Invited commentary: Fewell and colleagues--fuel for debate

Area of Science:

• Epidemiology
• Biostatistics
• Health Research Methods

Background:

• Concerns persist regarding the impact of measurement error in epidemiological studies.
• Historically, measurement error was considered to benignly attenuate associations.
• Recent evidence suggests measurement error can have more complex and detrimental effects, especially in multivariate analyses.

Purpose of the Study:

• To investigate the impact of measurement error on epidemiological findings.
• To examine the behavior of measurement error within multivariate modeling.
• To understand the persistence of confounding in the presence of measurement error.

Main Methods:

• Review of existing literature on measurement error in epidemiology.
• Analysis of simulation studies or observational data (as implied by Fewell et al.).
• Focus on multivariate statistical modeling techniques.

Main Results:

• Measurement error does not always benignly attenuate associations.
• In multivariate models, measurement error can be a source of significant inconsistency.
• Residual confounding is shown to be particularly persistent when multivariate confounding is present.

Conclusions:

• The impact of measurement error in epidemiology is a critical concern.
• Multivariate modeling does not inherently resolve issues caused by measurement error.
• Researchers must carefully consider and address potential measurement error to ensure the validity of epidemiological findings.