Calculates positive and negative predictive values for screening and diagnostic tests using Bayes' theorem. Demonstrates how disease probability changes with sequential testing and provides Fagan nomograms for clinical decision-making.
Usage
screeningcalculator(
sens = 0.9,
spec = 0.8,
prev = 0.1,
repeat2 = TRUE,
repeat3 = TRUE,
fnote = FALSE,
fagan = FALSE
)Value
A results object containing:
results$explanatoryText | a html | ||||
results$mathText | a html | ||||
results$singleTestTable | a table | ||||
results$repeatTest2Table | a table | ||||
results$repeatTest3Table | a table | ||||
results$plot1 | an image | ||||
results$plot2PP | an image | ||||
results$plot2NN | an image | ||||
results$plot3PPP | an image | ||||
results$plot3NNN | an image |
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$singleTestTable$asDF
as.data.frame(results$singleTestTable)
Details
This calculator is particularly useful for: • Understanding screening test performance in different prevalence settings • Sequential testing scenarios (confirmatory testing) • Teaching Bayesian probability concepts in medical decision-making • Evaluating diagnostic test chains in clinical workflows
Examples
# COVID-19 rapid test in community screening (low prevalence)
# Sensitivity: 85\%, Specificity: 95\%, Prevalence: 2\%
data(screening_examples)
covid_community <- screening_examples[1, ]
# Mammography screening in women aged 50-69
# Demonstrates low PPV due to low cancer prevalence
mammo_data <- screening_examples[screening_examples$scenario == "Mammography Screening", ]
# Sequential testing example: HIV screening followed by confirmatory test
# Shows how probability increases dramatically with confirmatory testing
hiv_screening <- screening_examples[screening_examples$scenario == "HIV Testing", ]
# Prevalence effect demonstration
# Shows how identical test performs differently at various prevalence levels
data(prevalence_demo)
# Load example datasets for realistic clinical scenarios
data(screening_examples) # 15 clinical scenarios across specialties
data(prevalence_demo) # Prevalence effect demonstration
data(performance_demo) # Test performance comparisons
data(sequential_demo) # Sequential testing examples
data(common_tests) # Reference test characteristics