A Guide to Estimating the Reference Range From a Meta-Analysis Using Aggregate or Individual Participant Data.
A Guide to Estimating the Reference Range From a Meta-Analysis Using Aggregate or Individual Participant Data.
复制标题
使用聚合或个体参与者数据进行荟萃分析估计参考范围的指南。
DOI：
10.1093/aje/kwac013
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发表时间：
2022
影响因子：
5
通讯作者：
Chu,Haitao
中科院分区：
文献类型：
--
作者：
Siegel,Lianne;Murad,MHassan;Riley,RichardD;Bazerbachi,Fateh;Wang,Zhen;Chu,Haitao
临床医生经常必须确定患者的测量结果是否反映了健康的“正常”个体。因此，参考范围被定义为健康人群中某个比例（通常为95%）的测量值预期会下降的区间。人们可以从一项研究中估计它，或者最好从多项研究的荟萃分析中估计它，以增加普遍性。该范围不同于荟萃分析中汇总平均值的置信区间和新研究平均值的预测区间，后者未捕获健康个体间的自然变异。最近提出了从综合数据的荟萃分析中估计参考范围的方法，该荟萃分析包括研究内和研究间的变化。在本指南中，我们提出了3种估计参考范围的方法：一种是频率论，一种是贝叶斯，一种是经验论。每种方法都可以应用于汇总或个体参与者数据荟萃分析，后者是可用的黄金标准。我们说明了这些方法在先前发表的个人参与者数据荟萃分析中的应用，这些数据来自2006年至2016年期间通过瞬态弹性成像测量健康个体肝脏硬度的研究。
Clinicians frequently must decide whether a patient’s measurement reflects that of a healthy “normal” individual. Thus, the reference range is defined as the interval in which some proportion (frequently 95%) of measurements from a healthy population is expected to fall. One can estimate it from a single study or preferably from a meta-analysis of multiple studies to increase generalizability. This range differs from the confidence interval for the pooled mean and the prediction interval for a new study mean in a meta-analysis, which do not capture natural variation across healthy individuals. Methods for estimating the reference range from a meta-analysis of aggregate data that incorporates both within- and between-study variations were recently proposed. In this guide, we present 3 approaches for estimating the reference range: one frequentist, one Bayesian, and one empirical. Each method can be applied to either aggregate or individual-participant data meta-analysis, with the latter being the gold standard when available. We illustrate the application of these approaches to data from a previously published individual-participant data meta-analysis of studies measuring liver stiffness by transient elastography in healthy individuals between 2006 and 2016.