The burden of proof Causality FETP India. Competency to be gained from this lecture Understand and use Doll and Hill causality criteria

The burden of proof

Causality

FETP India

Competency to be gained from this lecture

Understand and use Doll and Hill causality criteria

Key elements

• Historical developments in causal inference

• Classical causality criteria

ObservationsObservations

General theoriesGeneral theoriesP

redict

Pred

ict

Infer

Infer

Deduction

Induction

Logic of scientific reasoning

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

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

Classical causality criteria

1. Strength of the association 2. Dose-response relationship 3. Temporal exposure-outcome sequence4. Consistency between studies5. Biological plausibility 6. Specificity (infectious diseases)



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)

Strength of the associationand 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

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



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

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

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

Exposure to injections and acute hepatitis B, Thiruvananthapuram,

Kerala, India, 1992 Potential risk factors Cases

(N=160)

Controls (N=160)

Odds ratio

95% confidence

interval

No injections with reusable needle

51 120 - -

Single injections with reusable needle

41 25 3.9 2.0-7.3

Multiple injections with reusable needle

29 7 9.8 3.8-26

Chi-square : 42, 2 degrees of freedom, p<0.00001

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

< 7 4 19 Reference

7-13 1 2 2.6

14-20 2 8 1.2

21-27 5 4 5.9

28+ 7 5 6.6

Chi-square for trend: 7.1, p<0.008



Temporal exposure-outcome sequence

• The exposure needs to precede the outcome

• This criteria is:Met in cohort studiesMet in case-control studies with appropriate

referent exposure period Need onset date Need appropriate referent exposure period

Not met in cross sectional studies

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

Prevalent

case Non ill Total

Exposed a b a+b

Non exposed c d c+d

Total a+c b+d a+b+c+d

Analytical cross sectional study: Exposure and outcome are

examined at the same time



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



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



Specificity (Infectious diseases)

• One pathogen causes one disease• Example:

Pneumococcus and pneumonia “Hepatitis” G virus and viral hepatitis

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)

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)

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

Documents

The burden of proof Causality FETP India. Competency to be gained from this lecture Understand and use Doll and Hill causality criteria