Screening Program Evaluation & Performance Analysis — screeningevaluation • ClinicoPath

Comprehensive evaluation of screening programs and diagnostic test performance from tabular screening data. Analyzes screening outcomes, program effectiveness, population coverage, diagnostic accuracy, and cost-effectiveness. Essential for public health screening program assessment, quality improvement, and evidence-based screening policy development using standardized epidemiological methods and performance indicators.

Usage

screeningevaluation(
  data,
  screening_result,
  disease_status,
  participant_id,
  age_var,
  sex_var,
  screening_date,
  screening_round,
  risk_factors,
  location_var,
  screening_type = "population_based",
  target_disease = "cancer",
  screening_test_type = "laboratory",
  diagnostic_accuracy = TRUE,
  program_coverage = TRUE,
  detection_rates = TRUE,
  interval_cancer_analysis = FALSE,
  lead_time_analysis = FALSE,
  length_bias_analysis = FALSE,
  age_stratified_analysis = TRUE,
  age_group_breaks = "40,50,60,70,80",
  risk_stratified_analysis = FALSE,
  geographic_analysis = FALSE,
  calculate_predictive_values = TRUE,
  likelihood_ratios = TRUE,
  roc_analysis = FALSE,
  optimal_cutpoint = FALSE,
  quality_indicators = TRUE,
  participation_rates = TRUE,
  recall_rates = TRUE,
  completion_rates = TRUE,
  cost_effectiveness = FALSE,
  cost_per_case_detected = FALSE,
  screening_cost_var,
  followup_cost_var,
  time_to_diagnosis = FALSE,
  diagnosis_date_var,
  survival_analysis = FALSE,
  survival_time_var,
  death_indicator,
  overdiagnosis_analysis = FALSE,
  false_positive_impact = FALSE,
  screening_adherence = FALSE,
  performance_plots = TRUE,
  coverage_plots = TRUE,
  trend_analysis_plots = FALSE,
  comprehensive_report = TRUE,
  quality_assurance_report = TRUE,
  public_health_indicators = TRUE,
  international_standards = TRUE,
  export_for_registry = FALSE
)

Arguments

data: the data as a data frame
screening_result: Binary screening test result (positive/negative)
disease_status: True disease status from gold standard or follow-up
participant_id: Unique identifier for screening participants
age_var: Age of screening participants
sex_var: Sex/gender of screening participants
screening_date: Date of screening test
screening_round: Screening round number (for repeated screening)
risk_factors: Risk factor variables for stratified analysis
location_var: Geographic location or screening site
screening_type: Type of screening program
target_disease: Target disease or condition being screened
screening_test_type: Type of screening test used
diagnostic_accuracy: Calculate diagnostic accuracy measures from tabular data
program_coverage: Analyze screening program coverage and participation
detection_rates: Calculate disease detection rates by subgroups
interval_cancer_analysis: Analyze interval cancers (for cancer screening programs)
lead_time_analysis: Estimate lead time and lead time bias
length_bias_analysis: Assess potential length bias in screening
age_stratified_analysis: Perform analysis stratified by age groups
age_group_breaks: Age boundaries for stratified analysis (comma-separated)
risk_stratified_analysis: Perform analysis stratified by risk factors
geographic_analysis: Analyze screening performance by geographic location
calculate_predictive_values: Calculate positive and negative predictive values
likelihood_ratios: Calculate likelihood ratios and post-test probabilities
roc_analysis: Perform ROC curve analysis (for continuous test results)
optimal_cutpoint: Determine optimal cutpoint for continuous screening tests
quality_indicators: Calculate standard screening quality indicators
participation_rates: Analyze participation rates and demographic factors
recall_rates: Calculate recall rates for further investigation
completion_rates: Analyze completion of recommended follow-up
cost_effectiveness: Basic cost-effectiveness analysis
cost_per_case_detected: Calculate cost per case detected
screening_cost_var: Variable containing screening costs per individual
followup_cost_var: Variable containing follow-up diagnostic costs
time_to_diagnosis: Analyze time from screening to diagnosis
diagnosis_date_var: Date of disease diagnosis
survival_analysis: Compare survival between screen-detected and symptomatic cases
survival_time_var: Time to death or last follow-up
death_indicator: Death indicator (0=alive, 1=dead)
overdiagnosis_analysis: Assess potential overdiagnosis in screening program
false_positive_impact: Analyze impact of false positive results
screening_adherence: Analyze adherence to screening recommendations
performance_plots: Generate diagnostic performance plots
coverage_plots: Generate coverage and participation plots
trend_analysis_plots: Generate time trend analysis plots
comprehensive_report: Generate comprehensive screening evaluation report
quality_assurance_report: Generate quality assurance and improvement report
public_health_indicators: Generate public health screening indicators report
international_standards: Assess compliance with international screening standards
export_for_registry: Export results in cancer registry compatible format

Value

A results object containing:

`results$screening_overview`					a table
`results$diagnostic_accuracy_summary`					a table
`results$program_coverage`					a table
`results$detection_rates`					a table
`results$age_stratified_results`					a table
`results$quality_indicators`					a table
`results$recall_analysis`					a table
`results$interval_cancer_results`					a table
`results$cost_effectiveness_summary`					a table
`results$geographic_analysis`					a table
`results$time_trends`					a table
`results$overdiagnosis_assessment`					a table
`results$survival_comparison`					a table
`results$adherence_analysis`					a table
`results$diagnostic_performance_plot`					an image
`results$coverage_participation_plot`					an image
`results$detection_rate_plot`					an image
`results$age_stratified_plot`					an image
`results$time_trend_plot`					an image
`results$roc_curve_plot`					an image
`results$cost_effectiveness_plot`					an image
`results$comprehensive_report`					a html
`results$quality_assurance_report`					a html
`results$public_health_report`					a html

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

results$screening_overview$asDF

as.data.frame(results$screening_overview)

Examples

data('screening_data')

screeningevaluation(
    data = screening_data,
    screening_result = "screen_positive",
    disease_status = "true_disease",
    age_var = "age",
    screening_round = "round_number"
)