Exploring the varying covariate effects in proportional odds models with censored data
Exploring the varying covariate effects in proportional odds models with censored data
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DOI：
10.1016/j.jmva.2012.02.013
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发表时间：
2012-08
期刊：
影响因子：
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通讯作者：
Qihua Wang;Xingwei Tong;Liuquan Sun
中科院分区：
文献类型：
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作者：
Qihua Wang;Xingwei Tong;Liuquan Sun
在本文中，我们考虑比例优势模型，该模型允许检验协变量与暴露变量的非线性交互作用的程度，以分析右删失数据。提出了一种估计非线性相互作用(系数函数)和基线函数的局部极大似然方法。我们证明了所提出的估计量是一致的和渐近正态的，并且渐近方差是一致估计的。此外，我们还发展了局部轮廓似然比方法来构造系数函数的置信域。仿真研究评估了所提出的估计器的性能，并比较了基于正态逼近的置信度区域和基于局部轮廓似然比的置信度区域。该方法以斯坦福大学心脏移植数据为例进行了说明。
In this article, we consider a proportional odds model, which allows one to examine the extent to which covariates interact nonlinearly with an exposure variable for analysis of right-censored data. A local maximum likelihood approach is presented to estimate nonlinear interactions (the coefficient functions) and the baseline function. The proposed estimators are shown to be consistent and asymptotically normal with the asymptotic variance estimated consistently. Also, we develop local profile likelihood ratio method to construct confidence region of coefficient functions. Simulation studies are conducted to evaluate the performances of the proposed estimators, and compare the normal approximation based confidence regions and local profile likelihood ratio based confidence regions. The method is illustrated with Stanford heart transplant data.