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New model-averaged estimators of concordance correlation coefficients: simulation and application to longitudinal overdispersed Poisson data
Communications in Statistics - Simulation and Computation ( IF 0.8 ) Pub Date : 2021-01-22 , DOI: 10.1080/03610918.2021.1871923
Miao-Yu Tsai, Chao-Chun Lin

Abstract

The concordance correlation coefficient (CCC) is a common tool to assess agreement among multiple observers for continuous and discrete responses. However, previous results in the statistical literature have shown that the CCC estimators may suffer from a bias problem under a misspecified model for normal data. In order to avoid fitting data with a misspecified model, thus yielding biased CCC estimates for longitudinal overdispersed Poisson data, this research proposes new model-averaged estimators of CCC by combining the estimators of the variance components (VC) approach with model selection via corrected conditional Akaike information criterion (CAICC) and corrected conditional Bayesian information criterion (CBICC) measures under extended overdispersed three-way Poisson mixed-effects models. In simulation studies, the performance of the proposed model-averaged estimators is compared with the VC estimators with and without model selection via CAICC and CBICC and other existing model-averaged estimators for longitudinal Poisson and overdispersed Poisson data sets. An application of corticospinal diffusion tensor tractography study is presented for illustration. It can be concluded that the proposed model-averaged approach is a reliable procedure yielding small mean square errors and nominal 95% coverage rates. Therefore, the new model-averaged estimator is more robust to model misspecification than other competitors.



中文翻译:

一致性相关系数的新模型平均估计量:纵向过度分散泊松数据的模拟和应用

摘要

一致性相关系数 (CCC) 是评估多个观察者之间对连续和离散响应的一致性的常用工具。然而,统计文献中的先前结果表明,在错误指定的正常数据模型下,CCC 估计量可能会遇到偏差问题。为了避免使用错误指定的模型拟合数据,从而对纵向过度分散的泊松数据产生有偏差的 CCC 估计,本研究提出了新的 CCC 模型平均估计量,方法是将方差分量 (VC) 方法的估计量与通过校正条件选择的模型选择相结合Akaike 信息准则 (CAICC) 和修正条件贝叶斯信息准则 (CBICC) 在扩展的过度分散三向泊松混合效应模型下的测量。在模拟研究中,将所提出的模型平均估计器的性能与通过 CAICC 和 CBICC 进行模型选择和不进行模型选择的 VC 估计器以及纵向泊松和过度分散泊松数据集的其他现有模型平均估计器进行比较。皮质脊髓扩散张量纤维束成像研究的应用被提出以供说明。可以得出结论,所提出的模型平均方法是一种可靠的程序,产生小的均方误差和标称的 95% 覆盖率。因此,新的模型平均估计器比其他竞争者对模型错误指定更稳健。皮质脊髓扩散张量纤维束成像研究的应用被提出以供说明。可以得出结论,所提出的模型平均方法是一种可靠的程序,产生小的均方误差和标称的 95% 覆盖率。因此,新的模型平均估计器比其他竞争者对模型错误指定更稳健。皮质脊髓扩散张量纤维束成像研究的应用被提出以供说明。可以得出结论,所提出的模型平均方法是一种可靠的程序,产生小的均方误差和标称的 95% 覆盖率。因此,新的模型平均估计器比其他竞争者对模型错误指定更稳健。

更新日期:2021-01-22
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