PubMed · 10871984
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R P Sloan, E Bagiella. 2000-06-26. Data without a prayer.. https://doi.org/10.1001/archinte.160.12.1870
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Once a new vaccine has been licensed and is in widespread use, it is important to be able to detect and investigate adverse events which may be associated with the vaccine. This is particularly important for rare events which may have been missed in pre-licensure studies. Recent studies investigating adverse events following vaccination have used either a population or a sample of the population (cohort studies), cases matched to controls (case-control studies), or just information on the cases themselves (self-controlled case-series studies). In this paper, the three methods are reviewed and compared and examples are given of recent applications. The effects of potential sources of bias are demonstrated using a data set on MMR vaccine and convulsions.
The case-only design, which requires only diseased subjects, allows for estimation of multiplicative interactions between factors known to be independent in the study population. The design is being used as an alternative to the case-control design to study gene-environment interactions. Estimates of gene-environment interactions have been shown to be very efficient relative to estimates obtained with a case-control study under the assumption of independence between the genetic and environmental factors. In this paper, the authors explore the robustness of this procedure to uncertainty about the independence assumption. By using simulations, they demonstrate that inferences about the multiplicative interaction with the case-only design can be highly distorted when there is departure from the independence assumption. They illustrate their results with a recent study of gene-environment interactions and risk of lung cancer incidence in a cohort of miners from the Yunnan Tin Corporation in southern China. Investigators should be aware that the increased efficiency of the case-only design is a consequence of a strong assumption and that this design can perform poorly if the assumption is violated.
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