Analyzes how diagnostic accuracy changes when applying two tests in sequence, comparing three different testing strategies: serial positive (confirmation), serial negative (exclusion), and parallel testing. Provides comprehensive analysis including population flow, cost implications, and Fagan nomograms.
Usage
sequentialtests(
test1_name = "Screening Test",
test1_sens = 0.95,
test1_spec = 0.7,
test2_name = "Confirmatory Test",
test2_sens = 0.8,
test2_spec = 0.98,
strategy = "serial_positive",
prevalence = 0.1,
show_explanation = TRUE,
show_formulas = FALSE,
show_nomogram = FALSE
)Value
A results object containing:
results$summary_table | a table | ||||
results$individual_tests_table | a table | ||||
results$population_flow_table | a table | ||||
results$explanation_text | a html | ||||
results$formulas_text | a html | ||||
results$plot_nomogram | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$summary_table$asDF
as.data.frame(results$summary_table)
Details
This analysis is particularly useful for: • Designing diagnostic protocols and clinical pathways • Optimizing test sequencing for specific clinical contexts • Understanding trade-offs between sensitivity and specificity • Evaluating cost-effectiveness of different testing strategies • Teaching sequential testing concepts and Bayesian probability
Examples
# COVID-19 testing: Rapid antigen followed by RT-PCR confirmation
# Shows how serial positive strategy improves specificity
data(sequential_testing_examples)
covid_example <- sequential_testing_examples[
sequential_testing_examples$scenario == "COVID-19 Testing" &
sequential_testing_examples$clinical_setting == "Community screening", ]
# Cancer screening: Mammography followed by tissue biopsy
# Demonstrates cost-effective screening with definitive diagnosis
breast_cancer <- sequential_testing_examples[
sequential_testing_examples$scenario == "Breast Cancer Screening", ]
# Emergency medicine: Parallel testing for rapid diagnosis
# Shows how parallel strategy maximizes sensitivity
emergency_example <- sequential_testing_examples[
sequential_testing_examples$scenario == "Myocardial Infarction Rule-out", ]
# Strategy comparison across different prevalence settings
data(strategy_comparison)
# Cost-effectiveness analysis for resource planning
data(cost_effectiveness_examples)
# Load example datasets for realistic clinical scenarios
data(sequential_testing_examples) # 15+ clinical scenarios across specialties
data(strategy_comparison) # Strategy performance comparisons
data(cost_effectiveness_examples) # Economic analysis examples
data(teaching_examples) # Educational scenarios
data(common_test_combinations) # Reference test characteristics