ArticleStatistics in medicine2014
A toolkit for measurement error correction, with a focus on nutritional epidemiology.
Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it, 127 citations in OpenAlex.
- STROBE-causal machine learning for the human microbiome: systematic review on methodological innovations and validation frameworks.Frontiers in microbiology · 2026Pooled it
- Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology.BMC medical research methodology · 2017Pooled it
- Plasma alkylresorcinols, biomarkers of whole-grain intake, are not associated with progression of coronary artery atherosclerosis in postmenopausal women with coronary artery disease.Public health nutrition · 2016Trial
- Does Timing Matter? Exploring the Effects of Measurement Error on Models.Bulletin of mathematical biology · 2026Article
- How to develop causal directed acyclic graphs for observational health research: a scoping review.Health psychology review · 2025Article
- Regression calibration for time-to-event outcomes: mitigating bias due to measurement error in real-world endpoints.Epidemiologic methods · 2025Article
- Handling missing data and measurement error for early-onset myopia risk prediction models.BMC medical research methodology · 2024Article
- Observational and genetic associations between cardiorespiratory fitness and cancer: a UK Biobank and international consortia study.British journal of cancer · 2024Article
- Systemic inflammation and subsequent risk of amyotrophic lateral sclerosis: Prospective cohort study.Brain, behavior, and immunity · 2023Article
- Exploring regression dilution bias using repeat measurements of 2858 variables in ≤49 000 UK Biobank participants.International journal of epidemiology · 2023Article
- Article
- Issues in Implementing Regression Calibration Analyses.American journal of epidemiology · 2023Article
- An augmented likelihood approach for the Cox proportional hazards model with interval-censored auxiliary and validated outcome data-with application to the Hispanic Community Health Study/Study of Latinos.Statistical methods in medical research · 2023Article
- Comparison of multiple imputation and other methods for the analysis of imputed genotypes.BMC genomics · 2023Article
- Systemic inflammation and subsequent risk of amyotrophic lateral sclerosis: prospective cohort study.medRxiv : the preprint server for health sciences · 2023Article
- Regression calibration utilizing biomarkers developed from high-dimensional metabolites.Frontiers in nutrition · 2023Article
- Article
- Valuing the Diversity of Research Methods to Advance Nutrition Science.Advances in nutrition (Bethesda, Md.) · 2022Article
- Methodological perspectives on the study of the health effects of unemployment - reviewing the mode of unemployment, the statistical analysis method and the role of confounding factors.BMC medical research methodology · 2022Article
- Quantitative prediction error analysis to investigate predictive performance under predictor measurement heterogeneity at model implementation.Diagnostic and prognostic research · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Exposure measurement error is a problem in many epidemiological studies, including those using biomarkers and measures of dietary intake. Measurement error typically results in biased estimates of exposure-disease associations, the severity and nature of the bias depending on the form of the error. To correct for the effects of measurement error, information additional to the main study data is required. Ideally, this is a validation sample in which the true exposure is observed. However, in many situations, it is not feasible to observe the true exposure, but there may be available one or more repeated exposure measurements, for example, blood pressure or dietary intake recorded at two time points. The aim of this paper is to provide a toolkit for measurement error correction using repeated measurements. We bring together methods covering classical measurement error and several departures from classical error: systematic, heteroscedastic and differential error. The correction methods considered are regression calibration, which is already widely used in the classical error setting, and moment reconstruction and multiple imputation, which are newer approaches with the ability to handle differential error. We emphasize practical application of the methods in nutritional epidemiology and other fields. We primarily consider continuous exposures in the exposure-outcome model, but we also outline methods for use when continuous exposures are categorized. The methods are illustrated using the data from a study of the association between fibre intake and colorectal cancer, where fibre intake is measured using a diet diary and repeated measures are available for a subset.
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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.