Association and causation in epidemiology – half a century since the publication of Bradford Hill’s interpretational guidance
Authors: Richard Wakeford
Journal: Journal of the Royal Society of Medicine (2015)

Austin Bradford Hill was one of the greats in the fields of epidemiology and medical statistics. In the mid-20th century, with another great, Richard Doll, Bradford Hill initiated epidemiological studies that were to be highly influential in revealing the causal link between cigarette smoking and lung cancer. However, this link was not accepted without a battle, and opponents of a direct cause-and-effect interpretation of the epidemiological association included such notables as the eminent statistician Ronald Fisher. The debate spurred Bradford Hill to consider in some depth how the findings of epidemiological studies should be interpreted, and this led to the publication in 1965 in Proceedings of the Royal Society of Medicine of his seminal paper on association and causation. To mark the 50th anniversary of the publication of this landmark paper, it is reproduced in this issue of Journal of the Royal Society of Medicine. Bradford Hill’s 1965 paper is a remarkable one that is full of insights. It proposes nine guidelines (often erroneously referred to as ‘criteria’, which Bradford Hill made clear they were not) against which a statistical association found in an epidemiological study may be judged as to whether a causal interpretation is reasonable or not. The most important of these guidelines are ‘strength’ (a strong association is more likely to be causal than a weak one), ‘consistency’ (an association is observed in different studies, under different circumstances, times and places), ‘biological gradient’ (i.e. doseresponse – the effect should tend to be greater with a higher level of exposure) and ‘temporality’ (the effect follows the potential cause after an appropriate interval). Another guideline, ‘biological plausibility’, increases in importance as fundamental knowledge of disease aetiology accumulates, but such knowledge is clearly not complete, which should introduce a note of caution in placing too much emphasis on this guideline; nonetheless, the direct causal interpretation of certain statistical associations would stretch credibility given what is known today. What is it about epidemiology that demands a detailed examination of the interpretation of its findings such as that conducted by Bradford Hill? With the exception of a few studies that are able to ‘piggyback’ on clinical trials, epidemiology is an observational (i.e. non-experimental) science. This is in contrast to randomised controlled clinical trials, which are experimental set-ups: study subjects are randomised between treatment groups by the investigators so that any background individual differences that might affect the outcome of the trial, even if unknown, are ‘evened out’ between the groups. (Randomisation between treatment groups is a fundamental concept in clinical trials that Bradford Hill was key in establishing as a necessary requirement in study design.) In observational epidemiology, randomisation of study subjects between groups (say, different levels of exposure to tobacco smoke) is not possible because studies in which people are deliberately exposed to potentially harmful substances without a realistic prospect of personal benefit are unethical. As a consequence, epidemiology must rely on data generated under the unconstrained conditions of everyday life, with no intervention on the part of the investigators, and this greatly complicates the interpretation of epidemiological findings. In clinical trials, essentially all that needs to be considered (assuming the study has been correctly designed, conducted and analysed) in judging causality is whether chance is a reasonable alternative explanation for the findings, i.e. how statistically significant are the results. In epidemiology, not only must statistical fluctuations be taken into account but also the potential presence of systematic errors (of primary concern is the existence of bias in study data, but other errors are possible) and confounding (when a factor considered in a study is associated with another factor that influences the outcome, producing a distorted, potentially misleading, result).