ArticleInternational journal of epidemiology2020
Reflection on modern methods: five myths about measurement error in epidemiological research.
Article in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
66 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- Melodic Intonation Therapy for aphasia: A multi-level meta-analysis of randomized controlled trials and individual participant data.Annals of the New York Academy of Sciences · 2022Pooled it
- Treatment effects in randomised trials using routinely collected data for outcome assessment versus traditional trials: meta-research study.BMJ (Clinical research ed.) · 2021Pooled it
- Added Value of a Blinded Outcome Adjudication Committee in an Open-Label Randomized Stroke Trial.Stroke · 2022Trial
- Negative controls and how to use them: a guidance paper.International journal of epidemiology · 2026Article
- Measurement Error Models Enable Accurate Correlation of Nanoscale Properties and Structures.ACS nano · 2026Article
- Comparison of disease risk score methods to study treatment effect heterogeneity: a simulation study.American journal of epidemiology · 2026Article
- Associations of Exposure to Common Plasticizers and Organophosphate Pesticides during Pregnancy and in Childhood with Cognitive Performance in Adolescents: A Population-Based Study.Environmental science & technology · 2026Article
- Hookups, hashtags, and health: A Latent Class Analysis of social media, mental health and sleep among MSM in Paris, France.SSM. Mental health · 2026Article
- Seasonal Variation of Plaque Psoriasis in Relation to Individualized MED-Adjusted Ultraviolet Exposure: A Cross-Sectional Study in Poland.Journal of clinical medicine · 2026Article
- Measuring reading time: Comparing logged and self-reported data in relation to reading skills.PloS one · 2026Article
- Sociodemographic Determinants of Hospitalized Exacerbations in Exposure-Defined COPD Phenotypes: A Nationwide Prospective Cohort Study in China.International journal of chronic obstructive pulmonary disease · 2026Article
- Impact of the longitudinal evolution of impaired fasting glucose on cardio-kidney-metabolic multimorbidity.Frontiers in endocrinology · 2026Article
- Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke.Environmental research · 2025Article
- Survey of practices of handling exposure measurement errors in modern epidemiology: are the best practices in statistics being adopted by epidemiologists?BMC medical research methodology · 2025Article
- Assessing differential application of thromboprophylaxis regimes related to risk of pulmonary embolism and mortality in COVID-19 patients through instrumental variable analysis.Scientific reports · 2025Article
- Article
- Causal inference concepts can guide research into the effects of climate on infectious diseases.Nature ecology & evolution · 2025Article
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- Common methodological issues in observational epidemiological studies of older adults.BMJ medicine · 2025Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Epidemiologists are often confronted with datasets to analyse which contain measurement error due to, for instance, mistaken data entries, inaccurate recordings and measurement instrument or procedural errors. If the effect of measurement error is misjudged, the data analyses are hampered and the validity of the study's inferences may be affected. In this paper, we describe five myths that contribute to misjudgments about measurement error, regarding expected structure, impact and solutions to mitigate the problems resulting from mismeasurements. The aim is to clarify these measurement error misconceptions. We show that the influence of measurement error in an epidemiological data analysis can play out in ways that go beyond simple heuristics, such as heuristics about whether or not to expect attenuation of the effect estimates. Whereas we encourage epidemiologists to deliberate about the structure and potential impact of measurement error in their analyses, we also recommend exercising restraint when making claims about the magnitude or even direction of effect of measurement error if not accompanied by statistical measurement error corrections or quantitative bias analysis. Suggestions for alleviating the problems or investigating the structure and magnitude of measurement error are given.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.