In our introduction to epidemiology we explain how an observation of a statistical association between an exposure and a disease may be evidence of causation, or it may have other explanations, such as chance, bias or confounding.
Deciding whether to deduce causation or not is a judgement. But there are yardsticks to help with that judgement. A statistical association observed in an epidemiological study is more likely to be causal if:
- it is strong (the relative risk is reasonably large)
- it is statistically significant
- there is a dose-response relationship - higher exposure seems to produce more disease
- the scope for bias or confounding seems limited
- the evidence comes from more than one study, preferably studies of different design
- there is biological support from laboratory experiments
- it is physically plausible
The Bradford-Hill criteria
One often-quoted attempt to set out these factors was by Sir Austin Bradford Hill. His list was:
- Strength
- Consistency
- Specificity
- Temporal relationship
- Biological gradient
- Plausibility
- Coherence
- Experiment
- Analogy
People sometimes try to use these criteria as a ticklist - tick them all and you've proved causation. But Hill himself was clear these are just a guide and cannot take away the need for an informed judgement in each specific case.
The IARC rules
One organisation which has develooped rules for assessing the strength of evidence and whether it amounts to evidence of causation or not is the International Agency for Research on Cancer. See more on their rules.
