title: On the causal structure of information bias and confounding bias in randomized trials.
abstract: Randomized trials are undoubtedly different from observational studies, but authors sometimes propose differences between these designs that do not exist. In this article we examine two claims about randomized trials: first, a recent claim that the causal structure of exposure measurement (information) bias in a randomized trial differs from the causal structure of that bias in an observational study. Second, a long-standing claim that confounding bias cannot operate in a randomized trial - if randomization was perfectly implemented. Using causal diagrams (causal directed acyclic graphs), we show that both claims are false in the context of an intention-to-treat analysis. We also describe a previously unrecognized mechanism of information bias, and suggest that the term 'information bias' should replace the terms 'measurement bias' and 'observation bias'.
authors.affiliation: Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson,AZ 85724, USA. shahar@email.arizona.edu
authors.forename: Eyal
authors.initials: E
authors.lastname: Shahar
authors.forename: Doron J
authors.initials: DJ
authors.lastname: Shahar
mesh_terms: D015982:Bias; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D016032:Randomized Controlled Trials as Topic; D012107:Research Design; D009626:Terminology as Topic