دورية أكاديمية
Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study.
| العنوان: | Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study. |
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| المؤلفون: | Lee B; Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea., Lee YK; Department of Orthopedic Surgery, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea., Kim SH; Department of Urology, Urologic Cancer Center, Research Institute and Hospital of National Cancer Center, Goyang-si, Republic of Korea., Oh H; Division of Gastroenterology, Department of Internal Medicine, Center for Cancer Prevention and Detection of National Cancer Center, Goyang-si, Republic of Korea., Won S; Institute of Health & Environment, Seoul National University, Seoul, Republic of Korea.; Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.; Interdisciplinary Program for Bioinformatics, College of Natural Science, Seoul National University, Seoul, Republic of Korea., Jang SY; Department of Healthcare Management, Graduate School of Public Health, Yonsei University, Seoul, Republic of Korea., Jeon YJ; Department of Public Health, Graduate School, Yonsei University, Seoul, Republic of Korea., Yoo BN; National Evidence-based Healthcare Collaborating Agency (NECA), 3-5F 400, Neungdong-ro, Gwangin-gu, Seoul, 04933, Republic of Korea., Bak JK; National Evidence-based Healthcare Collaborating Agency (NECA), 3-5F 400, Neungdong-ro, Gwangin-gu, Seoul, 04933, Republic of Korea. xendo2014@gmail.com. |
| المصدر: | BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2024 Jul 09; Vol. 24 (1), pp. 193. Date of Electronic Publication: 2024 Jul 09. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: London : BioMed Central, [2001- |
| مواضيع طبية MeSH: | Big Data* , Medical Record Linkage*, Humans ; Female ; Biomedical Research ; Male ; Empirical Research |
| مستخلص: | Background: Linkage 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. Methods: The 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 Results: The linkage rates for DB Conclusions: To 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 (© 2024. The Author(s).) |
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| معلومات مُعتمدة: | NECA-S-21-004 National Evidence-based Healthcare Collaborating Agency, with funding from the Ministry of Health and Welfare |
| فهرسة مساهمة: | Keywords: Accuracy; Directly identifiable information; Indirectly identifiable information; Linkage levels |
| تواريخ الأحداث: | Date Created: 20240709 Date Completed: 20240710 Latest Revision: 20240712 |
| رمز التحديث: | 20240712 |
| مُعرف محوري في PubMed: | PMC11234607 |
| DOI: | 10.1186/s12911-024-02586-0 |
| PMID: | 38982481 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1472-6947 |
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| DOI: | 10.1186/s12911-024-02586-0 |