Estimation of semiparametric probit model based on case-cohort interval-censored failure time data

IF 1.6 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2025-08-10 DOI:10.1016/j.csda.2025.108266

Mingyue Du, Ricong Zeng

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Abstract

The estimation of semiparametric probit model is discussed for the situation where one observes interval-censored failure time data arising from case-cohort studies. The probit model has recently attracted some attention for regression analysis of failure time data partly due to the popularity of the normal distribution and its similarity to linear models. Although some methods have been developed in the literature for its estimation, it does not seem to exist an established approach for the situation of case-cohort interval-censored data. To address this, a pseudo-maximum likelihood method is proposed and furthermore, an EM algorithm is developed for its implementation. The resulting estimators of regression parameters are shown to be consistent and asymptotically follow the normal distribution. To assess the empirical performance of the proposed method, a simulation study is conducted and indicates that it works well in practical situations. In addition, it is applied to a set of real data arising from an AIDS clinical trial that motivated this study.

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基于病例队列间隔截尾失效时间数据的半参数概率模型估计

针对病例队列研究中出现的间隔截尾失效时间数据，讨论了半参数概率模型的估计问题。probit模型近年来在故障时间数据的回归分析中引起了一些关注，部分原因是由于正态分布的普及及其与线性模型的相似性。虽然文献中已经开发了一些方法来估计它，但对于病例队列间隔审查数据的情况，似乎没有一种既定的方法。为了解决这个问题，提出了伪极大似然方法，并进一步开发了一种EM算法来实现它。结果表明，回归参数的估计量是一致的，并且渐近地服从正态分布。为了评估该方法的经验性能，进行了仿真研究，并表明该方法在实际情况下效果良好。此外，它被应用于一组来自艾滋病临床试验的真实数据，这些临床试验激发了本研究。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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 [...]