Correcting for serial dependence in studies of respiratory dynamics.
Correcting for serial dependence in studies of respiratory dynamics.
复制标题
纠正呼吸动力学研究中的序列依赖性。
DOI：
10.1109/iembs.2011.6090493
复制
发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Brown,EmeryN
中科院分区：
文献类型：
--
作者：
Gong,JenJ;Wong,KinFoonKevin;Cotten,JosephF;Solt,Ken;Brown,EmeryN
了解药物治疗对患者的生理影响对于评估其表现和确定可能的副作用非常重要。虽然这种效果可能最好在个体受试者中确定，但传统方法通过平均n个受试者在给药前后的感兴趣生理测量来评估治疗效果。以这种方式对每个主题用两种方法总结大量的时间序列观测结果会导致严重的信息损失。治疗效果可以在个体受试者中进行分析。因为必须考虑来自同一动物的观察结果的序列依赖性，所以不能使用假设观察结果独立的方法，如t检验和z检验。我们在从注射多巴胺激动剂的麻醉大鼠收集的呼吸数据的情况下解决了这个问题。为了准确地评估时间序列数据中的治疗效果，我们首先制定了一种条件似然最大化方法来估计一阶自回归（AR）过程的参数。我们表明，多巴胺激动剂的治疗效果可以确定，同时纳入系列效应的分析。此外，虽然具有独立观测值的大样本的极大似然估计可能收敛于渐近正态分布，但当观测值是序列相关时，大样本理论的这一结果可能不成立。在这种情况下，可以使用参数自举比较来近似适当的不确定性度量。
Understanding the physiological impact of drug treatments on patients is important in assessing their performance and determining possible side effects. While this effect might be best determined in individual subjects, conventional methods assess treatment performance by averaging a physiological measure of interest before and after drug administration for n subjects. Summarizing large numbers of time-series observations in two means for each subject in this way results in significant information loss. Treatment effect can instead be analyzed in individual subjects. Because serial dependence of observations from the same animal must then be considered, methods that assume independence of observations, such as the t-test and z-test, cannot be used. We address this issue in the case of respiratory data collected from anesthetized rats that were injected with a dopamine agonist. In order to accurately assess treatment effect in time-series data, we begin by formulating a method of conditional likelihood maximization to estimate the parameters of a first-order autoregressive (AR) process. We show that treatment effect of a dopamine agonist can be determined while incorporating serial effect into the analysis. In addition, while maximum likelihood estimators of a large sample with independent observations may converge to an asymptotically normal distribution, this result of large sample theory may not hold when observations are serially dependent. In this case, a parametric bootstrap comparison can be used to approximate an appropriate measure of uncertainty.