Pseudo-observations for bivariate survival data.

IF 1.4 4区 数学 Q3 BIOLOGY
Biometrics Pub Date : 2025-01-07 DOI:10.1093/biomtc/ujaf006
Yael Travis-Lumer, Micha Mandel, Rebecca A Betensky
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引用次数: 0

Abstract

The pseudo-observations approach has been gaining popularity as a method to estimate covariate effects on censored survival data. It is used regularly to estimate covariate effects on quantities such as survival probabilities, restricted mean life, cumulative incidence, and others. In this work, we propose to generalize the pseudo-observations approach to situations where a bivariate failure-time variable is observed, subject to right censoring. The idea is to first estimate the joint survival function of both failure times and then use it to define the relevant pseudo-observations. Once the pseudo-observations are calculated, they are used as the response in a generalized linear model. We consider 2 common nonparametric estimators of the joint survival function: the estimator of Lin and Ying (1993) and the Dabrowska estimator (Dabrowska, 1988). For both estimators, we show that our bivariate pseudo-observations approach produces regression estimates that are consistent and asymptotically normal. Our proposed method enables estimation of covariate effects on quantities such as the joint survival probability at a fixed bivariate time point or simultaneously at several time points and, consequentially, can estimate covariate-adjusted conditional survival probabilities. We demonstrate the method using simulations and an analysis of 2 real-world datasets.

伪观察法作为一种对删减生存数据的协变量效应进行估计的方法,越来越受到人们的欢迎。它经常用于估算协变量对生存概率、受限平均寿命、累积发病率等量的影响。在这项工作中,我们建议将伪观察方法推广到观察到二元失效时间变量并进行右删减的情况。我们的想法是首先估计两个失效时间的联合生存函数,然后用它来定义相关的伪观测值。计算出伪观测值后,将其用作广义线性模型中的响应。我们考虑了联合生存函数的两个常见非参数估计器:Lin 和 Ying(1993 年)的估计器和 Dabrowska 估计器(Dabrowska,1988 年)。对于这两种估计器,我们的双变量伪观察方法都能产生一致且渐近正态的回归估计值。我们提出的方法可以估计共变因素对数量的影响,如在一个固定的双变量时间点或同时在几个时间点的联合生存概率,并因此可以估计共变因素调整的条件生存概率。我们通过模拟和对两个真实世界数据集的分析来演示该方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biometrics
Biometrics 生物-生物学
CiteScore
2.70
自引率
5.30%
发文量
178
审稿时长
4-8 weeks
期刊介绍: The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.
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