

Arguments

x

Data matrix.

y

Response matrix made by Surv.

nfolds

Fold numbers of cross-validation.

rule

Model selection criterion, "lambda.min" or "lambda.1se". See cv.glmnet for details.

seed

Two random seeds for cross-validation fold division in two estimation steps.

Examples

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library("survival")
library("rms")

# Load imputed SMART data
data("smart")
x = as.matrix(smart[, -c(1, 2)])
time = smart$TEVENT
event = smart$EVENT
y = Surv(time, event)

# Fit Cox model by adaptive lasso penalization
alassofit = hdcox.alasso(x, y, nfolds = 3, rule = "lambda.1se", seed = c(7, 11))

# Prepare data for hdnom.nomogram
x.df = as.data.frame(x)
dd = datadist(x.df)
options(datadist = "dd")

# Generate hdnom.nomogram objects and plot nomogram
nom = hdnom.nomogram(alassofit$alasso_model, model.type = "alasso",
                     x, time, event, x.df,
                     lambda = alassofit$alasso_best_lambda, pred.at = 365 * 2,
                     funlabel = "2-Year Overall Survival Probability")

plot(nom)

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