区间截尾数据半参数治愈模型的多重插值方法

IF 1.5 3区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computational Statistics & Data Analysis Pub Date : 2016-07-01 DOI:10.1016/j.csda.2016.01.013

Jie Zhou, Jiajia Zhang, Alexander C. McLain, Bo Cai

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引用次数: 15

摘要

比例风险混合治愈模型是一种流行的生存数据分析方法，其中一个亚组患者被治愈。当数据被间隔截除时，由于其复杂的数据结构，该模型的估计具有挑战性。提出了一种多重插值算法，对未治愈患者的治愈概率和生存分布进行参数估计和方差估计。该方法易于在R、SAS等常用统计软件中实现，经综合仿真研究，其性能可与全参数方法相媲美。举例来说，该方法应用于2000-2010年大乔治亚州乳腺癌监测、流行病学和最终结果项目的数据集。

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A multiple imputation approach for semiparametric cure model with interval censored data

The proportional hazards mixture cure model is a popular analysis method for survival data where a subgroup of patients are cured. When the data are interval censored, the estimation of this model is challenging due to its complex data structure. A multiple imputation algorithm is proposed to obtain parameter and variance estimates for both the cure probability and the survival distribution of the uncured patients. The proposed approach can be easily implemented in commonly used statistical softwares, such as R and SAS, and its performance is comparable to fully parametric methods via comprehensive simulation studies. For illustration, the approach is applied to the 2000–2010 Greater Georgia breast cancer data set from the Surveillance, Epidemiology, and End Results Program.

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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 [...]