A multiple imputation approach for flexible modelling of interval-censored data with missing and censored covariates

IF 1.5 3区 数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yichen Lou , Yuqing Ma , Liming Xiang , Jianguo Sun
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引用次数: 0

Abstract

This paper discusses regression analysis of interval-censored failure time data that commonly occur in biomedical studies among others. For the situation, the failure event of interest is known only to occur within an interval instead of being observed exactly. In addition to interval censoring on the failure time of interest, sometimes covariates may be missing or suffer censoring, which can bring extra theoretical and computational challenges for the regression analysis. To deal with such data, we propose a novel multiple imputation approach with the use of the rejection sampling under a class of semiparametric transformation models. The proposed method is flexible and can lead to more efficient estimation than the existing methods, and the resulting estimators are shown to be consistent and asymptotically normal. An extensive simulation study is conducted and demonstrates that the proposed approach works well in practice. Finally, we apply the proposed approach to a set of real data on Alzheimer's disease that motivated this study.
一种具有缺失协变量和被删协变量的区间截尾数据灵活建模的多重插值方法
本文讨论了生物医学研究中常见的间隔截尾失效时间数据的回归分析。对于这种情况,所关心的故障事件只知道在一个间隔内发生,而不是精确地观察到。除了对感兴趣的失效时间进行区间审查之外,有时协变量可能会丢失或遭受审查,这给回归分析带来了额外的理论和计算挑战。为了处理这类数据,我们在一类半参数变换模型下提出了一种利用拒绝抽样的多重输入方法。该方法具有较强的灵活性和较好的估计效率,所得到的估计量具有一致性和渐近正态性。进行了广泛的仿真研究,并证明了该方法在实践中是有效的。最后,我们将提出的方法应用于激发本研究的阿尔茨海默病的一组真实数据。
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来源期刊
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis 数学-计算机:跨学科应用
CiteScore
3.70
自引率
5.60%
发文量
167
审稿时长
60 days
期刊介绍: Computational Statistics and Data Analysis (CSDA), an Official Publication of the network Computational and Methodological Statistics (CMStatistics) and of the International Association for Statistical Computing (IASC), is an international journal dedicated to the dissemination of methodological research and applications in the areas of computational statistics and data analysis. The journal consists of four refereed sections which are divided into the following subject areas: I) Computational Statistics - Manuscripts dealing with: 1) the explicit impact of computers on statistical methodology (e.g., Bayesian computing, bioinformatics,computer graphics, computer intensive inferential methods, data exploration, data mining, expert systems, heuristics, knowledge based systems, machine learning, neural networks, numerical and optimization methods, parallel computing, statistical databases, statistical systems), and 2) the development, evaluation and validation of statistical software and algorithms. Software and algorithms can be submitted with manuscripts and will be stored together with the online article. II) Statistical Methodology for Data Analysis - Manuscripts dealing with novel and original data analytical strategies and methodologies applied in biostatistics (design and analytic methods for clinical trials, epidemiological studies, statistical genetics, or genetic/environmental interactions), chemometrics, classification, data exploration, density estimation, design of experiments, environmetrics, education, image analysis, marketing, model free data exploration, pattern recognition, psychometrics, statistical physics, image processing, robust procedures. [...] III) Special Applications - [...] IV) Annals of Statistical Data Science [...]
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