Model-robust inference for continuous threshold regression models.
Model-robust inference for continuous threshold regression models.
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DOI：
10.1111/biom.12623
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发表时间：
2017-06
期刊：
影响因子：
1.9
通讯作者：
Gilbert PB
中科院分区：
文献类型：
--
作者：
Fong Y;Di C;Huang Y;Gilbert PB
我们研究阈值回归模型，该模型允许结果与感兴趣的协变量之间的关系在协变量的阈值范围内变化。我们特别关注连续阈值模型，该模型在阈值处不会出现跳跃。连续阈值回归函数可以提供结果与感兴趣的协变量之间关联的有用总结，因为它们提供了灵活性和简单性之间的平衡。在研究传染病传播的免疫反应生物标志物的合作工作的推动下，我们在本文中研究了连续阈值模型的估计，特别关注模型错误指定下的推理。我们推导了最大似然估计量的极限分布，并提出了 Wald 和检验反演置信区间。我们评估我们方法的有限样本性能，将它们与引导置信区间进行比较，并为从业者提供指导，以在实际数据分析中选择最合适的方法。我们用 HIV-1 免疫相关研究的例子来说明我们的方法的应用。
We study threshold regression models that allow the relationship between the outcome and a covariate of interest to change across a threshold value in the covariate. In particular we focus on continuous threshold models, which experience no jump at the threshold. Continuous threshold regression functions can provide a useful summary of the association between outcome and the covariate of interest, because they offer a balance between flexibility and simplicity. Motivated by collaborative works in studying immune response biomarkers of transmission of infectious diseases, we study estimation of continuous threshold models in this paper with particular attention to inference under model misspecification. We derive the limiting distribution of the maximum likelihood estimator, and propose both Wald and test-inversion confidence intervals. We evaluate finite sample performance of our methods, compare them with bootstrap confidence intervals, and provide guidelines for practitioners to choose the most appropriate method in real data analysis. We illustrate the application of our methods with examples from the HIV-1 immune correlates studies.