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Assessing the known or intended effects of a drug using non-experimental epidemiologic designs is often infeasible because of the absence of accurate data on a major confounder, the severity of the disease treated by this drug. To circumvent this problem of confounding by indication, I propose the case-time-control design, which does not require a measure of this confounder. Instead, the design uses subjects from a conventional case-control design as their own controls and thus requires that exposure be measurable at two or more points in time. I present a logistic model to estimate relative risks under this design and illustrate the method with data from a case-control study of 129 cases of fatal or near-fatal asthma and 655 controls. The exposure of interest was quantity of use of inhaled beta-agonists, drugs prescribed for the treatment of asthma. I found that the “best” estimate of relative risk for high vs low beta-agonist use using the conventional case-control approach is 3.1 [95% confidence interval (CI) = 1.8–5.4], which inherently includes the confounding effect of unmeasured severity. The corresponding estimate of drug effect using the proposed case-time-control approach is 1.2 (95% CI = 0.5–3.0), which excludes the confounding effect of unmeasured severity. This example indicates that the class of beta-agonists may not play the leading role attributed to it in the risk of fatal or near-fatal asthma, as had been previously suspected, except perhaps at excessive doses, as indicated by the dose-response analyses.

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