Doidge, JC;
Harron, KL;
(2019)
Reflections on modern methods: linkage error bias.
International Journal of Epidemiology
10.1093/ije/dyz203.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Reflections on modern methods: linkage error bias |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/ije/dyz203 |
| Publisher version: | https://doi.org/10.1093/ije/dyz203 |
| Language: | English |
| Additional information: | © The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Linkage error, bias, bias analysis, data linkage, information bias, missing data, quantitative bias analysis, record linkage, selection bias, sensitivity analysis |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10084507 |
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