Harris E K
Clin Chem. 1976 Aug;22(8):1343-50.
Three models of intraindividual variation are reviewed, and statistical methods for distinguishing among them are discussed. Application of these methods to short series of observations from healthy individuals indicates that, in the large majority of cases, a strictly homeostatic model is appropriate for such constituents as serum calcium and magnesium. In less closely controlled variables, e.g., serum cholesterol and uric acid, a nonstationary, "rndom walk" model appears moresuitable in most cases. A more general autoregressive model, which includes the other models as extreme cases, could be used to describe all degrees of homeostatic control. This model is more complex, however, and requires at least 10 observations to yield estimates of acceptable precision. Moreover, it is sensitive to fluctuations in within-batch analytical variance. When biological variance is small relative to analytical variance, all three models yield essentially the same predicated values. To illustrate their use, these models have been applied to four short individual series of cholesterol observations showing increasing amounts of intrapersonal variation over long periods of time. I suggest that when less than 10 observations over time are available, the strictly homeostatic model and the nonstationary model be used to derive a "critical range" for assessing future changes. When longer series are available, the more general model might replace the other two for this purpose, if analytical variation has remained reasonably stable (within +/- 20% of its average value) during the period of observation. Much more experience with the use of all three models in health monitoring programs would be highly desirable.
本文回顾了个体内变异的三种模型,并讨论了区分它们的统计方法。将这些方法应用于健康个体的短时间观察系列表明,在大多数情况下,严格的稳态模型适用于血清钙和镁等成分。在控制不那么严格的变量中,例如血清胆固醇和尿酸,在大多数情况下,非平稳的“随机游走”模型似乎更合适。一个更通用的自回归模型,它将其他模型作为极端情况包含在内,可用于描述所有程度的稳态控制。然而,这个模型更复杂,需要至少10次观察才能得出精度可接受的估计值。此外,它对批内分析方差的波动很敏感。当生物方差相对于分析方差较小时,所有三种模型产生的预测值基本相同。为了说明它们的用途,这些模型已应用于四个胆固醇观察的短个体系列,这些系列显示长期内个体内变异量不断增加。我建议,当随时间的观察次数少于10次时,使用严格的稳态模型和非平稳模型来推导一个“临界范围”,以评估未来的变化。当有更长的系列数据时,如果在观察期间分析方差保持合理稳定(在其平均值的+/-20%范围内),更通用的模型可能会为此目的取代其他两个模型。非常希望在健康监测计划中使用这三种模型方面有更多的经验。