Lasso-Cox Regression — lassocox • ClinicoPath

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

Usage

lassocox(
  data,
  elapsedtime,
  outcome,
  outcomeLevel,
  censorLevel,
  explanatory,
  lambda = "lambda.1se",
  nfolds = 10,
  random_seed = 123456,
  standardize = TRUE,
  suitabilityCheck = TRUE,
  cv_plot = TRUE,
  coef_plot = TRUE,
  survival_plot = TRUE,
  showSummary = FALSE,
  showExplanations = FALSE,
  showMethodologyNotes = FALSE,
  includeClinicalGuidance = FALSE,
  showVariableImportance = FALSE,
  showModelComparison = FALSE
)

Arguments

data: The data as a data frame.
elapsedtime: The numeric variable representing follow-up time until the event or last observation.
outcome: Binary event indicator variable (event vs censored). Can be factor or numeric with exactly two observed values.
outcomeLevel: Level of outcome considered as the event. For binary factor outcomes, if left empty the second observed level is used; for numeric binary outcomes, the larger observed value is used (or 1 for 0/1 coding).
censorLevel: Level of outcome considered as censored (no event). Together with outcomeLevel, this defines a strict two-level encoding: rows whose outcome matches neither level are treated as missing and excluded.
explanatory: Variables to be considered for selection in the Lasso-Cox regression. Constant variables are removed automatically before fitting.
lambda: Method for selecting the optimal lambda parameter from cross-validation.
nfolds: Number of folds for cross-validation. Fold count is reduced automatically when sample size is limited.
random_seed: Random seed for reproducible cross-validation fold assignment.
standardize: Whether to standardize predictor variables before fitting. If enabled, reported coefficients are on the standardized predictor scale.
suitabilityCheck: Run a comprehensive data suitability assessment before LASSO analysis. Checks sample size, events-per-variable ratio, multicollinearity, and whether regularization is needed.
cv_plot: Whether to show the cross-validation plot.
coef_plot: Whether to show a bar plot of selected-variable coefficients at the chosen lambda value.
survival_plot: Whether to show survival curves by risk groups. Uses survminer if available, with a base-R fallback otherwise.
showSummary: Display a natural-language summary paragraph of the main results, suitable for copying into reports or manuscripts.
showExplanations: Display detailed explanations of LASSO Cox regression methodology, including regularization concepts and interpretation guidance.
showMethodologyNotes: Show comprehensive technical notes about LASSO regularization, cross-validation, and variable selection process.
includeClinicalGuidance: Include guidance for clinical interpretation of LASSO Cox regression results, risk scores, and variable selection outcomes.
showVariableImportance: Display analysis of variable importance rankings and selection patterns across different lambda values.
showModelComparison: Compare post-LASSO Cox refit (selected variables) with standard Cox using all encoded predictors. Intended as an exploratory comparison.

Value

A results object containing:

`results$todo`					a html
`results$suitabilityReport`					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$riskScore`					an output
`results$summaryText`					a html
`results$lassoExplanation`					a html
`results$methodologyNotes`					a html
`results$clinicalGuidance`					a html
`results$variableImportance`					a table
`results$modelComparison`					a table
`results$regularizationPathExplanation`					a html
`results$crossValidationExplanation`					a html
`results$riskScoreExplanation`					a html

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{
lassocox(data = data, elapsedtime = "Time", outcome = "Status",
    outcomeLevel = "1", explanatory = vars(age, stage, grade))
#> Error in lassocox(data = data, elapsedtime = "Time", outcome = "Status",     outcomeLevel = "1", explanatory = vars(age, stage, grade)): argument "censorLevel" is missing, with no default
# }