Publication: Removal of ineligible outcome cases reduces confounding
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Date
2018
Published Version
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Dove Medical Press
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Citation
Walker, Alexander M, Sebastian Schneeweiss, Maral DerSarkissian, and Mei Sheng Duh. 2018. “Removal of ineligible outcome cases reduces confounding.” Clinical Epidemiology 10 (1): 575-579. doi:10.2147/CLEP.S160482. http://dx.doi.org/10.2147/CLEP.S160482.
Research Data
Abstract
Background: When an exclusionary criterion is an imperfect screen, some ineligible patients will remain in a study. Medical record review for outcome adjudication can reveal such individuals. Objective: To ascertain the circumstances under which it is advisable to remove outcome cases first found to be ineligible on chart review. Methods: The impact on the relative risk caused by removal of ineligible outcome cases was examined under different circumstances of confounding, prevalence, and efficacy of the screening criterion for exclusions. The result is illustrated by a hospital-based cohort study in which electronic medical record diagnosis served to exclude ineligible cases, and review of text notes for putative outcome cases revealed that the codes were only 95% sensitive. Other hypothetical scenarios provide further evidence. Results: If a condition to be excluded is a confounder of the exposure–outcome relation, residual confounding will continue to bias a study after application of an imperfect screening criterion. Removal of ineligible outcome cases after chart review creates a new bias, distinct from residual confounding. The new bias does not depend on the magnitude of the confounder–outcome association, and will be small if the exclusion criterion has resulted in a low prevalence of the exclusionary condition. The new bias caused by removal of ineligible outcome cases is almost certain to be smaller than the confounding bias that can result if they are retained. Conclusions: Outcome cases first discovered at chart review to be study-ineligible should be removed from the study, even when similar scrutiny is infeasible for non-cases.
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Keywords
exclusions, eligibility, confounding, study design, adjudication, analysis
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