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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/120098
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/120098


    Title: Using weighted regression model for estimating cohort effect in age-period contingency table data
    Authors: Tzeng, I-Shiang;Ng, Chau Yee;Chen, Jau-Yuan;Chen, Li-Shya;Wu, Chin-Chieh
    陳麗霞
    Chen, Li-Shya
    Contributors: 統計學系
    Keywords: age-period-cohort;multiphase method;prediction;hepatocellular carcinoma
    Date: 2018-04
    Issue Date: 2018-09-14 16:09:44 (UTC+8)
    Abstract: Go to:
    Background
    Recently, the multiphase method was proposed to estimate cohort effects after removing the effects of age and period in age-period contingency table data. Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver and is strongly associated with cirrhosis, due to both alcohol and viral etiologies. In epidemiology, age-period-cohort (APC) model can be used to describe (or predict) the secular trend in HCC mortality.

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    Results
    The confidence interval (CI) of the weighted estimates was found to be relatively narrow (compared to unweighted estimates). Moreover, for males, the mortality trend reverses itself during 2006–2010 was found from an increasing trend into a slightly deceasing trend. For females, the increasing trend reverses (earlier than males) itself during 2001–2005.

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    Conclusions
    The weighted estimation of the regression model is recommended for the multiphase method in estimating the cohort effects in age-period contingency table data.

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    Impact
    The regression model can be modified through the weighted average estimate of the effects with narrower CI of each cohort.

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    Methods
    After isolating the residuals during the median polish phase, the final phase is to estimate the magnitude of the cohort effects using the regression model of these residuals on the cohort category with the weight equal to the occupied proportion according to the number of death of HCC in each cohort.
    Relation: Oncotarget, 2018, Vol. 9, No. 28, pp: 19826-19835
    PMID: 29731986
    Data Type: article
    DOI 連結: https://doi.org/10.18632/oncotarget.24868
    DOI: 10.18632/oncotarget.24868
    Appears in Collections:[統計學系] 期刊論文

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