Leicester Warwick Medical School

1. Leicester Warwick Medical School Health and Disease in PopulationsRevision Paul Burton

2. What you don’t need to know!!!!! • Detailed knowledge of how to fill in a death certificate • Detailed knowledge of census questions • Detailed knowledge of exactly what is in/not in specific information sources • Knowledge of web site addresses • Wording of Helsinki declaration, Hippocratic Oath • Mathematical formulae for error factors

3. Exposed Time Unexposed Cohort Studies Count events and pyrs Count events and pyrs

4. A worked example • 1,000 children followed from birth to age 5 yrs • 300 at least one parent smoked in the home • 700 neither parent smoked in the home • p-ySMOKE = 300  5 pyr = 1,500 p-y • p-yNON-SMOKE = 700  5 pyr = 3,500 p-y

5. A worked example • Smoke exposed, 75 diagnosed asthma • Smoke unexposed, 105 diagnosed asthma • IRSMOKERS = 75/1,500 = 50 per 1,000 p-y • IRNON-SMOKERS = 105/3,500 = 30 per 1,000 p-y

6. A worked example • IRR = 1.667 • e.f. = • e.f. = 1.35 • 95% CI: 1.667÷1.35 to 1.6671.35 • i.e. 1.23 to 2.25

7. Case-control Studies Exposed? Case Time Non-Case (Control) Exposed?

8. Analysis 95%CI: OR  e.f., OR  e.f.

9. Creutzfeld Jacob Disease (CJD) and occupation • Odds ratio = (9×104)/(3×13) = 24 • 95% CI: 24÷4.29, 24×4.29 = (5.59, 103.0)

10. How many controls? • Unlike an IRR, the precision of an OR is affected by the number of healthy people (x and z): • So, it is worth increasing the number of controls - up to a point (typically up to 4-6 times as many controls as there are cases)

11. Multiple levels of exposure

12. Retrospective v prospective? • Confusing terminology: two different issues • (1) Does the analysis look forwards or backwards? • (2) Are the data collected as and when they occur (i.e. prospectively) or from historical review - questionnaire, case-notes or other health records – (i.e. retrospectively). • Cohort analysis always looks forwards in time: • Given exposure status at baseline, how many events occurred over time in how many person years and what is the incidence rate ratio? • Simple case-control analysis is usually expressed as being backwards in time: • Given case-control status now, what is the ratio of the odds of exposure at baseline?

13. Retrospective v prospective? • Confusing terminology: two different issues • (1) Does the analysis look forwards or backwards? • (2) Are the data collected as and when they occur (i.e. prospectively) or from historical review - questionnaire, case-notes or other health records – (i.e. retrospectively). • Conventional cohort study: prospective • Historical cohort study: retrospective • Conventional case-control study: retrospective

14. Comparison of cohort andcase-control studies

15. Statistical inference on a rate ratio • Population 1: d1 cases in P1 person years • Population 2: d2 cases in P2 person years • Rate ratio = d1/P1  d2/P2

16. An example • 80 deaths in 8,000 pyrs (male) • 50 deaths in 10,000 pyrs (female) • RateM= 10 per 1,000 p-y; RateF= 5 per 1,000 p-y • Observed rate ratio (M/F) = 2.0 • 95% CI: [2÷1.43 to 2×1.43] = [1.40 to 2.86]  • Best guess for true rate ratio=2.0, and 95% certain that true rate ratio lies between 1.40 and 2.86. This range does not include 1.00 so able to reject hypothesis of equality (p<0.05)

17. Statistical inference on an SMR • Observe O deaths • Expect E deaths (based on age-specific rates in the standard population and age-specific population sizes in the test population)  • SMR = (O/E)  100

18. For example • On basis of age specific rates in standard population expect 50 deaths in test population. Observe 60. (O=60, E=50)   • SMR = (60/50)×100 = 120 • 95% CI for SMR = 120 ÷/× 1.29 = 93 to 155. CI includes 100 so data consistent with equality of death rate in test and standard populations (p>0.05). But also consistent with e.g. a50% excess so certainly doesn’t prove equality.