Bias Analysis Gone Bad

• Published in: American journal of epidemiology 2021-08, Vol.190 (8), p.1604-1612
• Main Authors: Lash, Timothy L, Ahern, Thomas P, Collin, Lindsay J, Fox, Matthew P, MacLehose, Richard F
• Format: Article
• Language: English
• Subjects: Antidepressive Agents - adverse effects; Bias; Breast Neoplasms - chemically induced; Contraceptive Agents, Hormonal - adverse effects; Data Interpretation, Statistical; Editor's Choice; Epidemiologic Studies; Epidemiology; Familiarity; Humans; Marijuana Abuse - complications; Mental Disorders - etiology; Models, Statistical; Practice of Epidemiology; Reproducibility of Results; Research Design - standards; Systematic errors; Uncertainty
• ISSN: 0002-9262; 1476-6256
• DOI: 10.1093/aje/kwab072

Abstract Quantitative bias analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional but could occasionally include intentional efforts to mislead. We identified 3 examples of suboptimal bias analysis, one for each common bias. For each, we describe the original research and its bias analysis, compare the bias analysis with good practices, and describe how the bias analysis and research findings might have been improved. We assert no motive to the suboptimal bias analysis by the original authors. Common shortcomings in the examples were lack of a clear bias model, computed example, and computing code; poor selection of the values assigned to the bias model’s parameters; and little effort to understand the range of uncertainty associated with the bias. Until bias analysis becomes more common, community expectations for the presentation, explanation, and interpretation of bias analyses will remain unstable. Attention to good practices should improve quality, avoid errors, and discourage manipulation.