Lasso-Cox Regression — lassocox • ClinicoPath

Performs Lasso-penalized Cox regression for variable selection in survival analysis.

Usage

lassocox(
  data,
  elapsedtime,
  outcome,
  outcomeLevel,
  explanatory,
  lambda = "lambda.1se",
  nfolds = 10,
  standardize = TRUE,
  cv_plot = TRUE,
  coef_plot = TRUE,
  survival_plot = TRUE
)

Arguments

data: The data as a data frame.
elapsedtime: The numeric variable representing follow-up time until the event or last observation.
outcome: The outcome variable. Typically indicates event status (e.g., death, recurrence).
outcomeLevel: The level of outcome considered as the event.
explanatory: Variables to be considered for selection in the Lasso-Cox regression.
lambda: Method for selecting the optimal lambda parameter from cross-validation.
nfolds: Number of folds for cross-validation.
standardize: Whether to standardize predictor variables before fitting.
cv_plot: Whether to show the cross-validation plot.
coef_plot: Whether to show the coefficient path plot.
survival_plot: Whether to show survival curves by risk groups.

Value

A results object containing:

`results$todo`					a html
`results$modelSummary`					a table
`results$coefficients`					a table
`results$performance`					a table
`results$cv_plot`					an image
`results$coef_plot`					an image
`results$survival_plot`					an image
`results$riskScore`					an output

Tables can be converted to data frames with asDF or as.data.frame. For example:

results$modelSummary$asDF

as.data.frame(results$modelSummary)

Examples

# \donttest{
# example will be added
# }