In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

The meddecide package provides tools to evaluate medical diagnostic tests. This vignette demonstrates the core functions for decision analysis.

Example Data

Small example datasets are included with the package. You can load them using system.file() and read.csv().

df_dec <- read.csv(system.file("extdata", "decision_example.csv", package = "meddecide"))
head(df_dec)

Calculating Diagnostic Metrics

The decision() function computes sensitivity, specificity and related metrics from raw test results.

res <- decision(data = df_dec,
                gold = df_dec$gold,
                goldPositive = 1,
                newtest = df_dec$newtest,
                testPositive = 1,
                ci = TRUE)
res$ratioTable

When you only have the four counts (true positives, false positives, true negatives and false negatives) you can use decisioncalculator() directly.

calc <- decisioncalculator(TP = 90, FN = 10, TN = 80, FP = 20,
                           ci = TRUE, fagan = TRUE)
calc$ratioTable

The option fagan = TRUE adds a Fagan nomogram to illustrate how the pre-test probability is updated by the diagnostic result.

calc$fagan

These tools help summarise diagnostic performance and can be combined with other functions in meddecide for more advanced analysis.

sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.