Quantifying possible bias in clinical and epidemiological studies with quantitative bias analysis: common approaches and limitations

Bias in epidemiological studies is a major concern. Biased studies have the potential to mislead, and as a result to negatively affect clinical practice and public health. The potential for residual systematic error due to measurement bias, confounding, or selection bias is often acknowledged in publications but is seldom quantified.1 Therefore, for many studies it is difficult to judge the extent to which residual bias could affect study findings, and how confident we should be about their conclusions. Increasingly large datasets with millions of patients are available for research, such as insurance claims data and electronic health records. With increasing dataset size, random error decreases but bias remains, potentially leading to incorrect conclusions.Sensitivity analyses to quantify potential residual bias are available.234567 However, use of these methods is limited. Effective use typically requires input from multiple parties (including clinicians, epidemiologists, and statisticians) to bring together clinical and domain area knowledge, epidemiological…
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