Clinical Prediction Model — predmodel • ClinicoPath

Build and validate clinical prediction models with integrated calibration and discrimination metrics. Supports logistic regression with stepwise/LASSO selection, ROC analysis, calibration plots, and bootstrap validation.

Usage

predmodel(
  data,
  outcome,
  predictors,
  modelSelection = "none",
  validationMethod = "bootstrap",
  nBootstrap = 200,
  nFolds = 10,
  showCalibration = TRUE,
  showDiscrimination = TRUE,
  showRiskGroups = TRUE,
  riskCutoffs = "0.33, 0.67",
  ciLevel = 0.95
)

Arguments

data: The data as a data frame.
outcome: Binary outcome variable (disease/event vs. no disease/no event).
predictors: Continuous or categorical predictor variables for the model.
modelSelection: Method for variable selection.
validationMethod: Internal validation method to assess model performance.
nBootstrap: Number of bootstrap samples for optimism correction.
nFolds: Number of folds for cross-validation.
showCalibration: Display calibration plot and Hosmer-Lemeshow test.
showDiscrimination: Display ROC curve, AUC, and Brier score.
showRiskGroups: Stratify patients into risk groups (low/medium/high).
riskCutoffs: Comma-separated risk probability cutoffs (e.g., "0.25, 0.75").
ciLevel: Confidence level for intervals (0.80-0.99).

Value

A results object containing:

`results$modelSummary`					a html
`results$coefficients`					a table
`results$discriminationMetrics`					a table
`results$calibrationMetrics`					a table
`results$riskStratification`					a table
`results$validationMetrics`					a table
`results$rocPlot`					an image
`results$calibrationPlot`					an image
`results$interpretation`					a html

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

results$coefficients$asDF

as.data.frame(results$coefficients)

Examples

# Example usage:
# data <- your_data
# ClinicoPath::predmodel(
#   data = data,
#   outcome = "disease",
#   predictors = vars(age, biomarker, stage),
#   validation = "bootstrap",
#   calibration = TRUE
# )