Principal Component Cox Models — pcacox • ClinicoPath

Principal Component Analysis for Cox regression with high-dimensional predictors. Reduces dimensionality while preserving survival-relevant information using supervised PCA.

Usage

pcacox(
  data,
  time,
  status,
  predictors,
  clinical_vars,
  pca_method = "supervised",
  n_components = 5,
  component_selection = "cv",
  cv_folds = 10,
  variance_threshold = 0.8,
  scaling = TRUE,
  centering = TRUE,
  survival_weighting = TRUE,
  permutation_test = FALSE,
  n_permutations = 100,
  bootstrap_validation = TRUE,
  n_bootstrap = 100,
  plot_scree = TRUE,
  plot_loadings = TRUE,
  plot_biplot = TRUE,
  plot_survival = TRUE,
  risk_score = TRUE,
  pathway_analysis = FALSE,
  feature_importance = TRUE
)

Arguments

data: The data as a data frame.
time: Survival time variable
status: Event status variable
predictors: Variables for principal component analysis
clinical_vars: Clinical predictors to retain in model
pca_method: PCA methodology for dimensionality reduction
n_components: Number of PCs to include in Cox model
component_selection: Selection criteria for PCs
cv_folds: K-fold CV parameter
variance_threshold: Proportion of variance threshold
scaling: Center and scale variables
centering: Center variables to zero mean
survival_weighting: Use survival information in PCA
permutation_test: Test component significance via permutation
n_permutations: Permutation sample size
bootstrap_validation: Assess model stability via bootstrap
n_bootstrap: Bootstrap sample size
plot_scree: Generate scree plot
plot_loadings: Generate loading visualizations
plot_biplot: Generate biplot visualization
plot_survival: Generate survival stratification plots
risk_score: Calculate combined risk score
pathway_analysis: Analyze biological pathways
feature_importance: Rank features by contribution

Value

A results object containing:

`results$todo`					a html
`results$summary`					a html
`results$pcaSummary`					a table
`results$coxResults`					a table
`results$componentLoadings`					a table
`results$modelPerformance`					a table
`results$featureImportance`					a table
`results$riskScore`					a table
`results$screePlot`					an image
`results$loadingsPlot`					an image
`results$biplot`					an image
`results$survivalPlot`					an image
`results$crossValidation`					a html
`results$bootstrapValidation`					a html
`results$permutationTest`					a html
`results$pathwayAnalysis`					a html
`results$technicalDetails`					a html
`results$clinicalInterpretation`					a html

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

results$pcaSummary$asDF

as.data.frame(results$pcaSummary)

Examples

# \donttest{
# Example usage
pcacox(
    data = highdim_data,
    time = survival_time,
    status = event_status,
    predictors = high_dimensional_variables,
    n_components = 5
)
# }