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Causal diagrams for encoding and evaluation of information bias
Author(s) -
Shahar Eyal
Publication year - 2009
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/j.1365-2753.2008.01031.x
Subject(s) - confounding , information bias , selection bias , causality (physics) , directed acyclic graph , notation , perspective (graphical) , causal model , causal inference , representation (politics) , computer science , causal structure , econometrics , statistics , mathematics , artificial intelligence , algorithm , physics , arithmetic , quantum mechanics , politics , political science , law
Background Epidemiologists and clinical researchers usually classify bias into three main categories: confounding, selection bias and information bias. Previous authors have described the first two categories in the logic and notation of causal diagrams, formally known as directed acyclic graphs (DAG). Methods I examine common types of information bias – disease‐related and exposure‐related – from the perspective of causal diagrams. Results Disease or exposure information bias always involves the use of an effect of the variable of interest – specifically, an effect of true disease status or an effect of true exposure status. The bias typically arises from a causal or an associational path of no interest to the researchers. In certain situations, it may be possible to prevent or remove some of the bias. Conclusions Common types of information bias, just like confounding and selection bias, have a clear and helpful representation within the framework of causal diagrams.