使用Clayton-Oakes模型的半参数版本对聚类区间截尾二变量生存数据的估计和检验及其应用。

IF 1.2 3区 数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Lifetime Data Analysis Pub Date : 2023-10-01 Epub Date: 2023-01-20 DOI:10.1007/s10985-022-09588-y
Bernard Rosner, Camden Bay, Robert J Glynn, Gui-Shuang Ying, Maureen G Maguire, Mei-Ling Ting Lee
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引用次数: 0

摘要

Kaplan-Meier估计器广泛用于估计时间到事件数据的生存概率。它是非参数的,因此不需要指定生存分布,但它确实假设任何时间t的风险集由独立的观察结果组成。这一假设不适用于来自配对器官系统的数据,如眼科(眼睛)或耳鼻喉科(耳朵)的数据,或其他类型的聚类数据。在这篇文章中,我们估计了聚类数据设置中的边际生存概率,并为这些估计提供了置信极限,其中聚类内相关性由Clayton-Oakes模型的区间截尾版本解释。我们为一般的二元区间截尾数据开发了一个拟合优度检验,并将其应用于所提出的Clayton-Oakes模型的区间截尾版本。我们还提出了一种似然比检验,用于在假设组间风险比不变的情况下,在聚类数据的情况下比较两组之间的生存分布。这种方法既可以用于平衡和不平衡的集群大小,也可以用于集群大小具有信息性的情况。我们将我们的检验与基于边际Cox比例风险模型的普通对数秩检验和林伟(LW)检验进行了比较,该模型具有从三明治估计器获得的鲁棒标准误差。仿真结果表明,普通对数秩检验过度膨胀了I型误差,而所提出的无条件似然比检验具有适当的I型误差和比LW检验更高的幂。Sorbinil视网膜病变试验和年龄相关性黄斑变性研究的实际例子证明了该方法。提供了这两项试验的原始数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Estimation and testing for clustered interval-censored bivariate survival data with application using the semi-parametric version of the Clayton-Oakes model.

Estimation and testing for clustered interval-censored bivariate survival data with application using the semi-parametric version of the Clayton-Oakes model.

The Kaplan-Meier estimator is ubiquitously used to estimate survival probabilities for time-to-event data. It is nonparametric, and thus does not require specification of a survival distribution, but it does assume that the risk set at any time t consists of independent observations. This assumption does not hold for data from paired organ systems such as occur in ophthalmology (eyes) or otolaryngology (ears), or for other types of clustered data. In this article, we estimate marginal survival probabilities in the setting of clustered data, and provide confidence limits for these estimates with intra-cluster correlation accounted for by an interval-censored version of the Clayton-Oakes model. We develop a goodness-of-fit test for general bivariate interval-censored data and apply it to the proposed interval-censored version of the Clayton-Oakes model. We also propose a likelihood ratio test for the comparison of survival distributions between two groups in the setting of clustered data under the assumption of a constant between-group hazard ratio. This methodology can be used both for balanced and unbalanced cluster sizes, and also when the cluster size is informative. We compare our test to the ordinary log rank test and the Lin-Wei (LW) test based on the marginal Cox proportional Hazards model with robust standard errors obtained from the sandwich estimator. Simulation results indicate that the ordinary log rank test over-inflates type I error, while the proposed unconditional likelihood ratio test has appropriate type I error and higher power than the LW test. The method is demonstrated in real examples from the Sorbinil Retinopathy Trial, and the Age-Related Macular Degeneration Study. Raw data from these two trials are provided.

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来源期刊
Lifetime Data Analysis
Lifetime Data Analysis 数学-数学跨学科应用
CiteScore
2.30
自引率
7.70%
发文量
43
审稿时长
3 months
期刊介绍: The objective of Lifetime Data Analysis is to advance and promote statistical science in the various applied fields that deal with lifetime data, including: Actuarial Science – Economics – Engineering Sciences – Environmental Sciences – Management Science – Medicine – Operations Research – Public Health – Social and Behavioral Sciences.
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