Interpreting the Medical Literature

1. Evidence-based medicine: benefits and risks - 7/9

2. Introduction, Skepticism, and Study Design - 7/23

3. Selection bias, Confounding variables, and Outcome measures - 7/30

4. Observer agreement, Kappa, Drop-outs, and Hawthorne effect - 8/6

5. Types of data, Measures of the middle, and the Normal distribution - 8/13

6. Measures of dispersion, spread, or variability - 8/20

7. Relationship between two variables: correlation, regression, risk/odds ratios - 9/3

8. Survival analysis: Kaplan-Meier, Hazard function/rate/ratio - 9/10

9. Statistical inference: Estimation, Confidence intervals - 9/17

10. Hypothesis testing: Tailed, Variable types, Alpha, P-value - 9/24

11. Type I and II errors: Multiple comparisons, Beta, Power - 10/8

12. P-value fallacy: Bayesian-analysis of P-values -

13. Adjustment for confounding, Multiple variable analysis, Meta-analysis -

14. Cause and effect: Contributory cause, Koch's postulates -

15. Extrapolation: J-curves, Ecologic fallacy -

16. Prevention and screening: Incidence/prevalence, Lead-time/Length-time, Relative risk, Number-needed-to treat/harm -