非同步遠距教學。歡迎大學部高年級生選修。

/* 本課程不開放旁聽，5/30 與 6/6 下午 1:20-3:10 於公衛 101 講堂實體上課，其它週皆為非同步遠距教學 */ 在生物醫學領域，研究者常需處理長期追蹤的資料，例如：分析病人數次回診的血壓值。由於忽略各筆觀測值之間的相依性，傳統的廣義線性模式並不能直接使用。於本門課中，我們將介紹隨機效應模式(random effects models)、廣義估計方程式(generalized estimating equations)、廣義線性混合模式(generalized linear mixed effects models)與轉銜模式(transition models)。除介紹統計理論模式外，將搭配統計軟體SAS或R來處理重複測量的資料。 In the Biomedical field, we usually need to deal with longitudinal data, say, the blood pressure levels measured at several clinical visits. The conventional generalized linear models cannot be directly used because of ignoring the dependence among measurements. This course will introduce random effects models, generalized estimating equations (GEEs), generalized linear mixed effects models (GLMMs), and transition models. In addition to statistical modeling, we will teach how to use SAS or R to analyze longitudinal data.

/* 本課程不開放旁聽，5/30 與 6/6 下午 1:20-3:10 於公衛 101 講堂實體上課，其它週皆為非同步遠距教學 */ 於此課程結束，同學預期將能 At the end of the course, the students are expected to A具備自行研讀統計方法文獻的能力，並有能力重複文獻當中統計分析的結果，以及辨別及校正常見的統計分析錯誤。 Read, understand, and reproduce the results in statistical literature, and to find and correct common mistakes when performing statistical analyses. B能使用圖表協助資料的呈現，並能了解、描述、執行不同的統計推論，如估計與檢定等分析工具，並能清楚說明適用之時機。 Use graphics and tables for data representation, and to understand, describe, and conduct different statistical procedures.

同學需先修過至少一門的 (生物) 統計學或流行病學課程。 Prerequisite course: at least one course in biostatistics (or statistics) or epidemiology.

課程講義 Teachers' handouts

1. Analysis of Correlated Data with SAS and R, 3rd edition. Shoukri, M. M. and Chaudhary M. A. (2007). Chapman & Hall/CRC. 2. Diggle, P.J., Heagerty, P.J., Liang, K.Y., & Zeger, S.L. (2002). Analysis of Longitudinal Data. 2nd Ed. Oxford University Press.