Medical Decision Calculator — decisioncalculator • meddecide

Medical Decision Calculator for diagnostic test evaluation when you have the four key counts: True Positives (TP), False Positives (FP), True Negatives (TN), and False Negatives (FN). Calculates comprehensive diagnostic performance metrics including sensitivity, specificity, positive and negative predictive values, likelihood ratios, and post-test probabilities. Supports confidence interval estimation and Fagan nomogram visualization for clinical decision making.

Usage

decisioncalculator(
  TP = 90,
  TN = 80,
  FP = 30,
  FN = 20,
  pp = FALSE,
  pprob = 0.3,
  fnote = FALSE,
  ci = FALSE,
  fagan = FALSE,
  showWelcome = TRUE,
  showSummary = FALSE,
  showAbout = FALSE,
  showGlossary = FALSE,
  multiplecuts = FALSE,
  cutoff1 = "Conservative",
  tp1 = 85,
  fp1 = 10,
  tn1 = 190,
  fn1 = 15,
  cutoff2 = "Aggressive",
  tp2 = 95,
  fp2 = 25,
  tn2 = 175,
  fn2 = 5
)

Arguments

TP: True Positive count: cases with disease that tested positive.
TN: True Negative count: cases without disease that tested negative.
FP: False Positive count: cases without disease that tested positive.
FN: False Negative count: cases with disease that tested negative.
pp: Boolean selection whether to use known population prevalence.
pprob: Prior probability (disease prevalence in the community). Requires a value between 0.001 and 0.999, default 0.300.
fnote: Boolean selection whether to show detailed explanatory footnotes.
ci: Boolean selection whether to calculate 95 percent confidence intervals.
fagan: Boolean selection whether to generate a Fagan nomogram plot.
showWelcome: Boolean selection whether to show welcome message.
showSummary: Boolean selection whether to show plain-language summary of results.
showAbout: Boolean selection whether to show about and assumptions panels.
showGlossary: Boolean selection whether to show clinical glossary.
multiplecuts: Boolean selection whether to evaluate multiple cut-off scenarios.
cutoff1: Name identifier for cut-off scenario 1.
tp1: .
fp1: .
tn1: .
fn1: .
cutoff2: Name identifier for cut-off scenario 2.
tp2: .
fp2: .
tn2: .
fn2: .

Value

A results object containing:

`results$welcome`					a html
`results$summary`					a html
`results$about`					a html
`results$assumptions`					a html
`results$glossary`					a html
`results$cTable`					a table
`results$nTable`					a table
`results$ratioTable`					a table
`results$advancedMetricsTable`					a table
`results$epirTable_ratio`					a table
`results$epirTable_number`					a table
`results$plot1`					an image
`results$multipleCutoffTable`					a table

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

results$cTable$asDF

as.data.frame(results$cTable)

Examples

# Basic diagnostic test evaluation with known counts
result1 <- decisioncalculator(
  TP = 90,  # True positives
  FN = 10,  # False negatives
  TN = 80,  # True negatives
  FP = 20   # False positives
)

# Include 95 percent confidence intervals
result2 <- decisioncalculator(
  TP = 90, FN = 10, TN = 80, FP = 20,
  ci = TRUE
)

# Complete analysis with Fagan nomogram
result3 <- decisioncalculator(
  TP = 90, FN = 10, TN = 80, FP = 20,
  ci = TRUE, pp = TRUE, pprob = 0.15, fagan = TRUE
)