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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105922

    Title: A functional inference for multivariate current status data with mismeasured covariate
    Authors: Wen, Chi-Chung;Huang, Yih-Huei;Wu, Yuh-Jenn
    Keywords: Conditional score;Correlated data;Measurement error;Proportional odds model;Self-consistency
    Date: 2015-07
    Issue Date: 2016-04-22 13:11:08 (UTC+8)
    Publisher: Springer New York LLC
    Abstract: 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.
    Relation: Lifetime Data Analysis 21(3), p.379-396
    DOI: 10.1007/s10985-014-9296-6
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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