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Happy the man who has been able to learn the causes of things—Virgil
In epidemiology we strive to identify the root causes of disease, in the hope that this will enable us to devise preventive strategies. Observational studies constitute our ‘bread and butter’ and, in carrying out such studies, we find associations and make judgements on whether they have arisen through chance, bias or confounding, before considering possible causal interpretations. Confounding is the bane of an epidemiologist’s life because, however hard we try to rule it out as a possible explanation for a link between exposure and disease in observational studies, it is nigh on impossible to do so with total confidence. While we do our best to take potential confounders ‘out of the equation’ in our analyses, usually by controlling for them in multivariable regression models, if confounders have been measured imprecisely there will still be residual confounding, and other confounders may not have been measured (some may be unknown) and therefore cannot be controlled for.
The gold standard study design for obtaining unconfounded evidence of causality is the randomised controlled trial (RCT), because randomisation should lead to even distribution of confounders between treatment arms. However, sometimes trials are not ethical to conduct and, even if feasible, large trials are very expensive and time-consuming. In recent years a welcome addition to the epidemiologist’s armoury for strengthening causal inference in observational studies has been Mendelian randomisation (MR), an approach championed by George Davey Smith and colleagues,1 which can be regarded as a ‘natural RCT’. MR is a form of instrumental variable analysis, whereby a genetic variant can be used as a proxy for a specific exposure to test whether that exposure is likely to …
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