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So‐called nested case–control studies are found in the health literature in abundance. The concept is very similar to the standard version of the case–control study, but the cases and controls are selected from a large cohort study, reducing the research work that would be needed were the whole cohort to be analysed. All the cases in the cohort will usually be selected, together with several (usually one to five) controls per case. Suppose we have decided to have five controls per case – to maximize statistical provision (more than five controls per case offer little extra precision). The controls could be selected randomly from the people in the cohort who are still at risk at the time of the index case and are time‐matched with their specific ‘case' – creating a ‘risk‐set', in this illustration consisting of one case and five controls. There are several advantages over the conventional case–control study. First, conventional case–control studies often struggle to determine control groups that truly represent the study population, and use convenience samples; here, the randomly selected controls are a representation of the people in the cohort and thereby in the study population. Second, the exposure data in the cohort design will have been collected prospectively and are thereby free of recall bias – a common problem with case–control studies (see Chapter 16). Nested studies may differ in their analysis from conventional case–control studies (see Chapter 28), but this matter is beyond the scope of this book.

Much less frequently encountered, but also derived from a large cohort study, is the case‐cohort design. In such a study, a sub‐cohort is selected from a large cohort study at the start of the follow‐up; the sub‐cohort is not time‐matched with the cases. The analysis is relatively complicated, partly because it involves the sub‐cohort and cases outside the sub‐cohort, but it offers some advantages over the conventional case–control analysis (see Chapter 28).