Bias may arise because of selection into or out of the study population. However, associations must be interpreted with caution. Such information can be used to explore aetiology – for example, the relation between cataract and vitamin status has been examined in cross sectional surveys. Selection of casesĪ cross sectional study measures the prevalence of health outcomes or determinants of health, or both, in a population at a point in time or over a short period. Statistical adjustment is still required. Unlike in a cohort study, however, matching does not on its own eliminate confounding. This statistical adjustment may be rendered more efficient by matching cases and controls for exposure to confounders, either on an individual basis (for example by pairing each case with a control of the same age and sex) or in groups (for example, choosing a control group with an overall age and sex distribution similar to that of the cases). Allowance is made for potential confounding factors by measuring them and making appropriate adjustments in the analysis. This permits estimation of odds ratios (but not of attributable risks). In a case-control study patients who have developed a disease are identified and their past exposure to suspected aetiological factors is compared with that of controls or referents who do not have the disease. An alternative which avoids this difficulty is the case-control or case-referent design. As discussed in the previous chapter, one of the drawbacks of using a longitudinal approach to investigate the causes of disease with low incidence is that large and lengthy studies may be required to give adequate statistical power.
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