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  • 标题:Pattern recognition in menstrual bleeding diaries by statistical cluster analysis
  • 本地全文:下载
  • 作者:Christoph Gerlinger ; Jens Wessel ; Gerd Kallischnigg
  • 期刊名称:BMC Women's Health
  • 印刷版ISSN:1472-6874
  • 电子版ISSN:1472-6874
  • 出版年度:2009
  • 卷号:9
  • 期号:1
  • 页码:21
  • DOI:10.1186/1472-6874-9-21
  • 语种:English
  • 出版社:BioMed Central
  • 摘要:

    Background

    The aim of this paper is to empirically identify a treatment-independent statistical method to describe clinically relevant bleeding patterns by using bleeding diaries of clinical studies on various sex hormone containing drugs.

    Methods

    We used the four cluster analysis methods single, average and complete linkage as well as the method of Ward for the pattern recognition in menstrual bleeding diaries.

    The optimal number of clusters was determined using the semi-partial R2, the cubic cluster criterion, the pseudo-F- and the pseudo-t2-statistic. Finally, the interpretability of the results from a gynecological point of view was assessed.

    Results

    The method of Ward yielded distinct clusters of the bleeding diaries. The other methods successively chained the observations into one cluster. The optimal number of distinctive bleeding patterns was six. We found two desirable and four undesirable bleeding patterns. Cyclic and non cyclic bleeding patterns were well separated.

    Conclusion

    Using this cluster analysis with the method of Ward medications and devices having an impact on bleeding can be easily compared and categorized.

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