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BACKGROUND: Studies of the effect of exposure to a risk factor measured in an entire cohort may be augmented by nested case-control subsets to investigate confounding or effect modification by additional factors not practically assessed on all cohort members. We compared three control-selection strategies-matching on exposure, counter matching on exposure, and random sampling-to determine which was most efficient in a situation where exposure is a known, continuous variable and high doses are rare. METHODS: We estimated the power to detect interaction using four control-to-case ratios (1:1, 2:1, 4:1, and 8:1) in a planned case-control study of the joint effect of atomic bomb radiation exposure and serum oestradiol levels on breast cancer. Radiation dose is measured in the entire cohort, but because neither serum oestradiol level nor the true degree of interaction was known, we simulated values of oestradiol and hypothetical levels of oestradiol-radiation interaction. RESULTS: Compared with random sampling, power to detect interaction was similarly higher with either matching or counter matching with two or more controls. CONCLUSIONS: Because counter matching is generally at least as efficient as random sampling, whereas matching on exposure can result in loss of efficiency and precludes estimation of exposure risk, we recommend counter matching for selecting controls in nested case-control studies of the joint effects of multiple risk factors when one is previously measured in the full cohort.
John Cologne (Tue,) studied this question.