1. An example of a study problem Bias or truth?

2. How it all started 1968/69 Invited: 1622 women aged 38, 46, 50, 54 and 60 years Examined: 1462 women (90.1%)

3. Psychiatric follow-up 1992/93: Those born 1908, 1914, 1918 and 1922, i.e. the four oldest age cohorts aged 70, 74, 78 and 84, Interview 526 of the survivors agreed (89.6%) CT scan: 277 women agreed

4. Samples 120 ml blood serum stored at –20°C in 2.5 ml covered polystyrene cups enclosed together in small batches, in firmly tied plastic bags for 28 years. Thawed once for other analyses at 25 y. and restored for two years at ‑ 80°C.

5. CT scan and Lacunar Infarcts (LI) Endpoint: –Lacunar infarcts –White matter lesions

6. Logistic regression

7. Predictors – independent variables Homocysteine was analysed in tertiles using cut points previously calculated for the whole sample.

8. Predictors – independent variables Covariates included age, basic cardiovascular risk factors and influential factors for tHcy. The basic CVD risk factors were systolic blood pressure, diastolic blood pressure, serum cholesterol, serum triglycerides, BMI and smoking.

9. Predictors – independent variables Factors considered to be influential for tHcy were serum B-12, serum creatinine, coffee consumption and dietary folate.

10. Odds ratio for LI according to tHcy 1 st tHcy tertile2 nd tHcy tertile3 rd tHcy tertile Range3.3-9.79.8-12.612.7-41.5 Median 8.3111.214.8 nn=87n=110n=80 Covariate studied LI (n) Age alone11.54(0.54-4.38)3.65(1.34-9.90) Systolic blood pressure*11.50(0.52-4.24)3.60(1.32-9.85) Diastolic blood pressure*11.44(0.50-4.13)3.50(1.28-9.55)

11. Study design Type of study? Possible biases?

12. True or false The data must be representative if we want to generalize the results A homogenous sample makes generalization easier

13. Estimate of OR 0∞ OR = 1

14. Estimation, precision ( | ) Estimate with confidence interval Sample 95% confidence interval: 95% of repeated intervals will contain the true value 0∞

15. Precision and validity Measures of populations –precision - random error - statistics –validity - systematic error - epidemiology True value Estimate Precision Bias

16. True or false It takes 2 to tango It takes 3 chords to play the blues It takes 4 numbers to be an epidemiologist

17. 2 x 2 table Exposure/testDiseaseNo disease positive a b negative c d

18. Odds ratio for the study population; hypothesis – no effect No effect, OR=1

19. Odds ratio for the sample- observed effect OR> 1

20. Odds ratio for the sample OR> 1

21. 2 x 2 table Exposure/testDiseaseNo disease positive a b negative c d

22. Odds ratio for the sample OR> 1 D too large? Those without disease and low tHcy more likely to be included?

23. Odds ratio for the sample OR> 1 a too large? Those with disease and high tHcy more likely to be included?

24. Conclusion 1 If those ill and exposed are more likely to be examined, the effect estimate will be overestimated Likewise, if those unaffected and unexposed are more likely to be examined.

25. Odds ratio for the sample OR> 1 a too small? Those without disease and high tHcy less likely to be included?

26. Conclusion 2 If those ill and exposed are less likely to be examined, the effect estimate will be attenuated

27. Generalization Do the results apply outside the sample? Statistical generalization –Representative sample Biological generalization –Information from outside the study –Homogenous sample