Causation and prevention in epidemiology: assumptions, derivations, and measures old and new
Allard, R.
Show abstract
Epidemiologic measures quantifying the causative or the preventive effect of a particular agent with respect to a given disease are frequently used, but the set of assumptions on which they rest, and the consequences of these assumptions, are not widely understood. We present a rigorous derivation of these measures from the sufficient-causes model of disease occurrence and from the definition of causation as the bringing forward of the occurrence time of an event. This exercise brings out the fact that an understanding of the assumptions underpinning all measures of effect, and of the extent to which they may or may not be met, is necessary to their prudent interpretation. We also introduce a new measure, discarding 1) the sufficient-causes model and 2) the assumption that the agent can only be either causative or preventive, relative to a given disease, but not both. Some may consider this more acceptable than having to decide, on slim or no evidence, that the agent has only one kind of effect on the disease. In any case, I submit that epidemiology should eventually discard the concept of causation, as has been done in some other basic sciences, and replace it with the adequate modeling of disease-producing processes, in individuals and populations.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.