A heteroscedastic Bayesian model for method comparison data

IF 0.3 Q4 MATHEMATICS, APPLIED
S. Lakmali, Lakshika S. Nawarathna, P. Wijekoon
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引用次数: 0

Abstract

Abstract When implementing newly proposed methods on measurements taken from a human body in clinical trials, the researchers carefully consider whether the measurements have the maximum accuracy. Further, they verified the validity of the new method before being implemented in society. Method comparison evaluates the agreement between two continuous variables to determine whether those measurements agree on enough to interchange the methods. Special consideration of our work is a variation of the measurements with the magnitude of the measurement. We propose a method to evaluate the agreement of two methods when those are heteroscedastic using Bayesian inference since this method offers a more accurate, flexible, clear, and direct inference model using all available information. A simulation study was carried out to verify the characteristics and accuracy of the proposed model using different settings with different sample sizes. A gold particle dataset was analyzed to examine the practical viewpoint of the proposed model. This study shows that the coverage probabilities of all parameters are greater than 0.95. Moreover, all parameters have relatively low error values, and the simulation study implies the proposed model deals with the higher heteroscedasticity data with higher accuracy than others. In each setting, the model performs best when the sample size is 500.
方法比较数据的异方差贝叶斯模型
当在临床试验中对人体测量实施新提出的方法时,研究人员仔细考虑测量是否具有最大的准确性。此外,他们在将新方法应用于社会之前验证了新方法的有效性。方法比较评估两个连续变量之间的一致性,以确定这些测量是否足够一致以交换方法。我们的工作需要特别考虑的是测量值随测量值大小的变化。我们提出了一种使用贝叶斯推理来评估两种方法在异方差时的一致性的方法,因为这种方法使用所有可用信息提供了更准确、灵活、清晰和直接的推理模型。通过不同的设置和不同的样本量,进行了仿真研究,验证了所提出模型的特性和准确性。通过对一个金颗粒数据集的分析,验证了该模型的实用性。研究表明,各参数的覆盖概率均大于0.95。此外,所有参数的误差值都相对较低,仿真研究表明,该模型对高异方差数据的处理精度高于其他模型。在每种设置中,当样本量为500时,模型表现最佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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