左截右删竞争风险数据累积关联函数的回归建模:一种改进的伪观测方法。

IF 0.8 4区 数学 Q4 STATISTICS & PROBABILITY
Rong Rong, Jing Ning, Hong Zhu
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引用次数: 0

摘要

对于给定左截右截的竞争风险数据的累积关联函数(CIF),已经开发了回归建模的统计方法。然而,现有的方法通常涉及复杂的加权估计方程或非参数条件似然函数,并且通常需要限制性的假设,即审查和/或截断时间与故障时间无关。伪观测(pseudo-observation, PO)方法在协变量独立和协变量相关两种情况下对右截尾竞争风险数据进行了CIF回归建模。我们将这种方法扩展到左截断右截断的竞争风险数据,并提出在一般截断和审查机制下,基于POs直接建模CIF。我们通过将协变量调整的权重纳入CIF的逆概率加权(IPW)估计量来调整协变量相关的截断和/或协变量相关的审查。我们在合理的模型假设和规则条件下推导了所提出的估计器的大样本性质,并通过各种场景下的模拟研究评估了它们的有限样本性能。我们将提出的方法应用于一项暴露于香豆素衍生物的妊娠队列研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regression Modeling of Cumulative Incidence Function for Left-Truncated Right-Censored Competing Risks Data: A Modified Pseudo-observation Approach.

Statistical methods have been developed for regression modeling of the cumulative incidence function (CIF) given left-truncated right-censored competing risks data. Nevertheless, existing methods typically involve complicated weighted estimating equations or nonparametric conditional likelihood function and often require a restrictive assumption that censoring and/or truncation times are independent of failure time. The pseudo-observation (PO) approach has been used in regression modeling of CIF for right-censored competing risks data under covariate-independent censoring or covariate-dependent censoring. We extend this approach to left-truncated right-censored competing risks data and propose to directly model the CIF based on POs, under general truncation and censoring mechanisms. We adjust for covariate-dependent truncation and/or covariate-dependent censoring by incorporating covariate-adjusted weights into the inverse probability weighted (IPW) estimator of the CIF. We derive large sample properties of the proposed estimators under reasonable model assumptions and regularity conditions and assess their finite sample performances by simulation studies under various scenarios. We apply the proposed method to a cohort study on pregnancy exposed to coumarin derivatives.

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来源期刊
CiteScore
2.00
自引率
12.50%
发文量
320
审稿时长
7.5 months
期刊介绍: The Theory and Methods series intends to publish papers that make theoretical and methodological advances in Probability and Statistics. New applications of statistical and probabilistic methods will also be considered for publication. In addition, special issues dedicated to a specific topic of current interest will also be published in this series periodically, providing an exhaustive and up-to-date review of that topic to the readership.
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