Diagnostic Test Evaluation
Page 1: Presentation Introduction
Title: Diagnostic Test Evaluation
Presented by: Talha Rafiq
Course: HTH SCI 2G03
Page 2: Diagnostic Test Validity
Definition: The ability of a test to accurately identify diseased and non-disease individuals.
Page 3: Evaluating Test Validity
Proposed Questions for Evaluation:
How many people are correctly diagnosed with the disease?
How many people are correctly diagnosed without the disease?
How many people are misdiagnosed with the disease but do not have it?
How many people are misdiagnosed as no disease but have it?
Page 4: Aspects of Test Validity
Validity evaluated through:
Sensitivity: Proportion of people who have the disorder and test positive.
Specificity: Proportion of people who do not have the disorder and test negative.
False Positive Rate: Proportion of people who do not have the disorder but test positive.
False Negative Rate: Proportion of people who have the disorder but test negative.
Page 5: Fill in the Blanks Activity
Fill in the blanks regarding test results based on the presence or absence of disease.
Page 6: True Positive Definition
True Positive: Those who test “positive” and ARE cases.
Page 7: True Negative Definition
True Negative: Those who test “negative” and are NOT cases.
Page 8: False Positive Definition
False Positive: Those who test “positive” but ARE NOT cases.
Page 9: False Negative Definition
False Negative: Those who test “negative” but ARE cases.
Page 10: Understanding Sensitivity and Specificity
Sensitivity: Ability of the test to correctly identify who has the disease (denominator: all people with disease).
Specificity: Ability of the test to correctly identify who does NOT have the disease (denominator: all people without disease).
Page 11: Formula for Sensitivity
Formula: Sensitivity = True Positives / (True Positives + False Negatives)
Page 12: Formula for Specificity
Formula: Specificity = True Negatives / (False Positives + True Negatives)
Page 13: Test Data
Data Overview:
Disease Present (Yes/No): 10 / 40
Disease Absent (Yes/No): 5 / 45
Page 14: Calculation of Sensitivity and Specificity
Calculated Sensitivity: 67% (10 / (10 + 5))
Calculated Specificity: 53% (45 / (40 + 45))
Page 15: Interpretation of Sensitivity and Specificity
Question raised: Is 53% specificity and 67% sensitivity good or bad?
Page 16: Further Exploration
Further discussion on interpreting test values.
Source: U. Penn Law Review, 146 (173).
Link: https://scholarship.law.upenn.edu
Page 17: Decision Tolerance
Decision points regarding tolerance for:
False positives:
Tolerate or not tolerate?
False negatives:
Tolerate or not tolerate?
Page 18: Costs of Misdiagnosis
Costs of False Positives (low specificity):
Emotional distress of being told one is sick.
Financial costs due to re-testing or needless treatment.
Costs of False Negatives (low sensitivity):
Untreated sick individuals potentially leading to severe consequences.
Page 19: ROC Curve
Definition: Receiver Operating Characteristic (ROC) Curve, useful for comparing screening or diagnostic tests.
Page 20: Comparing Test Performance
Example of ROC with varying True Positive (TP) and False Positive (FP) rates, indicating better performance tests.
Page 21: ROC Curve Analysis
ROC Curve insights on test performance—less false positives and more true positives are desired.
Page 22: Visual Representation of ROC Curves
Graphical analysis indicating accuracy levels from worthless to excellent based on false positive rate.
Page 23: Predictive Values
Positive Predictive Value: Probability the disease is present when the test is positive (denominator: positive tests).
Negative Predictive Value: Probability the disease is not present when the test is negative (denominator: negative tests).
Page 24: Calculation of Positive Predictive Value (PPV)
Formula: PPV = True Positives / (True Positives + False Positives)
Page 25: Calculation of Negative Predictive Value (NPV)
Formula: NPV = True Negatives / (False Negatives + True Negatives)
Page 26: Example Calculations for PPV and NPV
PPV Calculation: 10 / (10 + 40) = 20%
NPV Calculation: 45 / (5 + 45) = 90%
Page 27: Interpretation of PPV and NPV
PPV Insight: "Understood as the actual chance of having the disease after a positive result—20% chance."
NPV Insight: Likely not having the disease after a negative result—90% chance.
Page 28: Down’s Syndrome Study Overview
Summary of a study reporting instances of Down's Syndrome among fetuses using sensitivity, specificity, PPV, and NPV methods.
Page 29: Results of Down’s Syndrome Study
Probability insights on test results for affected versus unaffected fetuses based on previous findings on sensitivity, specificity, PPV, and NPV.
Page 30: COVID-19 Example
Summary of a COVID-19 testing scenario:
Pre-test probability: 5.0%
Test sensitivity: 91.4%
Test specificity: 98.7%
Implications of results: The balance of true and false test results and resulting behavioral decisions.
Page 31: Summary of Key Concepts
Sensitivity: Ability to correctly identify who has the disease.
Specificity: Ability to correctly identify who does NOT have the disease.
ROC Curves: Useful for comparing the validity of diagnostic tests.