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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/99699


    Title: Functional inference for interval-censored data in proportional odds model with covariate measurement error
    Authors: Wen, Chi-Chung;Chen, Yi-Hau
    Contributors: 淡江大學數學學系
    Keywords: Conditional score;interval-censoring;measurement error;semiparametric;survival analysis
    Date: 2014-07-01
    Issue Date: 2014-12-10 14:19:41 (UTC+8)
    Publisher: Academia Sinica * Institute of Statistical Science
    Abstract: 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.
    Relation: Statistica Sinica 24(3), pp.1301-1317
    DOI: 10.5705/ss.2013.081
    Appears in Collections:[數學學系暨研究所] 期刊論文

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