# Diagnostic Accuracy

> Diagnostic accuracy analysis in R, including sensitivity, specificity, ROC curves, likelihood ratios, and decision curves.

- **Type:** Skill
- **Install:** `agentstack add skill-choxos-biostatagent-diagnostic-accuracy`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [choxos](https://agentstack.voostack.com/s/choxos)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [choxos](https://github.com/choxos)
- **Source:** https://github.com/choxos/BiostatAgent/tree/main/plugins/r-tidy-modeling/skills/diagnostic-accuracy

## Install

```sh
agentstack add skill-choxos-biostatagent-diagnostic-accuracy
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# 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  0.9)
pauc_spec  0.9)
pauc_sens = 0.9
cp_constrained 
  plot(type = "clinical_impact")

# Net benefit with confidence intervals (bootstrap)
dca_boot  Post-test probability:", round(post_prob_pos * 100, 1), "%\n")
  cat("LR-:", lr_neg, "-> Post-test probability:", round(post_prob_neg * 100, 1), "%\n")

  return(list(
    pre_test = pre_test_prob,
    post_test_positive = post_prob_pos,
    post_test_negative = post_prob_neg
  ))
}

# Example usage
fagan_nomogram(pre_test_prob = 0.20, lr_pos = 8, lr_neg = 0.15)
```

## Reporting Diagnostic Study Results

```r
# Create comprehensive diagnostic report
create_diagnostic_report = cutpoint, 1, 0)

  # Confusion matrix
  cm <- table(Predicted = pred_class, Actual = df$disease)
  TP <- cm[2, 2]; FN <- cm[1, 2]
  FP <- cm[2, 1]; TN <- cm[1, 1]

  # Calculate metrics
  metrics <- data.frame(
    Metric = c("AUC", "Cutpoint", "Sensitivity", "Specificity",
               "PPV", "NPV", "LR+", "LR-", "Accuracy", "Youden Index"),
    Value = c(
      round(auc(roc_obj), 3),
      round(cutpoint, 2),
      round(TP / (TP + FN), 3),
      round(TN / (TN + FP), 3),
      round(TP / (TP + FP), 3),
      round(TN / (TN + FN), 3),
      round((TP / (TP + FN)) / (FP / (FP + TN)), 2),
      round((FN / (TP + FN)) / (TN / (FP + TN)), 2),
      round((TP + TN) / (TP + TN + FP + FN), 3),
      round(TP / (TP + FN) + TN / (TN + FP) - 1, 3)
    )
  )

  return(metrics)
}
```

## Key Packages Summary

| Package | Purpose |
|---------|---------|
| pROC | ROC curve analysis and AUC |
| cutpointr | Optimal cutpoint selection |
| OptimalCutpoints | Multiple cutpoint methods |
| dcurves | Decision curve analysis |
| irr | Inter-rater reliability (kappa, ICC) |
| mada | Diagnostic meta-analysis |
| BlandAltmanLeh | Method agreement plots |
| epiR | Diagnostic test evaluation |
| DiagTest3Grp | Three-group ROC analysis |
| caret | Confusion matrix utilities |

## Best Practices

1. **Report multiple metrics**: Sensitivity, specificity, PPV, NPV, and likelihood ratios
2. **Account for prevalence**: PPV/NPV depend heavily on disease prevalence
3. **Use appropriate cutpoint method**: Consider clinical consequences (cost of FN vs FP)
4. **Provide confidence intervals**: Especially for AUC and diagnostic measures
5. **Check calibration**: Predicted probabilities should match observed frequencies
6. **Decision curve analysis**: Evaluates clinical utility across threshold range
7. **Consider spectrum bias**: Ensure representative disease severity range
8. **Report according to STARD**: Standards for Reporting of Diagnostic Accuracy Studies

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [choxos](https://github.com/choxos)
- **Source:** [choxos/BiostatAgent](https://github.com/choxos/BiostatAgent)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-choxos-biostatagent-diagnostic-accuracy
- Seller: https://agentstack.voostack.com/s/choxos
- Browse the marketplace: https://agentstack.voostack.com/browse

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