医学研究中重复数据建模的高斯Copula回归方法

R. Karuppusami, Gomathi Sudhakar, Juliya Pearl Joseph Johnson, R. Mariappan, J. Rani, B. Antonisamy, Prasanna S. Premkumar
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引用次数: 0

摘要

在重复测量数据中,观察结果往往在每个主题中都是相关的,并且这些数据通常使用广义估计方程(GEE)进行分析,这对于许多方法持有的假设是稳健的。GEE的主要局限性在于它的估计方法是准似然的。最近的copula框架在处理重复数据方面非常流行。重复数据的最大似然分析可以用高斯耦合回归来实现。本研究的目的是展示高斯copula回归方法对重复数据的处理和分析,并将结果与GEE进行比较。本研究的前瞻性、双盲、随机对照试验数据来自基督教医学院麻醉学系
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Gaussian Copula Regression Approach for Modelling Repeated Data in Medical Research
: In repeated measures data, the observations tend to be correlated within each subject, and such data are often analyzed using Generalized Estimating Equations (GEE), which are robust to assumptions that many methods hold. The main limitation of GEE is that its method of estimation is quasi-likelihood. The recent framework of the copula is very popular for handling repeated data. The maximum likelihood-based analysis for repeated data can be obtained using Gaussian copula regression. The purpose of this study is to show the handling and analysis of the repeated data using the Gaussian copula regression approach and compare the findings with GEE. The prospective, double-blinded, randomized controlled trial data for this study was obtained from the Department of Anesthesia, Christian Medical College
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