Class Presentation: Core Concepts and Evaluation Metrics of Diagnostic Accuracy

May 21, 2026·
陳映竹
陳映竹
· 2 min read
Presenter: Ying-Chu Chen
Abstract
In this class presentation, Ph.D. Candidate Steve Ying-Chu Chen discussed an impactful article from the Journal of Clinical Epidemiology. The presentation covered the foundations of diagnostic accuracy, including the distinction between index tests and reference standards, the metric of sensitivity, and how to address covariate problems in diagnostic evaluations to minimize misdiagnosis in clinical applications. / 本次課堂報告由博士候選人陳映竹探討《臨床流行病學雜誌》中關於診斷準確性的文獻。內容涵蓋診斷準確性的基礎觀念（如指標測試與參考標準的差異）、敏感度指標，以及如何處理協變數干擾，以降低臨床誤診率並制定最佳決策邊界。
Date
May 21, 2026 3:10 PM — 3:30 PM
Event
Location

Department of Mathematics, Gongguan Campus, National Taiwan Normal University

88, Section 4, Tingzhou Rd., Taipei City, Wenshan Dist. 116

Presentation Highlights / 報告精華

Introduction & Rationale

In modern clinical practice, distinguishing between disease and non-disease states using biomarkers is a critical challenge. This presentation delves into a Q1 journal article (Impact Factor: 5.2) to explore how optimal decision boundaries are formed and how covariate-specific curves can improve diagnostic accuracy.

Key Insights

  • Diagnostic Foundations: Clearly defining the “Index Test” (the new diagnostic tool being evaluated) versus the “Reference/Gold Standard” (the absolute truth, e.g., HbA1c for Type 2 Diabetes).
  • Evaluation Metrics: Understanding Sensitivity (True Positive Fraction) as a cornerstone metric for ensuring accurate classification and minimizing false negatives.
  • Covariate Challenges: Addressing how population differences (covariates) impact the positive thresholds, and utilizing D-dimer for Venous Thromboembolism (VTE) as a real-world clinical application scenario.

Practical Application

Accurately assessing these diagnostic metrics prevents unnecessary invasive procedures and optimizes clinical decision-making when evaluating new assessment tools.

研究動機與背景

在現代臨床實務中，利用生物標記將病患正確分類為「患病」或「未患病」是一大挑戰。本次報告深入探討一篇影響因子 5.2 的 Q1 期刊文獻，解析如何透過特定協變數曲線與指標，來容納群體間的差異並制定最佳決策邊界。

核心發現

  • 診斷準確性基礎：釐清「指標測試（Index Test，即受評估的新型診斷工具）」與「參考標準（Reference Standard，即絕對真相，如檢測糖尿病的糖化血色素）」的差異。
  • 核心評估指標：探討「敏感度（Sensitivity，真陽性率）」作為關鍵指標的數學邏輯，確保檢測能正確超過陽性閾值。
  • 協變數的臨床應用：探討協變數干擾問題，並以 D-dimer（D-雙聚體）檢測靜脈血栓栓塞（VTE）作為實際的臨床應用案例進行分析。

實務建議

精準評估檢測工具的準確度與閾值，能有效避免病患接受不必要的侵入性醫療程序，將臨床誤診的機率降到最低。

報告紀錄與影音 (Presentation Recording)

(If the video does not display properly, you can watch it directly on YouTube.)

報告資訊 / Presentation Info

  • Presenter / 報告人: Ying-Chu Chen (陳映竹), Ph.D. Candidate
  • Instructor / 授課教師: Lu, Tsui-Shan, Ph.D.
  • Course / 課程: Categorical Data Analysis (類別資料分析)
  • Location / 地點: Department of Mathematics, Gongguan Campus, NTNU (師大公館校區數學系)
  • Lab / 實驗室: Physical Activity & Cognitive Neuroscience Lab (PACNL), NTNU
Categorical Data Analysis Diagnostic Accuracy Biomarkers Medical Statistics Covariates
陳映竹
Authors
陳映竹
Ph.D. Candidate | M.S. & M.A., CSCS*D, ACSM CEP & EP-C, CPT