Comprehensive toolkit for developing clinical nomograms, risk score calculators, and decision support tools. Transforms statistical models into practical clinical instruments for personalized risk assessment and treatment planning.
Usage
clinicalnomograms(
data,
time_var,
status_var,
outcome_var,
covariates,
nomogram_type = "survival_nomogram",
model_selection = "all_variables",
prediction_times = "1,3,5,10",
confidence_level = 0.95,
validation_method = "bootstrap",
bootstrap_samples = 1000,
cv_folds = 10,
nomogram_title = "Clinical Risk Assessment Nomogram",
points_scale = 100,
calibration_assessment = TRUE,
discrimination_assessment = TRUE,
decision_curve_analysis = TRUE,
risk_groups = TRUE,
risk_thresholds = "0.33,0.67",
interactive_nomogram = TRUE,
risk_calculator = TRUE,
clinical_scenarios = TRUE,
model_equation = TRUE,
confidence_intervals = TRUE,
external_validation = "",
performance_metrics = TRUE,
variable_importance = TRUE,
sensitivity_analysis = FALSE,
missing_data_handling = "complete_case",
export_formats = TRUE,
reporting_guidelines = "tripod",
clinical_implementation = TRUE
)Arguments
- data
The data as a data frame.
- time_var
Time to event or censoring
- status_var
Event indicator variable
- outcome_var
Binary or continuous outcome
- covariates
Predictor variables for model building
- nomogram_type
Nomogram model specification
- model_selection
Variable selection approach
- prediction_times
Times for survival probability estimation
- confidence_level
CI level for nomogram
- validation_method
Validation approach
- bootstrap_samples
Bootstrap iteration count
- cv_folds
CV fold specification
- nomogram_title
Nomogram plot title
- points_scale
Point scale range
- calibration_assessment
Enable calibration evaluation
- discrimination_assessment
Enable discrimination analysis
- decision_curve_analysis
Enable DCA analysis
- risk_groups
Enable risk stratification
- risk_thresholds
Risk stratification cutpoints
- interactive_nomogram
Enable interactive visualization
- risk_calculator
Create lookup table
- clinical_scenarios
Generate clinical examples
- model_equation
Display regression equation
- confidence_intervals
Display prediction intervals
- external_validation
External validation data source
- performance_metrics
Enable performance assessment
- variable_importance
Calculate variable contributions
- sensitivity_analysis
Enable sensitivity testing
- missing_data_handling
Missing data strategy
- export_formats
Enable multi-format export
- reporting_guidelines
Structured reporting standard
- clinical_implementation
Create implementation recommendations
Value
A results object containing:
results$instructions | Analysis instructions and overview | ||||
results$modelSummary | Summary of the fitted model for nomogram | ||||
results$variableSelection | Results of variable selection process | ||||
results$modelPerformance | Model performance metrics | ||||
results$nomogramEquation | Mathematical equation underlying the nomogram | ||||
results$riskCalculatorTable | Reference table for risk calculation | ||||
results$riskGroupAnalysis | Risk stratification analysis | ||||
results$calibrationResults | Model calibration analysis | ||||
results$discriminationResults | Model discrimination analysis | ||||
results$decisionCurveResults | Clinical utility assessment | ||||
results$variableImportance | Relative importance of predictor variables | ||||
results$clinicalScenarios | Example clinical scenarios with risk calculations | ||||
results$nomogramPlot | Visual nomogram for risk assessment | ||||
results$calibrationPlot | Calibration assessment visualization | ||||
results$discriminationPlot | ROC curves and discrimination assessment | ||||
results$decisionCurvePlot | Clinical utility visualization | ||||
results$riskGroupPlot | Survival curves by risk groups | ||||
results$variableImportancePlot | Variable importance visualization | ||||
results$interactiveNomogram | Interactive web-based nomogram calculator | ||||
results$validationReport | Comprehensive model validation report | ||||
results$implementationGuide | Guidance for clinical implementation | ||||
results$reportingChecklist | Structured reporting checklist | ||||
results$diagnostics | Diagnostic information and warnings |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$variableSelection$asDF
as.data.frame(results$variableSelection)
Examples
# \donttest{
# Example: Cox model nomogram
clinicalnomograms(
data = clinical_data,
time = survival_months,
status = death_event,
covariates = c("age", "stage", "grade"),
nomogram_type = "survival_nomogram"
)
# }