Nonparametric maximum likelihood analysis of clustered current status data with the gamma frailty Cox model
學年 99
學期 2
出版（發表）日期 2011-02-01
作品名稱 Nonparametric maximum likelihood analysis of clustered current status data with the gamma frailty Cox model
作品名稱（其他語言）
著者 Wen, Chi-chung; Chen, Yi-hau
單位 淡江大學數學學系
出版者 Amsterdam: Elsevier BV
著錄名稱、卷期、頁數 Computational Statistics and Data Analysis 55(2), pp.1053-1060
摘要 The Cox model with frailties has been popular for regression analysis of clustered event time data under right censoring. However, due to the lack of reliable computation algorithms, the frailty Cox model has been rarely applied to clustered current status data, where the clustered event times are subject to a special type of interval censoring such that we only observe for each event time whether it exceeds an examination (censoring) time or not. Motivated by the cataract dataset from a cross-sectional study, where bivariate current status data were observed for the occurrence of cataracts in the right and left eyes of each study subject, we develop a very efficient and stable computation algorithm for nonparametric maximum likelihood estimation of gamma-frailty Cox models with clustered current status data. The algorithm proposed is based on a set of self-consistency equations and the contraction principle. A convenient profile-likelihood approach is proposed for variance estimation. Simulation and real data analysis exhibit the nice performance of our proposal.
關鍵字 Correlated data; Cross-sectional study; Interval censoring; Self-consistency; Proportional hazards
語言 en
ISSN 0167-9473
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Chen, Yi-hau
審稿制度
國別 NLD
公開徵稿
出版型式 紙本
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