历史数据中高维疾病风险评分的降维和收缩方法

IF 3.6 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

Emerging Themes in Epidemiology Pub Date : 2016-04-05 DOI:10.1186/s12982-016-0047-x

H. Kumamaru, S. Schneeweiss, R. Glynn, S. Setoguchi, J. Gagne

引用次数: 20

摘要

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Dimension reduction and shrinkage methods for high dimensional disease risk scores in historical data

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来源期刊

Emerging Themes in Epidemiology Medicine-Epidemiology

CiteScore

4.40

自引率

4.30%

发文量

审稿时长

28 weeks

期刊介绍： Emerging Themes in Epidemiology is an open access, peer-reviewed, online journal that aims to promote debate and discussion on practical and theoretical aspects of epidemiology. Combining statistical approaches with an understanding of the biology of disease, epidemiologists seek to elucidate the social, environmental and host factors related to adverse health outcomes. Although research findings from epidemiologic studies abound in traditional public health journals, little publication space is devoted to discussion of the practical and theoretical concepts that underpin them. Because of its immediate impact on public health, an openly accessible forum is needed in the field of epidemiology to foster such discussion.