ReviewStatistics in medicine2020
STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 1-Basic theory and simple methods of adjustment.
Review in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
82 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Healthy Eating Index Diet Quality in Randomized Weight Loss Trials: A Systematic Review.Journal of the Academy of Nutrition and Dietetics · 2023Pooled it
- Accelerometer measurement error in a randomized physical activity intervention trial in breast cancer survivors was nondifferential but attenuated the intervention effect.The international journal of behavioral nutrition and physical activity · 2025Trial
- Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome.JAMA network open · 2023Trial
- Everything All at Once: On Choosing an Estimand for Multi-component Environmental Exposures.Epidemiology (Cambridge, Mass.) · 2026Article
- When Ultra-Processed Foods are Eaten: The Missing Dimension in Exposure Measurement for Obesity Research.Current obesity reports · 2026Review
- Methods for Adjusting for Covariate Measurement Error in Flexible Modeling of Functional Form: Designing a Blinded, Controlled Neutral Comparison Simulation Study.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Estimated Dietary Lead Exposure Through Aquatic Animal Consumption and Health Symptoms in a Lake-Dependent Community: Evidence from Nong Han Lake, Northeast Thailand.International journal of environmental research and public health · 2026Article
- Identifying and mitigating bias in multiple aspects of modern clinical research.Communications medicine · 2026Review
- Impact of Missing Data and Monitoring Duration on Downstream Analyses in Continuous Glucose Monitoring.Diabetes care · 2026Article
- Enhancing Study Design and Analysis of MR Imaging Markers Through Measurement Error Modeling.Journal of magnetic resonance imaging : JMRI · 2026Article
- Long-Term Recall Error in Retrospective Family Surveys: Cohabitation Histories in Denmark.European journal of population = Revue europeenne de demographie · 2026Article
- The (ab)use of food frequency questionnaire data in substitution modelling in nutritional epidemiology: a critique.European journal of clinical nutrition · 2026Review
- Article
- Effect of a digital dietary intervention on diet quality in young adults in Norway - The PREPARED randomised controlled trial.BMC nutrition · 2026Article
- Causal Covariate Selection for the Regression Calibration Method for Exposure Measurement Error Bias Correction.Statistics in medicine · 2026Article
- Analysis approaches to combine error-prone data with a subset of validated data: an application to a multinational study of Kaposi sarcoma and HIV.American journal of epidemiology · 2026Article
- Improving the design of epidemiology studies that use biomonitoring for exposure assessment: a SciPinion panel recommendation.BMC medical research methodology · 2026Article
- "Shaking the ladder" reveals how analytic choices can influence associations in nutrition epidemiology: beef intake and coronary heart disease as a case study.Critical reviews in food science and nutrition · 2026Review
- Pharmacoepidemiology unpacked: a roadmap for junior researchers.Frontiers in pharmacology · 2026Review
- The carbon isotope ratio of alanine is a biomarker of added sugar and sugar-sweetened beverage intakes: a pooled analysis of 4 studies.The American journal of clinical nutrition · 2025Article
22 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
11 authors.
Funding
Abstract
Measurement error and misclassification of variables frequently occur in epidemiology and involve variables important to public health. Their presence can impact strongly on results of statistical analyses involving such variables. However, investigators commonly fail to pay attention to biases resulting from such mismeasurement. We provide, in two parts, an overview of the types of error that occur, their impacts on analytic results, and statistical methods to mitigate the biases that they cause. In this first part, we review different types of measurement error and misclassification, emphasizing the classical, linear, and Berkson models, and on the concepts of nondifferential and differential error. We describe the impacts of these types of error in covariates and in outcome variables on various analyses, including estimation and testing in regression models and estimating distributions. We outline types of ancillary studies required to provide information about such errors and discuss the implications of covariate measurement error for study design. Methods for ascertaining sample size requirements are outlined, both for ancillary studies designed to provide information about measurement error and for main studies where the exposure of interest is measured with error. We describe two of the simpler methods, regression calibration and simulation extrapolation (SIMEX), that adjust for bias in regression coefficients caused by measurement error in continuous covariates, and illustrate their use through examples drawn from the Observing Protein and Energy (OPEN) dietary validation study. Finally, we review software available for implementing these methods. The second part of the article deals with more advanced topics.
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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.