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

Jeremy P Brown ORCID logo ; Jacob N Hunnicutt ; M Sanni Ali ORCID logo ; Krishnan Bhaskaran ORCID logo ; Ashley Cole ; Sinead M Langan ORCID logo ; Dorothea Nitsch ORCID logo ; Christopher T Rentsch ORCID logo ; Nicholas W Galwey ; Kevin Wing ORCID logo ; +1 more... Ian J Douglas ORCID logo ; (2024) Quantifying possible bias in clinical and epidemiological studies with quantitative bias analysis: common approaches and limitations. BMJ, 385. e076365-. ISSN 0959-8138 DOI: 10.1136/bmj-2023-076365
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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.


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