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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/33895

    Title: 不完整長期追蹤順序型資料之群序檢定分析方法
    Other Titles: Group sequential methods for analysis of longitudinal ordinal data with dropouts
    Authors: 黃怡樺;Huang, Yi-hua
    Contributors: 淡江大學統計學系碩士班
    陳怡如;Chen, Yi-ju
    Keywords: 廣義線性混合模式;廣義估計方程式模式;長期追蹤研究;遺失資料;順序型反應變數;Generalized estimating equations model;Generalized linear mixed model;Longitudinal study;Missing data;Ordinal response
    Date: 2008
    Issue Date: 2010-01-11 04:39:07 (UTC+8)
    Abstract: 不完整長期追蹤資料常見於臨床實驗中,Fitzmaurice et
    at random)時,比較不同形式GEE參數估計值之影響,其結果顯示
    Liang and Zeger(1986)所提出一般GEE方法隨著遺失比率增加會產
    生較大偏誤。此外,Spiessens et al.(2003)模擬結果指出,不完

    本文著重在討論不同遺失型態為MCAR(missing completely
    at random)與MAR之情況下,應用廣義線性混合模式和廣義估計
    Longitudinal studies with dropouts are commonly occurred in clinical trials. For the incomplete binary data, Fitzmaurice et al. (2001) discussed the impact on bias of direrent estimating equation methods where missing data follow a MAR (missing at random) process. They pointed out that generalization estimating equations (GEE) proposed by Liang and Zeger (1986) has manifest bias as the MAR dropout rate increases. Spiessens et al. (2003) conducted the group sequential tests for analyzing longitudinal binary data with MAR and MCAR (missing completely at random) dropouts, and compared the performance of logistic random exect models and GEE models in terms of type I error rate and power. The simulation studies indicated that logistic random exect models have noticeably larger power than GEE models for MAR dropouts data.

    In this article, we consider the group sequential tests based on GLMM (generalized linear mixed model) and GEE models for incomplete longitudinal ordinal data, and compare the two methods with respect to type I
    error rate and power for various dropout rates by simulation studies.
    Appears in Collections:[統計學系暨研究所] 學位論文

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