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A plea to stop using the case‐control design in retrospective database studies
Author(s) -
Schuemie Martijn J.,
Ryan Patrick B.,
Man Kenneth K.C.,
Wong Ian C.K.,
Suchard Marc A.,
Hripcsak George
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8215
Subject(s) - computer science , confounding , clinical study design , retrospective cohort study , covariate , odds ratio , research design , case control study , statistics , medicine , machine learning , surgery , clinical trial , mathematics
The case‐control design is widely used in retrospective database studies, often leading to spectacular findings. However, results of these studies often cannot be replicated, and the advantage of this design over others is questionable. To demonstrate the shortcomings of applications of this design, we replicate two published case‐control studies. The first investigates isotretinoin and ulcerative colitis using a simple case‐control design. The second focuses on dipeptidyl peptidase‐4 inhibitors and acute pancreatitis, using a nested case‐control design. We include large sets of negative control exposures (where the true odds ratio is believed to be 1) in both studies. Both replication studies produce effect size estimates consistent with the original studies, but also generate estimates for the negative control exposures showing substantial residual bias. In contrast, applying a self‐controlled design to answer the same questions using the same data reveals far less bias. Although the case‐control design in general is not at fault, its application in retrospective database studies, where all exposure and covariate data for the entire cohort are available, is unnecessary, as other alternatives such as cohort and self‐controlled designs are available. Moreover, by focusing on cases and controls it opens the door to inappropriate comparisons between exposure groups, leading to confounding for which the design has few options to adjust for. We argue that this design should no longer be used in these types of data. At the very least, negative control exposures should be used to prove that the concerns raised here do not apply.