課程名稱 
統計思考 Statistical Thinking 
開課學期 
1042 
授課對象 
公共衛生學院 流行病學與預防醫學研究所 
授課教師 
杜裕康 
課號 
EPM5001 
課程識別碼 
849 U0300 
班次 

學分 
2 
全/半年 
半年 
必/選修 
選修 
上課時間 
星期一6,7(13:20~15:10) 
上課地點 
公衛213 
備註 
限研究生和大三，大四學生選修。 總人數上限：40人 
Ceiba 課程網頁 
http://ceiba.ntu.edu.tw/1042EPM5001_ST105 
課程簡介影片 

核心能力關聯 
本課程尚未建立核心能力關連 
課程大綱

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課程概述 
It is essential for graduate students to develop critical thinking in order to identify caveat in current research, develop new approaches to existing research problems and explore new horizon in their research fields. Students would find it very helpful, if real examples in clinical and epidemiological research are used to demonstrate how to achieve these goals, and this is the aim of this course. The syllabus of this course will loosely follow the order of chapters in my new book of the same title published by Chapman & Hall this summer with supplementary materials. Two tools to promote understanding of statistical modelling will be first introduced: vector geometry for linear models and the directed acyclic graphs for causal thinking. Then, background knowledge will be provided for students to identify the potential caveats and to use their previous knowledge to choose the most appropriate approaches or to develop new approaches. Potential scenarios and examples used in this course include:
• Testing the relation between baseline and changes
• The use of ratio variables in regression analysis
• Statistical methods for testing differences in changes in randomised controlled trials
• Lord’s paradox and the adjustment of baseline values in observational studies
• The problem of collinearity in linear models
• Testing statistical and biological interaction
• Confounding, causality and Simpson’s paradox
• Reversal paradox and the adjustment of intermediate variables on a causal pathway
• Testing direct, indirect and total effects
Students will be asked to give presentations to discuss the reading materials and lead discussion. Students’ performance will be assessed by their participation in the classroom discussion, their presentations and the final essays on one of the topics discussed in the course.

課程目標 
By the end of this course, students should be able to:
• Describe the rationales of directed acyclic graphs in causal inference
• Describe how to use vector geometry to represent linear models
• Describe the problems with mathematical coupling in testing associations between variables
• Explain the problem of regression to the mean with assessing the impact of baseline measurements on changes from baseline
• Explain the potential problems caused by imbalance in baseline covariates in assessing group differences in changes from baseline
• Describe the strength and limitations in methods for adjusting baseline imbalance
• Describe the conceptual relations between biological and statistical interaction
• Explain the causes and potential solutions to the problem of reversal paradox
• Explain the different approaches to separate direct and indirect effects and their underlying assumptions

課程要求 
Students are required to do a midterm and final presentation on their projects and submit final group project at the end of the course. 
預期每週課後學習時數 

Office Hours 
另約時間 
指定閱讀 
• Journal articles
• YuKang Tu, Mark Gilthorpe: “Statistical Thinking in Epidemiology”. Chapman & Hall, 2012.

參考書目 
• Judea Pearl. “Causality” 2nd edition. Cambridge University Press, 2010.
• Bjorn Andersen. Methodological errors in medical research. London: Blackwell, 2000.
• Steven Sloman. “Causal Models”. Oxford University Press, 2005.

評量方式 (僅供參考) 
No. 
項目 
百分比 
說明 
1. 
class participation 
20% 

2. 
oral presentation 
30% 

3. 
final exam 
20% 

4. 
written report 
30% 


週次 
日期 
單元主題 
第1週 
02/22 
What is “statistical thinking”? 杜裕康 
第2週 
02/29 
補假 
第3週 
03/07 
Causation and directed acyclic graphs (1) 
第4週 
03/14 
Causation and directed acyclic graphs (2) 
第5週 
03/21 
Vector geometry for linear models 
第6週 
03/28 
Why is randomized controlled trial the “gold standard”? 
第7週 
04/04 
放假 
第8週 
04/11 
Lord’s paradox and Simpson’s paradox 
第9週 
04/18 
Regression to the mean 
第10週 
04/25 
Propensity scores 
第11週 
05/02 
Instrumental variables (1) 
第12週 
05/09 
Instrumental variables (2) 
第13週 
05/16 
Regression discontinuity design (1) 
第14週 
05/23 
Regression discontinuity design (2) 
第15週 
05/30 
Student presentation (1) 
第16週 
06/06 
Student presentation (2) 
第17週 
06/13 
Student presentation (3) 
第18週 
06/20 
Student presentation (4) 
