Causal diagrams for encoding and evaluation of information bias

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.