Doty R L
J Clin Endocrinol Metab. 1979 Jun;48(6):912-8. doi: 10.1210/jcem-48-6-912.
A procedure is presented which overcomes most of the conceptual and statistical problems associated with the combining of data from heterogeneous menstrual cycles for graphical or statistical analyses. This procedure is based upon an initial normalization of the raw data to eliminate extraneous between-cycle variability, followed by the assignment of the data to a set of discrete cycle phases using a weighted-average technique. The efficacy of this procedure is compared to that of seven other published categorization methods by examining the proportion of variance accounted for and the P values from analyses of variance computed for 17 beta-estradiol and olfactory sensitivity measures. A major advantage of the proposed procedure is that it allows for the grouping of data from entire cycles (including menses) on the same figure without exhibiting points from heterogeneous sectors of individual cycles and without changing the sample size as distance from the midcycle LH surge increases. Thus, this procedure provides equal sample sizes for all phases of the cycle, allowing repeated-measures parametric statistical analyses to be performed. Data are presented which suggest that the categorization of menstrual cycle data without carefully established cycle phases can lead to quite erroneous conclusions.
本文介绍了一种方法，该方法克服了与将来自异质性月经周期的数据进行合并以进行图形分析或统计分析相关的大多数概念性和统计学问题。此方法基于对原始数据进行初始标准化以消除周期间无关的变异性，然后使用加权平均技术将数据分配到一组离散的周期阶段。通过检查17β-雌二醇和嗅觉敏感性测量值的方差分析中所占方差比例和P值，将此方法的有效性与其他七种已发表的分类方法进行了比较。所提出方法的一个主要优点是，它允许在同一图上对来自整个周期（包括月经期）的数据进行分组，而不会显示来自各个周期异质部分的点，并且不会随着距周期中期促黄体生成素激增距离的增加而改变样本大小。因此，该方法为周期的所有阶段提供了相等的样本大小，从而可以进行重复测量的参数统计分析。所呈现的数据表明，在没有仔细确定周期阶段的情况下对月经周期数据进行分类可能会导致相当错误的结论。
