An improved Bayesian approach to estimating the reference interval from a meta-analysis: Directly monitoring the marginal quantiles and characterizing their uncertainty.
An improved Bayesian approach to estimating the reference interval from a meta-analysis: Directly monitoring the marginal quantiles and characterizing their uncertainty.
复制标题
一种改进的贝叶斯方法，用于根据荟萃分析估计参考区间：直接监测边缘分位数并表征其不确定性。
DOI：
10.1002/jrsm.1624
复制
发表时间：
2023
影响因子：
9.8
通讯作者：
Chu,Haitao
中科院分区：
文献类型：
--
作者：
Siegel,Lianne;Chu,Haitao
参考间隔或参考范围通过在具有代表性的健康人群中包含预先指定的测量比例(例如，95%)来帮助医疗决策。我们最近提出了三种从基于随机效应模型的荟萃分析估计参考区间的方法：频数法、贝叶斯后验预测区间和经验法。由于包含估计不确定性的贝叶斯后验预测区间变得更宽，当研究数量较少或研究间异质性较大时，它可能系统地包含大于95%的测量值。在这种情况下，频率和经验方法也捕捉到了不到95%的测量的中位数，并且没有开发出参考区间界限的95%的置信度或可信区间。在这一更新中，我们描述了如何使用贝叶斯方法来总结研究中个体边际分布的适当分位数(例如，2.5和97.5)，并构建一个可信的区间来描述参考区间下限和上限中的估计不确定性。通过模拟实验证明，该方法能够很好地从边缘分布中获取95%的值，并且即使在研究数量较少或研究间异质性较大的情况下，也能保持接近95%的边缘分布的中位覆盖率。我们还将这种方法的结果与先前提出的三种方法在额部主观姿势垂直测量的荟萃分析的原始案例研究中得到的结果进行了比较。
Reference intervals, or reference ranges, aid medical decision‐making by containing a pre‐specified proportion (e.g., 95%) of the measurements in a representative healthy population. We recently proposed three approaches for estimating a reference interval from a meta‐analysis based on a random effects model: a frequentist approach, a Bayesian posterior predictive interval, and an empirical approach. Because the Bayesian posterior predictive interval becomes wider to incorporate estimation uncertainty, it may systematically contain greater than 95% of measurements when the number of studies is small or the between study heterogeneity is large. The frequentist and empirical approaches also captured a median of less than 95% of measurements in this setting, and 95% confidence or credible intervals for the reference interval limits were not developed. In this update, we describe how one can instead use Bayesian methods to summarize the appropriate quantiles (e.g., 2.5th and 97.5th) of the marginal distribution of individuals across studies and construct a credible interval describing the estimation uncertainty in the lower and upper limits of the reference interval. We demonstrate through simulations that this method performs well in capturing 95% of values from the marginal distribution and maintains a median coverage of near 95% of the marginal distribution even when the number of studies is small, or the between‐study heterogeneity is large. We also compare the results of this method to those obtained from the three previously proposed methods in the original case study of the meta‐analysis of frontal subjective postural vertical measurements.