...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics in medicine >A population-based approach to analyzing pulses in time series of hormone data
【24h】

A population-based approach to analyzing pulses in time series of hormone data

机译：一种基于群体的激素数据时间序列脉冲分析方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Studies of reproductive physiology involve rapid sampling protocols that result in time series of hormone concentrations. The signature pattern in these times series is pulses of hormone release. Various statistical models for quantifying the pulsatile release features exist. Currently these models are fitted separately to each individual and the resulting estimates averaged to arrive at post hoc population-level estimates. When the signal-to-noise ratio is small or the time of observation is short (e.g., 6 h), this two-stage estimation approach can fail. This work extends the single-subject modelling framework to a population framework similar to what exists for complex pharamacokinetics data. The goal is to leverage information across subjects to more clearly identify pulse locations and improve estimation of other model parameters. This modelling extension has proven difficult because the pulse number and locations are unknown. Here, we show that simultaneously modelling a group of subjects is computationally feasible in a Bayesian framework using a birth-death Markov chain Monte Carlo estimation algorithm. Via simulation, we show that this population-based approach reduces the false positive and negative pulse detection rates and results in less biased estimates of population-level parameters of frequency, pulse size, and hormone elimination. We then apply the approach to a reproductive study in healthy women where approximately one-third of the 21 subjects in the study did not have appropriate fits using the single-subject fitting approach. Using the population model produced more precise, biologically plausible estimates of all model parameters. Copyright (C) 2017 John Wiley Sons, Ltd.
机译：生殖生理学研究涉及快速采样方案，这些方案可产生激素浓度的时间序列。这些时间序列中的特征模式是激素释放脉冲。存在用于量化脉动释放特征的各种统计模型。目前，这些模型分别拟合到每个人身上，并将由此产生的估计值平均，以得出事后种群水平的估计值。当信噪比较小或观察时间较短（例如，6 小时）时，这种两阶段估计方法可能会失败。这项工作将单受试者建模框架扩展到类似于复杂法动力学数据的种群框架。目标是利用跨受试者的信息来更清楚地识别脉冲位置并改进对其他模型参数的估计。事实证明，这种建模扩展很困难，因为脉冲数量和位置是未知的。在这里，我们表明，在贝叶斯框架中，使用出生-死亡马尔可夫链蒙特卡洛估计算法同时对一组受试者进行建模在计算上是可行的。通过仿真，我们表明这种基于人群的方法降低了假阳性和阴性脉冲检测率，并减少了频率、脉冲大小和激素消除等人群水平参数的偏差估计。然后，我们将该方法应用于健康女性的生殖研究，其中研究中的 21 名受试者中约有三分之一没有使用单受试者拟合方法进行适当的拟合。使用种群模型对所有模型参数进行了更精确、生物学上合理的估计。版权所有 （C） 2017 John Wiley & Sons， Ltd.

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
  • 1. Data pulse timing [P] . 外国专利： GB2200518A . 1988-08-03

    机译：data pulse timing

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号