Quantifying possible bias in clinical and epidemiological studies with quantitative bias analysis: common approaches and limitations
Jeremy Brown, Jacob N. Hunnicutt, M. Sanni Ali, Krishnan Bhaskaran, Ashley L. Cole, Sinéad Langan, Dorothea Nitsch, Christopher T. Rentsch, N. W. Galwey, Kevin Wing, Ian Douglas
Abstract
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.