A Bayesian Approach to Modeling Associations Between Pulsatile Hormones
A Bayesian Approach to Modeling Associations Between Pulsatile Hormones
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DOI：
10.1111/j.1541-0420.2008.01117.x
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发表时间：
2009-06-01
期刊：
影响因子：
1.9
通讯作者：
Brown, Morton B.
中科院分区：
文献类型：
--
作者：
Carlson, Nichole E.;Johnson, Timothy D.;Brown, Morton B.
豆类中分泌许多激素。激素之间的脉动关系调节许多生物过程。为了了解内分泌系统的调节，需要收集激素浓度的时间序列。目标是表征激素之间的脉动模式和关联。目前，每个受试者的每种激素都是单变量拟合的。这导致了脉冲数量的估计和激素分泌量的估计；然而，当信噪比较小时，现有方法的脉冲检测和参数估计仍然很困难。在本文中，我们提出了脉动激素数据的双变量反卷积模型，重点关注合并脉动关联。通过模拟，我们证明，使用两种激素之间潜在的脉冲关联可以改进对脉冲数量和定义每种激素的其他参数的估计。我们开发了一对一的驾驶员响应案例，并展示了如何使用生死马尔可夫链蒙特卡罗进行估计。我们通过模拟研究展示了这些特征，并将该方法应用于黄体生成激素和卵泡刺激激素。
Many hormones are secreted in pulses. The pulsatile relationship between hormones regulates many biological processes. To understand endocrine system regulation, time series of hormone concentrations are collected. The goal is to characterize pulsatile patterns and associations between hormones. Currently each hormone on each subject is fitted univariately. This leads to estimates of the number of pulses and estimates of the amount of hormone secreted; however, when the signal-to-noise ratio is small, pulse detection and parameter estimation remains difficult with existing approaches. In this article, we present a bivariate deconvolution model of pulsatile hormone data focusing on incorporating pulsatile associations. Through simulation, we exhibit that using the underlying pulsatile association between two hormones improves the estimation of the number of pulses and the other parameters defining each hormone. We develop the one-to-one, driver-response case and show how birth-death Markov chain Monte Carlo can be used for estimation. We exhibit these features through a simulation study and apply the method to luteinizing and follicle stimulating hormones.