Lahti Ari, Hyltoft Petersen Per, Boyd James C
Department of Clinical Chemistry, Rikshospitalet University Hospital of Oslo, N-0027 Oslo, Norway.
Clin Chem. 2002 Nov;48(11):1987-99.
The aims of this report were to examine how unequal subgroup prevalences in the source population may affect reference interval partitioning decisions and to develop generally applicable guidelines for partitioning gaussian-distributed data.
We recently proposed a new model for partitioning reference intervals when the underlying data distribution is gaussian. This model is based on controlling the proportions of the subgroup distributions that fall outside each of the common reference limits, using the distances between the reference limits of the subgroup distributions as functions to these proportions. We examine the significance of the unequal prevalence effect for the partitioning problem and quantify it for distance partitioning criteria by deriving analytical expressions to express these criteria as a function of the ratio of prevalences. An application example, illustrating various aspects of the importance of the prevalence effect, is also presented.
Dramatic shrinkage of the critical distances between reference limits of the subgroups needed for partitioning was observed as the ratio of prevalences, the larger one divided by the smaller one, was increased from unity. Because of this shrinkage, the same critical distances are not valid for all ratios of prevalences, but specific critical distances should be used for each particular value of this ratio. Although proportion criteria used in determining the need for reference interval partitioning are not dependent on the prevalence effect, this effect should be accounted for when these criteria are being applied by adjusting the sample sizes of the subgroups to make them correspond to the ratio of prevalences.
The prevalences of subgroups in the reference population should be known and observed in the calculations for every reference interval study, irrespective of whether distance or proportion criteria are being used to determine the need for reference interval partitioning. We present detailed methods to account for the prevalences when applying each of these types of criteria. Analytical expressions for the distance criteria, to be used when high precision is needed, and approximate distances, to be used in practical work, are derived. General guidelines for partitioning gaussian distributed data are presented. Following these guidelines and using the new model, we suggest that partitioning can be performed more reliably than with any of the earlier models because the new model not only offers an improved correspondence between the critical distances and the critical proportions, but also accounts for the prevalence effect.
本报告的目的是研究源人群中各亚组患病率的不均衡会如何影响参考区间划分决策，并制定适用于高斯分布数据划分的通用指南。
我们最近提出了一种在基础数据分布为高斯分布时划分参考区间的新模型。该模型基于控制各亚组分布超出每个常见参考限的比例，将亚组分布参考限之间的距离用作这些比例的函数。我们研究了患病率不均衡效应在划分问题中的重要性，并通过推导解析表达式将距离划分标准表示为患病率之比的函数，从而对其进行量化。还给出了一个应用示例，说明患病率效应重要性的各个方面。
当患病率之比（较大值除以较小值）从1开始增大时，观察到划分所需的亚组参考限之间的临界距离急剧缩小。由于这种缩小，相同的临界距离对所有患病率之比都无效，而应针对该比例的每个特定值使用特定的临界距离。尽管用于确定是否需要划分参考区间的比例标准不依赖于患病率效应，但在应用这些标准时，应通过调整亚组样本量使其与患病率之比相对应来考虑该效应。
在每项参考区间研究的计算中，无论使用距离标准还是比例标准来确定是否需要划分参考区间，都应了解并考虑参考人群中亚组的患病率。我们给出了应用每种类型标准时考虑患病率的详细方法。推导了在需要高精度时使用的距离标准的解析表达式以及在实际工作中使用的近似距离。给出了高斯分布数据划分的通用指南。遵循这些指南并使用新模型，我们建议与任何早期模型相比，划分可以更可靠地进行，因为新模型不仅在临界距离和临界比例之间提供了更好的对应关系，还考虑了患病率效应。
