Co-Testing Analysis

Function for analyzing combined results of two concurrent diagnostic tests. Calculates post-test probabilities based on various scenarios (either test positive, both positive, both negative).

Usage

cotest(
  test1_sens = 0.8,
  test1_spec = 0.9,
  test2_sens = 0.75,
  test2_spec = 0.95,
  indep = TRUE,
  cond_dep_pos = 0.05,
  cond_dep_neg = 0.05,
  prevalence = 0.1,
  fnote = FALSE,
  fagan = FALSE,
  preset = "custom"
)

Arguments

test1_sens

Sensitivity (true positive rate) of Test 1.

test1_spec

Specificity (true negative rate) of Test 1.

test2_sens

Sensitivity (true positive rate) of Test 2.

test2_spec

Specificity (true negative rate) of Test 2.

indep

Assume tests are conditionally independent (default is true).

cond_dep_pos

Conditional dependence between tests for subjects with disease. Value between 0 (independence) and 1 (complete dependence).

cond_dep_neg

Conditional dependence between tests for subjects without disease. Value between 0 (independence) and 1 (complete dependence).

prevalence

Prior probability (disease prevalence in the population). Requires a value between 0.001 and 0.999.

fnote

.

fagan

.

preset

Select a clinical preset or use custom values. Presets load evidence-based sensitivity and specificity values from medical literature with appropriate dependence parameters and prevalence estimates.

Value

A results object containing:

results$testParamsTable					a table
results$cotestResultsTable					a table
results$dependenceInfo					a html
results$dependenceExplanation					a html
results$explanation					a html
results$plot1					an image

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

results$testParamsTable$asDF

as.data.frame(results$testParamsTable)

Examples

# Basic co-testing analysis with independent tests
cotest(
    test1_sens = 0.80,
    test1_spec = 0.90,
    test2_sens = 0.75,
    test2_spec = 0.95,
    prevalence = 0.10,
    indep = TRUE,
    fagan = TRUE
)

# Co-testing with dependent tests
cotest(
    test1_sens = 0.85,
    test1_spec = 0.88,
    test2_sens = 0.82,
    test2_spec = 0.92,
    prevalence = 0.05,
    indep = FALSE,
    cond_dep_pos = 0.15,
    cond_dep_neg = 0.10,
    fnote = TRUE
)

# High-stakes screening scenario
cotest(
    test1_sens = 0.95,
    test1_spec = 0.85,
    test2_sens = 0.90,
    test2_spec = 0.90,
    prevalence = 0.02,
    indep = TRUE,
    fagan = TRUE,
    fnote = TRUE
)