Lecture Notes - Common Questions in Medical Studies

Overview

• Focus: Understanding commonly asked questions regarding medical studies, test sensitivity, specificity, and case study types.

Questions & Concepts

1. Diagnosing UTIs in Women

• Scenario: A new test compared against gold standards (urine dipstick + culture).
• Key Formulas:
  • Specificity: True Negatives / (True Negatives + False Positives)
  • Sensitivity: True Positives / (True Positives + False Negatives)
  • Positive Predictive Value (PPV) and Negative Predictive Value (NPV)
• Example Calculation:
  • Specificity = 180 / (180 + 20) = 90%

2. Study Types - Cohort vs. Case-Control

• Case-Control Study: Compares individuals with disease vs. those without (initially differing states)
• Cohort Study: Compares individuals based on exposure status (initially no disease)
• Key Actions:
  • Relative risk with cohorts
  • Odds ratio with case-control studies

3. Prostate Cancer Diagnosis Study

• Sensitivity of Test: 70%, Specificity: 90%
• Calculating False Negatives:
  • If 100 patients have UTI, 30 false negatives (100 - 70 = 30)

4. Identifying Study Designs for Test Scores

• Scenario: Comparing older vs. younger students' test scores.
• Outcome Knowledge: If outcomes known beforehand, it's a case-control study.
• Odds Ratio Calculation: Odds of lower scores for older students compared to younger.

5. Biomarker Detection

• Changing Cutoff Points: Affects sensitivity and predictive values.
  • Moving cutoff affects true/false negatives/positives.
  • Key Concept: Lower cutoff increases sensitivity but may decrease specificity.

6. Sensitivity and Specificity in Epidemiology

• Sensitivity: True positives / (True positives + False negatives)
• Specificity: True negatives / (True negatives + False positives)
• Modifications to curve: Understanding how cutoffs impact sensitivity and specificity.

7. Prevention Types

• Primary Prevention: Immunizations, health education before disease occurs.
• Secondary Prevention: Screening tests (e.g. checking blood pressure) to identify diseases early.

8. Case Fatality Rate

• Calculation for Falls:
  • Fatal cases / Total falls = 4 / 20 for falls.

9. Instrument Accuracy vs. Precision

• Accuracy: Close to gold standard.
• Precision: Consistent results, regardless of accuracy.
• Example: Instrument readings averaging 70 when gold standard is 40: precise but not accurate.

10. p-Value Interpretation

• Defined: Probability that the observed result occurred by chance.
• Threshold for Study Validity: p <= 0.05 is generally acceptable.

11. Understanding Power of the Study

• Power = 1 - Beta (probability of type II error)

12. Prevalence and Risk Ratios

• Relative Risk: Used to compare the incidence of health outcomes in different groups.

13. Correlation in Scatter Diagrams

• Negative Association: Higher alcohol consumption correlates with lower test scores.
• Slope Understanding: Steeper slopes indicate stronger correlations.

14. Standard Deviation Applications

• Calculating Expected Numbers: Understanding distribution based on standard deviations (68-95-99.7 rule).
• Cholesterol Study Example: Expected patients with levels above the threshold based on statistical principles.

Final Notes

• Go through the concepts quickly for better retention.
• Familiarize with calculations and understand their implications for the exams!