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首页> 外文期刊>Biometrics: Journal of the Biometric Society : An International Society Devoted to the Mathematical and Statistical Aspects of Biology >Testing the correlation for clustered categorical and censored discrete time-to-event data when covariates are measured without/with errors.
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Testing the correlation for clustered categorical and censored discrete time-to-event data when covariates are measured without/with errors.

机译：当协变量测量有无误差时，测试聚类的分类数据和删失的离散事件时间数据的相关性。

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摘要

In the analysis of clustered categorical data, it is of common interest to test for the correlation within clusters, and the heterogeneity across different clusters. We address this problem by proposing a class of score tests for the null hypothesis that the variance components are zero in random effects models, for clustered nominal and ordinal categorical responses. We extend the results to accommodate clustered censored discrete time-to-event data. We next consider such tests in the situation where covariates are measured with errors. We propose using the SIMEX method to construct the score tests for the null hypothesis that the variance components are zero. Key advantages of the proposed score tests are that they can be easily implemented by fitting standard polytomous regression models and discrete failure time models, and that they are robust in the sense that no assumptions need to be made regarding the distributions of the random effects and the unobserved covariates. The asymptotic properties of the proposed tests are studied. We illustrate these tests by analyzing two data sets and evaluate their performance with simulations.
机译：在分析聚类分类数据时，测试聚类内的相关性以及不同聚类之间的异质性是普遍关注的问题。通过针对随机假设模型中方差成分为零的零假设（针对聚类名义和有序分类响应）提出一类评分测试，以解决此问题。我们扩展结果以适应聚类的删节离散事件时间数据。接下来，我们在协变量被测量为误差的情况下考虑进行此类检验。我们建议使用SIMEX方法构造方差分量为零的零假设的得分测试。拟议的分数测试的主要优点在于，可以通过拟合标准的多变量回归模型和离散故障时间模型轻松实现它们，并且在不需要对随机效应和误差分布进行假设的情况下，它们很健壮。未观察到的协变量。研究了拟议测试的渐近性质。我们通过分析两个数据集来说明这些测试，并通过仿真评估它们的性能。

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