Decoding The Data: Contingency Tables- Part 1
Scott Sherrill-Mix, Ph.D., discusses how to interpret contingency tables when evaluating diagnostic tests and associated measures of test accuracy using a real figure example.
Video Notes
Scott Sherrill-Mix, Ph.D., assistant professor in the Department of Microbiology, Genetics and Immunology at Michigan State University provides a guide on:
- How contingency tables are structured.
- How to calculate and interpret sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
- Which calculations are population independent (sensitivity and specificity), and which are population dependent (PPV and NPV).
- Common pitfalls that lead to misleading results when applied to low鈥憄revalence populations.
Definitions mentioned during the video:
- True Positives (TP): correctly detected disease.
- False Positives (FP): healthy individuals incorrectly flagged.
- True Negatives (TN): correctly identified as healthy.
- False Negatives (FN): disease missed by the test.
- Sensitivity: proportion of actual positives correctly identified.
- Specificity: proportion of actual negatives correctly identified; measures test performance.
- Positive predictive value: proportion of predicted positives correctly identified; measures test performance.
- Negative predictive value: proportion of predicted negatives correctly identified.
Continue learning about contingency tables in part 2 of this video series. Sherrill-Mix covers how to calculate PPV when taking a test from 1 population and applying it to another population, and common pitfalls that lead to misleading results when applied to low鈥憄revalence populations.
Author Information
Scott Sherrill-Mix, Ph.D., Assistant Professor, Department of Microbiology, Genetics and Immunology, Michigan State University.
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