ArticleInternational journal of epidemiology2019
Reflections on modern methods: linkage error bias.
Article in International journal of epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 35 papers, 4 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
35 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Use of routine healthcare data in randomised implementation trials: a methodological mixed-methods systematic review.Implementation science : IS · 2023Pooled it
- Pooled it
- Clinical Indicators for Asthma-COPD Overlap: A Systematic Review and Meta-Analysis.International journal of chronic obstructive pulmonary disease · 2022Pooled it
- ATLAS: an automated association test using probabilistically linked health records with application to genetic studies.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Data Resource Profile: Australia's Person Level Integrated Data Asset (PLIDA).International journal of epidemiology · 2026Article
- Investigating the association between health vulnerabilities and police enforcement during the Covid-19 pandemic: A novel study using linked administrative data in Scotland.Journal of criminology · 2025Article
- False Discovery Estimation in Record Linkage.Statistics in medicine · 2025Article
- Exploring the Complexity of Real-World Health Data Record Linkage-An Exemplary Study Linking Cancer Registry and Claims Data.Pharmacoepidemiology and drug safety · 2025Article
- Using a deterministic matching computer routine to identify hospital episodes in a Brazilian de-identified administrative database for the analysis of obstetrics hospitalisations.International journal of population data science · 2025Article
- Linking digital footprint data into longitudinal population studies.International journal of population data science · 2025Article
- Linking migration and hospital data in England: linkage process and evaluation of bias.International journal of population data science · 2024Article
- Generating synthetic identifiers to support development and evaluation of data linkage methods.International journal of population data science · 2024Article
- Examining the quality and population representativeness of linked survey and administrative data: guidance and illustration using linked 1958 National Child Development Study and Hospital Episode Statistics data.International journal of population data science · 2024Article
- Incremental benefit of booster vaccinations for COVID-19 in the United Kingdom.The Lancet regional health. Europe · 2023Article
- Retrospective, Observational Studies for Estimating Vaccine Effects on the Secondary Attack Rate of SARS-CoV-2.American journal of epidemiology · 2023Observational
- Comparing primary caregivers' reported injury data with routinely recorded injury data to assess predictors of childhood injury.BMC medical research methodology · 2023Article
- Leveraging Administrative Data to Better Understand and Address Child Maltreatment: A Scoping Review of Data Linkage Studies.Child maltreatment · 2023Article
- Lessons learned from using linked administrative data to evaluate the Family Nurse Partnership in England and Scotland.International journal of population data science · 2023Article
- Article
- Thirty-three myths and misconceptions about population data: from data capture and processing to linkage.International journal of population data science · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Linked data are increasingly being used for epidemiological research, to enhance primary research, and in planning, monitoring and evaluating public policy and services. Linkage error (missed links between records that relate to the same person or false links between unrelated records) can manifest in many ways: as missing data, measurement error and misclassification, unrepresentative sampling, or as a special combination of these that is specific to analysis of linked data: the merging and splitting of people that can occur when two hospital admission records are counted as one person admitted twice if linked and two people admitted once if not. Through these mechanisms, linkage error can ultimately lead to information bias and selection bias; so identifying relevant mechanisms is key in quantitative bias analysis. In this article we introduce five key concepts and a study classification system for identifying which mechanisms are relevant to any given analysis. We provide examples and discuss options for estimating parameters for bias analysis. This conceptual framework provides the 'links' between linkage error, information bias and selection bias, and lays the groundwork for quantitative bias analysis for linkage error.
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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.