ArticleBMC medical informatics and decision making2024
Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
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Who cites it
2 citing papers in PubMed.
- Risk of Pelvic Insufficiency Fractures in Cervical Cancer Survivors: Using the National Claim Database.Journal of Korean medical science · 2025Article
- Risk of fragility fracture of pelvis in endometrial cancer survivors : A national claim database study.Archives of osteoporosis · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundLinkage errors that occur according to linkage levels can adversely affect the accuracy and reliability of analysis results. This study aimed to identify the differences in results according to personally identifiable information linkage level, sample size, and analysis methods through empirical analysis.
methodsThe difference between the results of linkage in directly identifiable information (DII) and indirectly identifiable information (III) linkage levels was set as III linkage based on name, date of birth, and sex and DII linkage based on resident registration number. The datasets linked at each level were named as database
resultsThe linkage rates for DB
conclusionsTo prevent distortion of the analyses results in health and medical research, it is important to check that the patient population and sample size by each factor of interest (FOI) are sufficient when different data are linked using DB
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