Eyal Shahar1. 1. Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, AZ 85724, USA. shahar@email.arizona.edu
Abstract
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.
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.
Authors: Matthew A Sparks; Andrew M South; Andrew D Badley; Carissa M Baker-Smith; Daniel Batlle; Biykem Bozkurt; Roberto Cattaneo; Steven D Crowley; Louis J Dell'Italia; Andria L Ford; Kathy Griendling; Susan B Gurley; Scott E Kasner; Joseph A Murray; Karl A Nath; Marc A Pfeffer; Janani Rangaswami; W Robert Taylor; Vesna D Garovic Journal: Hypertension Date: 2020-09-28 Impact factor: 10.190
Authors: Aya A Mitani; Nathaniel D Mercaldo; Sebastien Haneuse; Jonathan S Schildcrout Journal: BMC Med Res Methodol Date: 2021-07-11 Impact factor: 4.612