Software tools for quantitative bias analysis on measurement error or misclassification (Web of Science Core Collection and four other databases hosted on the same platform).

Abstract

Abstract The main aim of many epidemiological studies is to estimate the causal effect of an exposure on an outcome. When data is obtained for such studies, there is potential for some of the exposure, confounders, mediators, effect modifiers, or outcomes to be measured with error. Where we have categorical variables, we refer to this measurement error as misclassification. If measurement error and misclassification are not appropriately accounted for, erroneous study conclusions may be reached. Quantitative bias analysis (QBA) can be applied to studies that have not accounted for measurement error and be used to quantify the potential impact of measurement error, or how much measurement error would be needed to result in changes to the study conclusions. Currently, QBA methods are not implemented as a standard practise, in some part due to a lack of awareness about accessible software for the purpose. With this review, we aim to identify the available software that implements a QBA for studies with measurement error or misclassification.