1. The burden of proof Causality FETP India

2. Competency to be gained from this lecture Understand and use Doll and Hill causality criteria

3. Key elements Historical developments in causal inference Classical causality criteria

4. Observations General theories Predict Infer Deduction Induction Logic of scientific reasoning

5. History of ideas in causal thinking Rationalism  Based on deductive logic Empiricism  Based on inductive logic Hume’s problem Popper’s solution  Conjecture and refutation

6. Causal inference in epidemiology Deterministic outlook  Henle –Koch postulates  Problems Multifactorial etiology Multiplicity of effects Limited conceptualization Imperfect knowledge about diseases Probabilistic (Stochastic) Hill’s criteria

7. Classical causality criteria 1.Strength of the association 2.Dose-response relationship 3.Temporal exposure-outcome sequence 4.Consistency between studies 5.Biological plausibility 6.Specificity (infectious diseases)

8. Classical causality criteria 1.Strength of the association 2.Dose-response relationship 3.Temporal exposure-outcome sequence 4.Consistency between studies 5.Biological plausibility 6.Specificity (infectious diseases)

9. Strength of the association Strong association are less likely to be caused by  Bias  Confounding Weak associations may be secondary to:  Bias  Confounding  Residual confounding (insufficient adjustment)

10. Strength of the association and power Small studies capture stronger association Large studies capture weaker association Beware of small studies when they do not capture an association  It may be because of a lack of statistical power Beware of large studies when capture a weak association or a small difference  It may be because of a bias

11. Causality controversies Rare for strong effects  Nobody argues that tobacco causes lung cancer More common for weaker effects  Passive smoking  Oral contraceptives and breast cancer  Hepatitis B vaccine and multiple sclerosis  DTP and non-specific mortality increase

12. Classical causality criteria 1.Strength of the association 2.Dose-response relationship 3.Temporal exposure-outcome sequence 4.Consistency between studies 5.Biological plausibility 6.Specificity (infectious diseases)

13. Dose response relationship Cohort study  Increase in the dose of exposure leads to higher incidence of the outcome Case control study  Increase in the dose of exposure is linked to a higher odds ratio

14. Documenting a dose-response relationship Collect good data on exposure  Continuous variables (e.g., Blood pressure in mm Hg)  Categorical variables (e.g., 0, 0-5, 5-10, 10=)  Qualitative variable (e.g., never, rarely, often) Analyse by increasing dose of exposure  Chi-square  Chi-square for trend

15. Testing a dose-response relationship Chi-square for heterogeneity of odds ratio  Tests the null hypothesis that the odds ratio do not differ  No particular conditions needed Chi-square for trend  Tests for a linear trend for the increase of the odds ratios with increased levels of exposure  Requires equal interval exposure categories

16. Exposure to injections and acute hepatitis B, Thiruvananthapuram, Kerala, India, 1992 Potential risk factorsCases (N=160) Controls (N=160) Odds ratio 95% confidence interval No injections with reusable needle 51120-- Single injections with reusable needle 41253.92.0-7.3 Multiple injections with reusable needle 2979.83.8-26 Chi-square : 42, 2 degrees of freedom, p<0.00001

17. Hospital stay in the last two months in Clostridium difficile diarrhea cases and controls, AIDS ward, Paris hospital, France, 1991 Hospital stay in last 2 months Cases (n=19) Controls (n=38) Odds ratio < 7419Reference 7-13122.6 14-20281.2 21-27545.9 28+756.6 Chi-square for trend: 7.1, p<0.008

18. Classical causality criteria 1.Strength of the association 2.Dose-response relationship 3.Temporal exposure-outcome sequence 4.Consistency between studies 5.Biological plausibility 6.Specificity (infectious diseases)

19. Temporal exposure-outcome sequence The exposure needs to precede the outcome This criteria is: Met in cohort studies Met in case-control studies with appropriate referent exposure period  Need onset date  Need appropriate referent exposure period  Not met in cross sectional studies

20. Reasons not to conduct risk factors studies on prevalent cases Date of onset unknown Referent exposure period impossible to determine Lifetime referent exposure period does not address the problem  Exposure could have occurred after onset

21. Prevalent case Non ill Total Exposedaba+b Non exposedcdc+d Totala+cb+da+b+c+d Analytical cross sectional study: Exposure and outcome are examined at the same time

22. Classical causality criteria 1.Strength of the association 2.Dose-response relationship 3.Temporal exposure-outcome sequence 4.Consistency between studies 5.Biological plausibility 6.Specificity (infectious diseases)

23. Consistency between studies If different studies made by different authors, in different settings, using different methods made identical findings, the causal relationship is more likely If findings depend upon authors, settings, and methods, causality may be questioned

24. Classical causality criteria 1.Strength of the association 2.Dose-response relationship 3.Temporal exposure-outcome sequence 4.Consistency between studies 5.Biological plausibility 6.Specificity (infectious diseases)

25. Biological plausibility If the effect may be explained through theoretical rationale and / or reproduced experimentally, causality is more likely If the effect may not be explained through theoretical rationale and / or reproduced experimentally, causality need to be demonstrated

26. Classical causality criteria 1.Strength of the association 2.Dose-response relationship 3.Temporal exposure-outcome sequence 4.Consistency between studies 5.Biological plausibility 6.Specificity (infectious diseases)

27. Specificity (Infectious diseases) One pathogen causes one disease Example: Pneumococcus and pneumonia  “Hepatitis” G virus and viral hepatitis

28. Example (1): Hepatitis B vaccine and multiple sclerosis  Strength of the association  Dose-response relationship Temporal exposure-outcome sequence ?Consistency between studies  Biological plausibility  Specificity (infectious diseases)

29. Example (2): Hepatitis C virus infection and health care injections Strength of the association Dose-response relationship Temporal exposure-outcome sequence Consistency between studies Biological plausibility  Specificity (infectious diseases)

30. Take home messages Epidemiologists can never prove a causal relationship between exposure and disease They can develop and test hypotheses to establish causal relationship beyond reasonable doubt  smoking and lung cancer