Exploring Diagnostic Tests and Epidemiology

Aug 22, 2024

Lecture Notes: Understanding Diagnostic Tests and Epidemiological Studies

Key Concepts

  • Focus on understanding common questions related to diagnostic tests and studies.
  • Remember to visualize data and concepts (e.g. 2x2 tables).

Diagnostic Tests

Test Evaluation Metrics

  • Sensitivity: Ability of a test to correctly identify those with the disease.
  • Specificity: Ability of a test to correctly identify those without the disease.
  • Positive Predictive Value (PPV): Probability that subjects with a positive screening test truly have the disease.
  • Negative Predictive Value (NPV): Probability that subjects with a negative screening test truly do not have the disease.

Example Calculation: UTI Diagnosis

  • Specificity Calculation:
    • Specificity = True Negatives / (True Negatives + False Positives)
    • Given: 180 True Negatives, 20 False Positives:
      • Specificity = 180 / (180 + 20) = 90%
  • Sensitivity Calculation:
    • Given: 40 True Positives, 160 False Negatives:
      • Sensitivity = True Positives / (True Positives + False Negatives) = 40 / (40 + 160) = 20%

Study Types

Cohort vs. Case Control

  • Cohort Study:
    • Both groups start without the disease.
    • Measures relative risk.
  • Case Control Study:
    • One group has the disease, one does not.
    • Measures odds ratio.

Example: Respiratory Disease Study

  • Study of individuals with and without pet dogs for respiratory disease is a cohort study.

Test Specificity and Sensitivity

  • Prostate Cancer Diagnosis:
    • Sensitivity = 70%, Specificity = 90%.
    • From 100 true patients, 30 false negatives can be calculated.

Odds Ratio vs. Relative Risk

  • Odds Ratio Calculation:
    • For case-control studies, compare frequency of exposure among cases and controls.
  • Relative Risk Calculation:
    • For cohort studies, compare incidence rates between groups.

Biomarker and Cutoff Changes

  • Moving cutoffs affects sensitivity and specificity.
  • Lower sensitivity results in a higher false negative count.

Study Power and P-value

  • P-value: Probability that the results were due to chance.
    • A p-value < 0.05 indicates statistical significance.
  • Power of a Study:
    • Power = 1 - beta (probability of Type II error).
    • Higher power indicates a higher likelihood of detecting an effect if there is one.

Standard Deviation and Normal Distribution

  • Understanding the normal distribution is crucial for interpreting data.
  • Standard Deviations:
    • 68% of data within 1 SD, 95% within 2 SDs.
  • For a sample mean of 210 with SD of 15, identify patients above a certain threshold (e.g., cholesterol > 240).

Summary

  • Emphasize understanding and practice with diagnostic tests, study types, and statistical measures.
  • Repeated exposure to questions will enhance retention and understanding for exams.