When applying study results to their practice, the clinician is constrained by a number of factors, perhaps none more important than spectrum bias, which describes the effect a change in patient case mix may have on the performance of a test. Although the literature contains notable examples of spectrum bias, the emphasis has been to demonstrate its existence and its implications on study design rather than how it affects the clinician. Here a definition is proposed before considering it from a GP's perspective. As a patient's probability of disease is in part determined by the test's result, having reliable estimates of a test's performance is imperative to making good decisions on patient management. Knowing how the test performs on a patient usually means knowing its performance within a particular subgroup. Unfortunately, studies tend to report weighted average estimates of performance across broad populations. Such estimates may be inaccurate at an individual level and at a population level with the overall performance of the test in practice varying significantly from the average estimate reported, owing to differing case mixes. To avert such problems, investigators should design studies to evaluate tests over all relevant subgroups, and where this is not possible, to be explicit about the case mix in the study sample. Furthermore, GPs should endeavour to know both individual patients and practice populations as a whole in terms of demographics and co-morbidities before applying study results to their patients.
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