Bias in epidemiological studies can adversely affect the validity of study findings. Sensitivity analyses, known as quantitative bias analyses, are available to quantify potential residual bias arising from measurement error, confounding, and selection into the study. Effective application of these methods benefits from the input of multiple parties including clinicians, epidemiologists, and statisticians. This article provides an overview of a few common methods to facilitate both the use of these methods and critical interpretation of applications in the published literature. Examples are given to describe and illustrate methods of quantitative bias analysis. This article also outlines considerations to be made when choosing between methods and discusses the limitations of quantitative bias analysis.
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
A multipronged observational study suggests that fluoroquinolone use does not increase the risk for aortic aneurysm or dissection. But an expert urges caution in interpretation of these data.
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