Causal diagrams for encoding and evaluation of information bias

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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.

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