不完整長期追蹤順序型資料之群序檢定分析方法

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 學位論文 > Item 987654321/33895

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/33895

题名:	不完整長期追蹤順序型資料之群序檢定分析方法
其它题名:	Group sequential methods for analysis of longitudinal ordinal data with dropouts
作者:	黃怡樺;Huang, Yi-hua
贡献者:	淡江大學統計學系碩士班陳怡如;Chen, Yi-ju
关键词:	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
日期:	2008
上传时间:	2010-01-11 04:39:07 (UTC+8)
摘要:	不完整長期追蹤資料常見於臨床實驗中，Fitzmaurice et al.(2001)針對不完整二元資料，考慮遺失型態為MAR(missing 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，不完整長期追蹤二元資料且當遺失型態為MAR之群序檢定方法時，邏輯斯隨機效果模式的型I誤差機率估計值較GEE模式更接近所設定的顯著水準，而且邏輯斯隨機效果模式比廣義估計方程式模式具有較高的檢定力。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方程式模式於不完整長期追蹤順序型資料，並以模擬研究來比較在不完整資料下，此兩種模式之型I誤差機率和檢定力之差異。 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.
显示于类别:	[統計學系暨研究所] 學位論文

文件中的档案:

档案	大小	格式	浏览次数
	0Kb	Unknown	437	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....