For the purpose of screening, it may not be possible to obtain data that directly relate to the phenomena and populations of interest, nor to develop and apply models specifically designed for these situations.  In these cases, however, it may be helpful (and possible) to use analogues – i.e. to ‘borrow’ data or models from other, comparable situations. 

For example:

• if an exposure-response function is not available for the specific exposure (or health outcome or sub-population) of interest, one might be inferred from a similar one;
• if exposure distributions cannot be estimated in the specific micro-environment of interest, they might be inferred from an analogous one;
• if severity weights are not available for the specific disease (or population sub-group) of interest, they might be inferred from one with similar characteristics.

Analogues therefore have parallels with proxies (see link to left), and share many of the same issues.  They differ in that, while proxies are functional substitutes for the phenomena of interest, analogues are extrapolations from similar (but functionally unrelated) situations.   We therefore have to choose analogues with great care, for their validity depends wholly on the extent to which they are comparable in behaviour.  As with other methods of approximation, evidence to support their use is crucial; if this does not already exist, then some form of testing ought to be done to confirm that they are appropriate.