USEFUL AND EXTRANEOUS VARIABILITY IN LONGITUDINAL ASSESSMENT OF LUNG-FUNCTION
USEFUL AND EXTRANEOUS VARIABILITY IN LONGITUDINAL ASSESSMENT OF LUNG-FUNCTION
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DOI：
10.1378/chest.92.5.877
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发表时间：
1987-11-01
期刊：
影响因子：
9.6
通讯作者：
WEILL, H
中科院分区：
文献类型：
--
作者：
GLINDMEYER, HW;JONES, RN;WEILL, H
纵向测量越来越多地用于量化近期和持续影响对肺功能的影响，无论是患者群体的治疗，还是工作或社区人群的暴露。估计值的变异性，例如FEV1的平均年变化，来自两个来源：个体间年变化真实差异的变异性（称为信号）和测量误差的变异性（称为噪声）。信号是有用的变异性，可能与解释变量相关，而噪声是无关的。假设真实差异的方差保持不变，噪声的任何增加都会使信号在总观测中的比例产生可计算的下降，我们称之为“信号衰减”。“这不是个人数量的函数，而是影响统计能力，以确定观察到的差异不太可能仅仅是偶然的。对个体变化率估计的不精确是信号衰减的主要来源。在实际的限度内，这可以通过增加研究的长度来补偿。较高的受试者流失率导致信号衰减，除了统计功效的损失和对幸存者偏倚的敏感性。在恒定长度的研究中，增加测试频率对信号和噪声的影响很小，但间隔测试可以防止长期偏倚，并最大限度地减少受试者流失造成的数据丢失。
Longitudinal measurement is increasingly used to quantify the effects of recent and ongoing influences on lung function, whether treatments of groups of patients, or exposures of working or community populations. The variability in an estimate, eg, mean annual change in FEV1, comes from two sources: variability from true differences in annual change among individuals (called signal), and variability from measurement error (called noise). Signal is useful variability, potentially relatable to explanatory variables, and noise is extraneous. Assuming the variance of true differences remains constant, any increase in noise produces a calculable fall in the proportion of signal in the total observation, which fall we term "signal decay." This is not a function of the number of individuals, which influences rather the statistical power to determine that observed differences are not likely from chance alone. Imprecision in the estimation of individuals'' rates of change is a major source of signal decay. Within practical limits, this can be compensated for by increasing the length of the study. Higher rates of subject attrition cause signal decay, in addition to loss of statistical power and susceptibility to survivor bias. Increasing the frequency of testing, within a study of constant length, has little effect on signal and noise, but interval testing protects against secular bias and minimizes data loss from subject attrition.