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 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 expertise, and a statistical understanding of the methods. Improved awareness of these methods and their pitfalls will enable more frequent and effective implementation, as well as critical interpretation of their …