diagnostic-accuracylisted

# Diagnostic Accuracy Analysis in R ## Overview Comprehensive diagnostic test accuracy analysis covering ROC curve analysis, optimal cutpoint determination, sensitivity and specificity estimation, likelihood ratios, decision curve analysis, inter-rater reliability measures, and diagnostic meta-analysis. ## Basic Diagnostic Measures ### 2x2 Table Analysis ```r library(epiR) # Create 2x2 table # Format: [TP, FN; FP, TN] diag_table <- matrix(c(85, 15, # Disease+ (TP, FN) 20, 180), # Disease- (FP, TN) nrow = 2, byrow = TRUE, dimnames = list( Test = c("Positive", "Negative"), Disease = c("Present", "Absent") )) # Calculate diagnostic measures results <- epi.tests(as.table(diag_table), method = "exact") print(results) # Extract specific measures results$detail # All measures with CIs # Includes: Se, Sp, PPV, NPV, LR+, LR-, DOR, accuracy, prevalence ``` ### Manual Calculations ```r # From confusion matrix elements TP <- 85; FN <- 15; FP <- 20; TN <- 180 # Sensitivity (True Positive Rate) sensitivity <- TP / (TP + FN) # Specificity (True Negative Rate) specificity <- TN / (TN + FP) # Positive Predictive Value ppv <- TP / (TP + FP) # Negative Predictive Value npv <- TN / (TN + FN) # Positive Likelihood Ratio lr_pos <- sensitivity / (1 - specificity) # Negative Likelihood Ratio lr_neg <- (1 - sensitivity) / specificity # Diagnostic Odds