Functional inference for interval-censored data in proportional odds model with covariate measurement error

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題名:	Functional inference for interval-censored data in proportional odds model with covariate measurement error
作者:	Wen, Chi-Chung;Chen, Yi-Hau
貢獻者:	淡江大學數學學系
關鍵詞:	Conditional score;interval-censoring;measurement error;semiparametric;survival analysis
日期:	2014-07-01
上傳時間:	2014-12-10 14:19:41 (UTC+8)
出版者:	Academia Sinica * Institute of Statistical Science
摘要:	It is common in regression analysis of failure time data, such as the AIDS Clinical Trail Group (ACTG) 175 clinical trial data, that the failure time (AIDS incidence time) is subject to interval-censoring and the covariate (baseline CD4 count) is subject to measurement error. To perform valid analysis in this setting, we propose a functional inference method under the semiparametric proportional odds model. The proposal utilizes the working independence strategy to handle general mixed case interval censorship, as well as the conditional score approach to handle mismeasured covariate without specifying the covariate distribution. The asymptotic theory, together with a stable computational procedure combining the Newton-Raphson and self-consistency algorithms, is established for the proposed estimation method. We illustrate the performance of the proposal via simulation studies and analysis of ACTG 175 data.
關聯:	Statistica Sinica 24(3), pp.1301-1317
DOI:	10.5705/ss.2013.081
顯示於類別:	[數學學系暨研究所] 期刊論文

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