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    題名: A functional inference for multivariate current status data with mismeasured covariate
    作者: Wen, Chi-Chung;Huang, Yih-Huei;Wu, Yuh-Jenn
    關鍵詞: Conditional score;Correlated data;Measurement error;Proportional odds model;Self-consistency
    日期: 2015-07-01
    上傳時間: 2016-04-22 13:11:08 (UTC+8)
    出版者: Springer New York LLC
    摘要: Covariate measurement error problems have been recently studied for current status failure time data but not yet for multivariate current status data. Motivated by the three-hypers dataset from a health survey study, where the failure times for three-hypers (hyperglycemia, hypertension, hyperlipidemia) are subject to current status censoring and the covariate self-reported body mass index may be subject to measurement error, we propose a functional inference method under the proportional odds model for multivariate current status data with mismeasured covariates. The new proposal utilizes the working independence strategy to handle correlated current status observations from the same subject, as well as the conditional score approach to handle mismeasured covariate without specifying the covariate distribution. The asymptotic theory, together with a stable computation procedure combining the Newton–Raphson and self-consistency algorithms, is established for the proposed estimation method. We evaluate the method through simulation studies and illustrate it with three-hypers data.
    關聯: Lifetime Data Analysis 21(3), p.379-396
    DOI: 10.1007/s10985-014-9296-6
    顯示於類別:[數學學系暨研究所] 期刊論文

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