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利用多重插补法估算牙周炎易感性病例用于流行病学研究。

Estimating Periodontitis Susceptibility Cases for Epidemiological Studies with Multiple Imputation.

机构信息

Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN, USA.

Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.

出版信息

JDR Clin Trans Res. 2024 Oct;9(4):378-386. doi: 10.1177/23800844241228277. Epub 2024 Mar 14.

Abstract

Our proposed estimate of periodontitis susceptibility cases addresses the issue of missing teeth, offering an innovative solution through a generative missing data imputation model. The implications of our findings extend to fostering more robust investigations into the relationships between periodontal health and systemic diseases, thereby offering valuable insights to clinicians for informed decision-making. Moreover, the study's capacity to shape clinical practices and interventions in public health will further fortify health policy strategies.

摘要

我们提出的牙周炎易感性病例估计方法解决了缺牙问题,通过生成式缺失数据插补模型提供了一种创新的解决方案。我们研究结果的意义还在于促进对牙周健康与全身疾病之间关系的更深入研究,从而为临床医生提供决策依据。此外,该研究在塑造公共卫生临床实践和干预措施方面的能力将进一步加强卫生政策策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c850/11406868/92a459514740/10.1177_23800844241228277-fig1.jpg

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