Johnson M L, Veldhuis J D, Lampl M
Department of Pharmacology, University of Virginia Health Sciences Center, Charlottesville 22908, USA.
Endocrinology. 1996 Dec;137(12):5197-204. doi: 10.1210/endo.137.12.8940335.
Several investigators have proposed that descriptive statistics can be employed to identify and discriminate growth patterns. These studies assumed that the shape of the frequency distribution of daily growth velocities (FDGVs) is diagnostic in differentiating between a pattern of growth characterized by smooth, continuous daily acquisition and a pattern of growth characterized by a discontinuous, i.e. pulsatile process. The FDGV from a saltation and stasis, i.e. episodic or pulsatile, growth pattern was assumed to be bimodal or significantly skewed to the right, whereas a continuous growth function was assumed to be approximately Gaussian. The use of FDGV characteristics is an unprecedented approach to the analysis of longitudinal growth data and was not previously validated for this use. The present study investigates the performance characteristics of the FDGV method by Monte-Carlo simulations of known saltatory, i.e. pulsatile, growth patterns. These analyses show that the FDGV for a saltation and stasis growth process can be either unimodal or bimodal and either skewed to the right or to the left. Data collection frequency, measurement error, and total study duration all determine the shape of the FDGV and the statistical significance of the results. If the FDGV is highly skewed, then it is consistent with saltatory growth. However, if the FDGV is not highly skewed, then it is consistent with both the saltatory model and a smooth, continuous growth model, and thus, the results are ambiguous. We conclude that FDGV analysis is not a valid method to exclude saltation and stasis growth processes in longitudinal growth studies.
几位研究者提出，可以使用描述性统计来识别和区分生长模式。这些研究假设，日生长速度频率分布（FDGVs）的形状在区分以平稳、连续的每日生长为特征的生长模式和以不连续（即脉动）过程为特征的生长模式时具有诊断价值。跳跃式和停滞式（即间歇性或脉动式）生长模式的FDGV被认为是双峰的或显著向右偏斜，而连续生长函数则被认为近似于高斯分布。使用FDGV特征是一种分析纵向生长数据的前所未有的方法，此前并未针对此用途进行验证。本研究通过对已知跳跃式（即脉动式）生长模式的蒙特卡罗模拟，研究了FDGV方法的性能特征。这些分析表明，跳跃式和停滞式生长过程的FDGV可以是单峰的或双峰的，并且可以向右或向左偏斜。数据收集频率、测量误差和总研究持续时间都决定了FDGV的形状和结果的统计显著性。如果FDGV高度偏斜，那么它与跳跃式生长一致。然而，如果FDGV没有高度偏斜，那么它既与跳跃式模型一致，也与平稳、连续生长模型一致，因此，结果是不明确的。我们得出结论，在纵向生长研究中，FDGV分析不是排除跳跃式和停滞式生长过程的有效方法。
