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Lessons from Epi Midterm (S05)

Lessons from Epi Midterm (S05). Reflections +. Validity is the Goal. Axiom: There is no such thing as a perfect test (Chap 4) Corollary 1: All test results require interpretation Corollary 2: A test does not have to be perfect to be useful. Why name appears outside only.

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Lessons from Epi Midterm (S05)

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  1. Lessons from Epi Midterm (S05) Reflections +

  2. Validity is the Goal • Axiom: There is no such thing as a perfect test (Chap 4) • Corollary 1: All test results require interpretation • Corollary 2: A test does not have to be perfect to be useful

  3. Why name appears outside only • Blinding increases likelihood grade will not be based on prior belief  increasing validity • Blinding does not remove random error

  4. Q1 • Why was kappa was given? • Goal is interpretation (not arithmetic)

  5. Q2 • Q2 • Prevalence • Incidence proportion (risk) • Incidence rate • Lay people may refer to all these as “rates” • But we know better • Rate  risk when disease is rare • Prevalence  Incidence × Duration

  6. Q3 • Group project has some benefits • But leaves some individuals behind

  7. Q4 • Vital statistics in an open population • No problems in calculations

  8. Question 5 “By How Much?” • Which population has a higher rate? • And “By how much?” • Introduces the concepts in Chapter 8 • Answer on exam showed good intuition

  9. Ways to quantify differences • Relative difference • Absolute difference • “Impact” of exposure

  10. Relative Difference • Relative difference derived by ratios • e.g., “Twice the rate” • Suppose • Group 1 rate is 200 per 100,000 • Group 0 rate is 100 per 100,000 • Let RR represent the relative risk

  11. Absolute Difference • Absolute difference derived by subtraction • Let RD represent the risk difference

  12. Potential Impact • There are two ways to discuss “impact” • Let’s consider the effect of the exposure in exposed cases • Assume difference in risk entirely due to exposure • Population 1 risk would revert to the population 0 risk if exposure was averted • This is the Attributable Fraction in the exposed (AFe) • Illustrative example:

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