Lasso-Cox Regression for Variable Selection in Survival Analysis

Performs Lasso-penalized Cox proportional hazards regression for variable selection in survival analysis. This function uses penalized likelihood to identify the most important predictors while preventing overfitting, making it ideal for high-dimensional survival data where the number of potential predictors may approach or exceed the sample size.

Details

The Lasso-Cox regression combines the Cox proportional hazards model with L1 regularization (Lasso penalty) to perform automatic variable selection. The method minimizes the partial likelihood penalized by the L1 norm of the coefficient vector, effectively shrinking less important coefficients toward zero and setting some exactly to zero.

Key features:

• Automatic variable selection through L1 regularization

• Cross-validation for optimal tuning parameter selection

• Risk score calculation and stratification

• Comprehensive model performance evaluation

• Survival curve visualization by risk groups

The function uses the glmnet package for efficient computation and supports both lambda.min (minimum cross-validation error) and lambda.1se (1 standard error rule) for tuning parameter selection.

Super classes

jmvcore::Analysis -> ClinicoPath::lassocoxBase -> lassocoxClass

Methods

Public methods

• lassocoxClass$clone()

Inherited methods

• jmvcore::Analysis$.createImage()
• jmvcore::Analysis$.createImages()
• jmvcore::Analysis$.createPlotObject()
• jmvcore::Analysis$.load()
• jmvcore::Analysis$.render()
• jmvcore::Analysis$.save()
• jmvcore::Analysis$.savePart()
• jmvcore::Analysis$.setCheckpoint()
• jmvcore::Analysis$.setParent()
• jmvcore::Analysis$.setReadDatasetHeaderSource()
• jmvcore::Analysis$.setReadDatasetSource()
• jmvcore::Analysis$.setResourcesPathSource()
• jmvcore::Analysis$.setStatePathSource()
• jmvcore::Analysis$addAddon()
• jmvcore::Analysis$asProtoBuf()
• jmvcore::Analysis$asSource()
• jmvcore::Analysis$check()
• jmvcore::Analysis$init()
• jmvcore::Analysis$optionsChangedHandler()
• jmvcore::Analysis$postInit()
• jmvcore::Analysis$print()
• jmvcore::Analysis$readDataset()
• jmvcore::Analysis$run()
• jmvcore::Analysis$serialize()
• jmvcore::Analysis$setError()
• jmvcore::Analysis$setStatus()
• jmvcore::Analysis$translate()
• ClinicoPath::lassocoxBase$initialize()

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Method clone()

The objects of this class are cloneable with this method.

Usage

lassocoxClass$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Basic Lasso-Cox regression
result <- lassocox(
  data = survival_data,
  elapsedtime = "time",
  outcome = "status", 
  outcomeLevel = "1",
  explanatory = c("age", "gender", "stage", "grade"),
  lambda = "lambda.1se",
  nfolds = 10
)

# High-dimensional scenario
result <- lassocox(
  data = genomic_data,
  elapsedtime = "survival_time",
  outcome = "event",
  outcomeLevel = "death",
  explanatory = gene_variables,
  lambda = "lambda.min",
  nfolds = 5,
  standardize = TRUE
)
} # }