Department of Biostatistics and Informatics, University of Colorado Denver, 13001 E. 17th PL, MS B119, Aurora, CO, 80047, U.S.A.
Stat Med. 2013 Nov 20;32(26):4624-38. doi: 10.1002/sim.5882. Epub 2013 Jun 21.
Many endocrine systems are regulated by pulsatile hormones - hormones that are secreted intermittently in boluses rather than continuously over time. To study pulsatile secretion, blood is drawn every few minutes for an extended period. The result is a time series of hormone concentrations for each individual. The goal is to estimate pulsatile hormone secretion features such as frequency, location, duration, and amount of pulsatile and non-pulsatile secretion and compare these features between groups. Various statistical approaches to analyzing these data have been proposed, but validation has generally focused on one hormone. Thus, we lack a broad understanding of each method's performance. By using simulated data with features seen in reproductive and stress hormones, we investigated the performance of three recently developed statistical approaches for analyzing pulsatile hormone data and compared them to a frequently used deconvolution approach. We found that methods incorporating a changing baseline modeled both constant and changing baseline shapes well; however, the added model flexibility resulted in a slight increase in bias in other model parameters. When pulses were well defined and baseline constant, Bayesian approaches performed similar to the existing deconvolution method. The increase in computation time of Bayesian approaches offered improved estimation and more accurate quantification of estimation variation in situations where pulse locations were not clearly identifiable. Within the class of deconvolution models for fitting pulsatile hormone data, the Bayesian approach with a changing baseline offered adequate results over the widest range of data.
许多内分泌系统受脉冲激素调节 - 这些激素间歇性地以脉冲形式分泌，而不是随时间持续分泌。为了研究脉冲分泌，需要每隔几分钟抽取一次血液，持续一段时间。结果是每个个体的激素浓度时间序列。目标是估计脉冲激素分泌的特征，如频率、位置、持续时间以及脉冲和非脉冲分泌的量，并比较这些特征在组之间的差异。已经提出了各种用于分析这些数据的统计方法，但验证通常集中在一种激素上。因此，我们缺乏对每种方法性能的广泛理解。通过使用具有生殖和应激激素中可见特征的模拟数据，我们研究了三种最近开发的用于分析脉冲激素数据的统计方法的性能，并将其与常用的反卷积方法进行了比较。我们发现，结合了变化基线的方法很好地模拟了恒定和变化的基线形状；然而，额外的模型灵活性导致其他模型参数的偏差略有增加。当脉冲定义明确且基线恒定时，贝叶斯方法的表现与现有的反卷积方法相似。在不明确识别脉冲位置的情况下，贝叶斯方法的计算时间增加提供了改进的估计，并更准确地量化了估计变化。在拟合脉冲激素数据的反卷积模型中，具有变化基线的贝叶斯方法在最广泛的范围内提供了足够的结果。
