一般線性模型於醫學研究的應用

有感於不少醫護研究者對一般線性模型認識不深，未能善加利用此方法於生物醫學研究上，本文作者於是希望在這裡以一個典型醫學研究的例子，來說明如何可使用一般線性模型進行如重覆測量變方分析等種種統計分析，及解決一些如遺漏數據等常見的應用問題。在解說的過程中，我們更把統計軟體SPSS內一般線性模型的操作過程加以說明，並闡釋如何判讀其報表，藉此協助讀者們了解，以期日後能實際應用於研究計劃中。

關鍵字

一般線性模型；遺漏數據；固定及隨機效果

並列摘要

General Linear Model (GLM) is a statistical method that can be applied to analyze data generated from different fields of medical research. However, accordingly to our consulting experience, bio-medical researchers do not possess adequate understanding of the technique. Therefore, we present a hypothetical data set similar to those generated by intervention studies to illustrate the use of GLM for various analyses such as repeated measures of analysis of variance. We also examine the usefulness of GLM when there are missing observations in the data set, a problem widely encountered in follow-up studies. Throughout the article, we demonstrate the procedures required to run GLM using the commonly available statistical software package, SPSS, and explain how the outputs should be interpreted. Readers will be equipped with the basic skills to analyze their own research data using GLM after reading this manuscript.

並列關鍵字

general linear model ； missing data ； fixed and random effect

國際替代計量

一般線性模型於醫學研究的應用

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