论文标题
估计序列数据的群内相关性
Estimating intracluster correlation for ordinal data
论文作者
论文摘要
目的：在本文中，我们考虑序列数据内集群中相关性的估计。我们专注于纯音调听力阈值数据，其中阈值以5分贝的增量进行测量。我们估计了基于iPhone的听力评估应用程序测试的群内相关性，以衡量测试/重新测试可靠性。方法：我们提出了一种使用混合效应累积逻辑和概率模型来估计簇内相关性的方法，该模型假定结果数据是有序的。这与使用混合效应线性模型相反，该模型假设结果数据是连续的。结果：在模拟研究中，我们表明，使用混合效应线性模型来估计顺序数据的群内相关性会导致负有限的样本偏差，同时使用混合效应累积逻辑或概率模型减少了这种偏见。与使用混合效应线性模型相比，使用混合效应累积逻辑和概率模型时，基于iPhone的听力评估应用的估计集群内相关性更高。结论：当数据是序数时，使用混合效应累积逻辑或概率模型会减少相对于使用混合效应线性模型的簇内相关估计的偏差。
Purpose: In this paper we consider the estimation of intracluster correlation for ordinal data. We focus on pure-tone audiometry hearing threshold data, where thresholds are measured in 5 decibel increments. We estimate the intracluster correlation for tests from iPhone-based hearing assessment application as a measure of test/retest reliability. Methods: We present a method to estimate the intracluster correlation using mixed effects cumulative logistic and probit models, which assume the outcome data are ordinal. This contrasts with using a mixed effects linear model which assumes that the outcome data are continuous. Results: In simulation studies we show that using a mixed effects linear model to estimate the intracluster correlation for ordinal data results in a negative finite sample bias, while using mixed effects cumulative logistic or probit models reduces this bias. The estimated intracluster correlation for the iPhone-based hearing assessment application is higher when using the mixed effects cumulative logistic and probit models compared to using a mixed effects linear model. Conclusion: When data are ordinal, using mixed effects cumulative logistic or probit models reduces the bias of intracluster correlation estimates relative to using a mixed effects linear model.
