Matching

Ideally, the control or comparison subjects should be indistinguishable from the patients in every characteristic apart from features of the disorder. For example, it is important that cases and controls should be matched for age, handedness, and sex, since all of these factors may affect brain structure, and a spurious difference between cases and controls might well arise if, say, the patients were on average 20 years older than the controls. Unfortunately, there are a number of other possible confounding factors which are not so obviously unrelated to presence of the disorder. For example, an unrefined sample of patients with schizophrenia will generally have lower IQ and smaller head size than an age- and sex-matched group of comparison subjects. Should we try to correct these differences as if they were spurious (by either refined sampling or post hoc statistical modelling), or should we accept that they represent real features of the disorder? In practice, most published studies tend to correct group differences on global variables by statistical modelling in order to focus attention on regional differences which may be more interesting. A comparable problem arises in relation to medication. Cases are likely to have been treated, often for many years, by drugs that can cause structural change to the brain; control subjects, by definition, will not have had this treatment. Because exposure to drug treatment is systematically confounded with diagnostic status, it cannot be well corrected by statistical modelling after the data have been acquired. The only way to be sure that group differences are uncontaminated by medication effects is to sample selectively those patients who have never been treated. However, sampling such a naive group of patients is likely to be time consuming and will also limit the scope of inference.

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