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对存在协变量缺失的现状数据进行分析。

Analysis of current status data with missing covariates.

作者信息

Wen Chi-Chung, Lin Chien-Tai

机构信息

Department of Mathematics, Tamkang University, 151 Ying-Chuan Road, Tamsui 25137, Taiwan.

出版信息

Biometrics. 2011 Sep;67(3):760-9. doi: 10.1111/j.1541-0420.2010.01505.x. Epub 2010 Nov 29.

Abstract

Statistical inference based on right-censored data for the proportional hazards (PH) model with missing covariates has received considerable attention, but interval-censored or current status data with missing covariates has not yet been investigated. Our study is partly motivated by the analysis of fracture data from the 2005 National Health Interview Survey Original Database in Taiwan, where the occurrence of fractures was interval censored and the covariate osteoporosis was not reported for all residents. We assume that the data are realized from a PH model. A semiparametric maximum likelihood estimate implemented by a hybrid algorithm is proposed to analyze current status data with missing covariates. A comparison of the performance of our method with full-cohort analysis, complete-case analysis, and surrogate analysis is made via simulation with moderate sample sizes. The fracture data are then analyzed.

摘要

基于右删失数据对具有缺失协变量的比例风险(PH)模型进行统计推断已受到广泛关注,但对于具有缺失协变量的区间删失数据或当前状态数据尚未进行研究。我们的研究部分源于对台湾2005年国民健康访问调查原始数据库中骨折数据的分析,其中骨折的发生是区间删失的,并且并非所有居民都报告了协变量骨质疏松症。我们假设数据是由一个PH模型生成的。提出了一种通过混合算法实现的半参数最大似然估计方法来分析具有缺失协变量的当前状态数据。通过中等样本量的模拟,将我们的方法与全队列分析、完全病例分析和替代分析的性能进行了比较。然后对骨折数据进行了分析。

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