不完整長期追蹤順序型資料之群序檢定分析方法

Group Sequential Methods for Analysis of Longitudinal Ordinal Data with Dropouts

黃怡樺(Yi-Hua Huang)

指導教授 ： 陳怡如

淡江大學/管理學院/統計學系碩士班/碩士(2008年)

https://doi.org/10.6846/TKU.2008.00828

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。

關鍵字

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

並列摘要

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.

並列關鍵字

Generalized estimating equations model ； Generalized linear mixed model ； Longitudinal study ； Missing data ； Ordinal response

參考文獻

[25]林鍵志(民95)。順序型長期追蹤資料之群序檢定方法。淡江大學統計學系應用統計碩士班碩士論文。

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