具缺失共變量現狀數據之柯斯回歸

機構典藏 > College of Sciences > Graduate Institute & Department of Mathematics > Research Paper > Item 987654321/54345

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/54345

Title:	具缺失共變量現狀數據之柯斯回歸
Other Titles:	Cox Regression for Current Status Data with Missing Covariates
Authors:	溫啟仲
Contributors:	淡江大學數學學系
Date:	2010
Issue Date:	2011-07-06 00:29:32 (UTC+8)
Abstract:	具缺失共變量之右設限數資料在正比風險模型下的統計推論受到極大關注，但對具缺失共變量之區間設限資料或現狀數據卻未曾研究。研究動機部分來自2005年國民健康調查2670位65~102歲受訪者的骨折資料，其中骨折發生時間為區間設限且共變量：骨質疏鬆並非人人有紀錄。在此計劃中，我們將研究具缺失共變量之現狀數據在正比風險模型下的無母數最大概然估計。此外，我們將建立一新演算法計算估計量。藉由模擬試驗，我們將比較所提方法、全體觀測值分析和完整觀測值分析的數值表現；也將分析骨折資料。 Statistical inference based on the right-censored data for proportional hazard (PH) model with missing covariates has received considerable attention, but interval-censored or current status data with missing covariates are yet to be investigated. Our study is partly motivated by analysis of clinical fractures data from a survey of 2670 Taiwan residents age 65–102 years within 2005 National Health Interview Survey Original Database in Taiwan, where the occurrence of fractures is interval-censored and covariate osteoporosis is not reported for all residents. In this plan, we wish to present a nonparametric maximum likelihood method for analyzing current status data with missing covariates under the Cox proportional hazards model. In addition, we aim to develop a new algorithm to compute the estimates. The comparison of the performance of our method with full-cohort analysis and complete-case analysis will be made via simulation. The clinical fractures data will also be analyzed.
Appears in Collections:	[Graduate Institute & Department of Mathematics] Research Paper

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