缺失协变量半参数变换模型下间隔截尾失效时间数据的回归分析。

IF 1.2 4区 数学
Yichen Lou, Mingyue Du
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引用次数: 0

摘要

本文讨论了在随机缺失协变量的情况下,由半参数变换模型产生的间隔截尾失效时间数据的回归分析。我们定义了一种针对区间审查的MAR机制的特定公式,其中观测时间增加了处理缺失协变量的复杂性。为了克服现有方法中存在的局限性和计算挑战,我们提出了一种可以通过使用标准软件轻松实现的多重imputation程序。提出的方法利用每个个体的两个预测分数和由这些分数定义的距离。此外,它利用来自不完全观测的部分信息,因此产生比完全案例分析和逆概率加权方法更有效的估计器。通过广泛的仿真研究来评估该方法的性能,并表明该方法在实际情况下具有良好的性能。最后,我们将提出的方法应用于一项阿尔茨海默病研究,该研究激发了这项工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Regression analysis of interval-censored failure time data under semiparametric transformation models with missing covariates.

This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of covariates that are missing at random (MAR). We define a specific formulation of the MAR mechanism tailored to the interval censoring, where the timing of observation adds complexity to handling missing covariates. To overcome the limitations and computational challenges present in the existing methods, we propose a multiple imputation procedure that can be easily implemented with the use of the standard software. The proposed method makes use of two predictive scores for each individual and the distance defined by these scores. Furthermore, it utilizes partial information from incomplete observations and thus yields more efficient estimators than the complete-case analysis and the inverse probability weighting approach. An extensive simulation study is conducted to assess the performance of the proposed method and indicates that it performs well in practical situations. Finally we apply the proposed approach to an Alzheimer's Disease study that motivated this work.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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