Measurement error and information bias in causal diagrams: mapping epidemiological concepts and graphical structures

Area of Science:

• Epidemiology
• Causal Inference
• Biostatistics

Background:

• Measurement error and information bias are common challenges in epidemiological studies.
• Directed acyclic graphs (DAGs) are underutilized for representing measurement error and information bias, despite their utility for confounding and selection bias.
• Current DAG applications often focus on unmeasured constructs, overlooking empirically measured variables.

Purpose of the Study:

• To demonstrate the application of DAGs in depicting data-generating mechanisms, including measurement error.
• To highlight the benefits and challenges of using DAGs to represent measurement error in epidemiological research.
• To extend DAG utility to empirically measured variables and information bias, aiding causal analysis.

Main Methods:

• Utilized a general example to illustrate empirical data considerations related to measurement error.
• Developed a specific worked example from clinical epidemiology of hearing health to showcase DAG application.
• Focused on mapping traditional epidemiological concepts (information bias, confounding) onto causal graphical structures.

Main Results:

• DAGs can be effectively applied to visualize and analyze the impact of measurement error on epidemiological data.
• Incorporating empirically measured variables into DAGs offers advantages for understanding complex associations.
• The study highlights implications for causal structures, such as unblocked backdoor paths, when accounting for measurement error.

Conclusions:

• Applying DAGs to empirically measured variables, including those with measurement error, enhances epidemiological analysis.
• This approach increases clarity in mapping epidemiological concepts like information bias and confounding onto causal graphs.
• Increased adoption of DAGs for measurement error can improve the rigor and interpretability of epidemiological research.