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Reflection on modern methods: five myths about measurement error in epidemiological research
Maarten van Smeden1, Timothy L Lash2, Rolf H H Groenwold1,3
1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
International Journal of Epidemiology
|December 11, 2019
Summary
Measurement error in epidemiological data can invalidate study findings if misunderstood. This paper debunks five common myths about measurement error, offering guidance for epidemiologists to better analyze and report its impact.
Area of Science:
- Epidemiology
- Biostatistics
- Health Research Methods
Background:
- Epidemiological datasets frequently contain measurement error from various sources like data entry mistakes or instrument inaccuracies.
- Misjudging the impact of measurement error can compromise data analysis validity and study inferences.
Purpose of the Study:
- To clarify common misconceptions surrounding measurement error in epidemiological analyses.
- To address five prevalent myths regarding the structure, impact, and mitigation of measurement error.
Main Methods:
- Discussion and clarification of five key myths related to measurement error in epidemiological studies.
- Analysis of how measurement error can influence effect estimates beyond simple heuristics.
- Recommendations for addressing measurement error, including statistical corrections and bias analysis.
Main Results:
- Measurement error's influence can be complex and deviate from simple assumptions, such as consistent attenuation of effect estimates.
- Common heuristics about measurement error's impact are often insufficient for accurate assessment.
- The paper identifies specific myths that lead to misjudgments about measurement error.
Conclusions:
- Epidemiologists should carefully consider the structure and impact of measurement error in their analyses.
- Claims about the magnitude or direction of measurement error effects require statistical correction or bias analysis.
- Guidance is provided for investigating and mitigating problems associated with measurement error in epidemiological research.
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