On the causal structure of information bias and confounding bias in randomized trials

Eyal Shahar, Doron J. Shahar

Research output: Contribution to journalShort survey

10 Scopus citations

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'.

Original languageEnglish (US)
Pages (from-to)1214-1216
Number of pages3
JournalJournal of Evaluation in Clinical Practice
Volume15
Issue number6
DOIs
StatePublished - Dec 1 2009

Keywords

  • Causal diagrams
  • Confounding
  • Directed acyclic graphs
  • Information bias
  • Measurement bias
  • Randomized trials

ASJC Scopus subject areas

  • Health Policy
  • Public Health, Environmental and Occupational Health

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