Nonparametric tests for multistate processes with clustered data

Bakoyannis Giorgos; Bandyopadhyay Dipankar

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Nonparametric tests for multistate processes with clustered data

机译：使用聚类数据对多状态过程进行非参数检验

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In this work, we propose nonparametric two-sample tests for population-averaged transition and state occupation probabilities for continuous-time and finite state space processes with clustered, right-censored, and/or left-truncated data. We consider settings where the two groups under comparison are independent or dependent, with or without complete cluster structure. The proposed tests do not impose assumptions regarding the structure of the within-cluster dependence and are applicable to settings with informative cluster size and/or non-Markov processes. The asymptotic properties of the tests are rigorously established using empirical process theory. Simulation studies show that the proposed tests work well even with a small number of clusters, and that they can be substantially more powerful compared to the only, to the best of our knowledge, previously proposed nonparametric test for this problem. The tests are illustrated using data from a multicenter randomized controlled trial on metastatic squamous-cell carcinoma of the head and neck.

机译：在这项工作中，我们提出了非参数双样本检验，用于具有聚类、右删失和/或左截断数据的连续时间和有限状态空间过程的总体平均过渡和状态占领概率。我们考虑比较的两个组是独立的或依赖的设置，有或没有完整的集群结构。所提出的测试不对簇内依赖关系的结构进行假设，并且适用于具有信息性簇大小和/或非马尔可夫过程的设置。测试的渐近性质是使用经验过程理论严格建立的。仿真研究表明，即使使用少量聚类，所提出的测试也能很好地工作，并且据我们所知，与之前针对此问题提出的非参数测试相比，它们可以更强大。这些测试使用一项关于转移性头颈部鳞状细胞癌的多中心随机对照试验的数据进行说明。

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来源
《Annals of the Institute of Statistical Mathematics》 |2022年第5期|837-867|共31页
作者
Bakoyannis Giorgos; Bandyopadhyay Dipankar;
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作者单位

Indiana Univ;

Virginia Commonwealth Univ;

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原文格式 PDF
正文语种英语
中图分类数理统计;
关键词
Cluster randomized trial; Informative cluster size; Multistate model; Multicenter; Two-sample test; STAGE OCCUPATION PROBABILITIES; INTEGRATED TRANSITION HAZARDS; REGRESSION; INFERENCE; MODEL; TRIALS; SIZE;

机译：整群随机试验;信息簇大小;多状态模型;多中心;双样本测试;舞台占用概率;综合过渡危险;回归;推理;型号;试用;大小;

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Nonparametric tests for multistate processes with clustered data

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