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(
preset = "custom",
test1_name = "Screening Test",
test1_sens = 0.95,
test1_spec = 0.7,
test1_cost = 0,
test2_name = "Confirmatory Test",
test2_sens = 0.8,
test2_spec = 0.98,
test2_cost = 0,
strategy = "serial_positive",
prevalence = 0.1,
population_size = 1000,
show_explanation = FALSE,
show_formulas = FALSE,
show_cost_analysis = FALSE,
show_nomogram = FALSE
)Arguments
- preset
Select a clinical preset or use custom values. Presets load evidence-based test parameters and optimal strategies from medical literature.
- test1_name
.
- test1_sens
.
- test1_spec
.
- test1_cost
.
- test2_name
.
- test2_sens
.
- test2_spec
.
- test2_cost
.
- strategy
.
- prevalence
.
- population_size
Population size used to illustrate population flow counts. Does not affect probabilities.
- show_explanation
.
- show_formulas
.
- show_cost_analysis
.
- show_nomogram
.
Value
A results object containing:
results$plain_summary | a html | ||||
results$summary_table | a table | ||||
results$individual_tests_table | a table | ||||
results$population_flow_table | a table | ||||
results$cost_analysis_table | a table | ||||
results$explanation_text | a html | ||||
results$formulas_text | a html | ||||
results$plot_flow_diagram | an image | ||||
results$plot_performance | an image | ||||
results$plot_probability | an image | ||||
results$plot_population_flow | an image | ||||
results$plot_sensitivity_analysis | an image | ||||
results$clinical_guidance | a html | ||||
results$notices | a html |
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