Bora Lee, Young-Kyun Lee, Sung Han Kim, HyunJin Oh, Sungho Won, Suk-Yong Jang, Ye Jin Jeon, Bit-Na Yoo, Jean-Kyung Bak
{"title":"链接水平对健康和医学研究中大数据分析推论的影响：一项实证研究。","authors":"Bora Lee, Young-Kyun Lee, Sung Han Kim, HyunJin Oh, Sungho Won, Suk-Yong Jang, Ye Jin Jeon, Bit-Na Yoo, Jean-Kyung Bak","doi":"10.1186/s12911-024-02586-0","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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<sub>III</sub> (DB<sub>III</sub>) and database<sub>DII</sub> (DB<sub>DII</sub>), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed.</p><p><strong>Results: </strong>The linkage rates for DB<sub>DII</sub> and DB<sub>III</sub> were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was \"none\" to \"very little.\" With respect to cervical cancer that had a relatively small sample size, analysis of DB<sub>III</sub> resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DB<sub>III</sub> vs. 1.80 [95% CI: 1.18-2.73] in DB<sub>DII</sub>). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DB<sub>III</sub> vs. 1.92 [95% CI: 1.70-2.17] in DB<sub>DII</sub> for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DB<sub>III</sub> vs. 2.05 [95% CI: 1.67-2.52] in DB<sub>DII</sub> for the regional stage).</p><p><strong>Conclusions: </strong>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<sub>DII</sub>. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11234607/pdf/","citationCount":"0","resultStr":"{\"title\":\"Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study.\",\"authors\":\"Bora Lee, Young-Kyun Lee, Sung Han Kim, HyunJin Oh, Sungho Won, Suk-Yong Jang, Ye Jin Jeon, Bit-Na Yoo, Jean-Kyung Bak\",\"doi\":\"10.1186/s12911-024-02586-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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<sub>III</sub> (DB<sub>III</sub>) and database<sub>DII</sub> (DB<sub>DII</sub>), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed.</p><p><strong>Results: </strong>The linkage rates for DB<sub>DII</sub> and DB<sub>III</sub> were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was \\\"none\\\" to \\\"very little.\\\" With respect to cervical cancer that had a relatively small sample size, analysis of DB<sub>III</sub> resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DB<sub>III</sub> vs. 1.80 [95% CI: 1.18-2.73] in DB<sub>DII</sub>). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DB<sub>III</sub> vs. 1.92 [95% CI: 1.70-2.17] in DB<sub>DII</sub> for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DB<sub>III</sub> vs. 2.05 [95% CI: 1.67-2.52] in DB<sub>DII</sub> for the regional stage).</p><p><strong>Conclusions: </strong>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<sub>DII</sub>. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.</p>\",\"PeriodicalId\":9340,\"journal\":{\"name\":\"BMC Medical Informatics and Decision Making\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11234607/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Medical Informatics and Decision Making\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s12911-024-02586-0\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Medical Informatics and Decision Making","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12911-024-02586-0","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Impact of linkage level on inferences from big data analyses in health and medical research: an empirical study.
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 databaseIII (DBIII) and databaseDII (DBDII), respectively. Considering the analysis results of the DII-linked dataset as the gold standard, descriptive statistics, group comparison, incidence estimation, treatment effect, and moderation effect analysis results were assessed.
Results: The linkage rates for DBDII and DBIII were 71.1% and 99.7%, respectively. Regarding descriptive statistics and group comparison analysis, the difference in effect in most cases was "none" to "very little." With respect to cervical cancer that had a relatively small sample size, analysis of DBIII resulted in an underestimation of the incidence in the control group and an overestimation of the incidence in the treatment group (hazard ratio [HR] = 2.62 [95% confidence interval (CI): 1.63-4.23] in DBIII vs. 1.80 [95% CI: 1.18-2.73] in DBDII). Regarding prostate cancer, there was a conflicting tendency with the treatment effect being over or underestimated according to the surveillance, epidemiology, and end results summary staging (HR = 2.27 [95% CI: 1.91-2.70] in DBIII vs. 1.92 [95% CI: 1.70-2.17] in DBDII for the localized stage; HR = 1.80 [95% CI: 1.37-2.36] in DBIII vs. 2.05 [95% CI: 1.67-2.52] in DBDII for the regional stage).
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 DBDII. In cases involving a rare disease or with a small sample size for FOI, there is a high likelihood that a DII linkage is unavoidable.
期刊介绍：
BMC Medical Informatics and Decision Making is an open access journal publishing original peer-reviewed research articles in relation to the design, development, implementation, use, and evaluation of health information technologies and decision-making for human health.
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