課程名稱 |
統計思考 Statistical Thinking |
開課學期 |
110-2 |
授課對象 |
公共衛生學院 公共衛生碩士學位學程 |
授課教師 |
杜裕康 |
課號 |
EPM5001 |
課程識別碼 |
849 U0300 |
班次 |
|
學分 |
2.0 |
全/半年 |
半年 |
必/選修 |
必修 |
上課時間 |
星期四3,4(10:20~12:10) |
上課地點 |
公衛214 |
備註 |
MPH生物統計組領域必修,兩年開課一次。 限學士班三年級以上 總人數上限:40人 |
Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1102EPM5001_ST |
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
|
為確保您我的權利,請尊重智慧財產權及不得非法影印
|
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 participate in the discussion during the class and do an oral presentation on their project and submit a written report at the end of the course. |
預期每週課後學習時數 |
|
Office Hours |
|
指定閱讀 |
• Journal articles
• Yu-Kang Tu, Mark Gilthorpe: “Statistical Thinking in Epidemiology”. Chapman & Hall, 2012.
|
參考書目 |
Bjorn Andersen. Methodological errors in medical research. London: Blackwell,
2000.
Richard J Murnane, John B Willett: “Methods Matter”. Oxford University Press,
2011
Steven Sloman. “Causal Models”. Oxford University Press, 2005.
Judea Pearl. “Causality” 2nd edition. Cambridge University Press, 2010.
Judea Pearl, Dana Mackenzie. The Book of Why: The New Science of Cause and Effect 因果革命:人工智慧的大未來. Penguin, 2018.
Miguel A Hernán, James M Robins. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC, 2020.
Scott Cunningham. Causal Inference: the mixtape. Yale, 2021.
Babette A Brumback. Fundamentals of Causal Inference: With R. Boca Raton: Chapman & Hall/CRC, 2021.
Nick Huntington-Klein. The Effect: An Introduction to Research Design and Causality. Boca Raton: Chapman & Hall/CRC, 2022. |
評量方式 (僅供參考) |
No. |
項目 |
百分比 |
說明 |
1. |
final report |
30% |
|
2. |
midterm presentation |
20% |
|
3. |
final presentation |
30% |
|
4. |
class participation |
20% |
|
|
週次 |
日期 |
單元主題 |
第1週 |
2/17 |
What is “statistical thinking”? 杜裕康老師 |
第2週 |
2/24 |
Causation and directed acyclic graphs (1) 杜裕康老師 |
第3週 |
3/3 |
Causation and directed acyclic graphs (2) 杜裕康老師 |
第4週 |
3/10 |
Vector geometry for linear models 杜裕康老師 |
第5週 |
3/17 |
Mathematical coupling & Regression to the mean 杜裕康老師 |
第6週 |
3/24 |
Why is randomized controlled trial the “gold standard”? 杜裕康老師 |
第7週 |
3/31 |
Lord’s paradox and Simpson’s paradox 杜裕康老師 |
第8週 |
4/7 |
期中報告 |
第9週 |
4/14 |
Propensity scores matching 杜裕康老師 |
第10週 |
4/21 |
特別演講 Propensity scores matching的應用 陳姿婷博士 |
第11週 |
4/28 |
Instrumental variables 杜裕康老師 |
第12週 |
5/5 |
特別演講 Mendelian randomization 陳姿婷博士 |
第13週 |
5/12 |
Regression discontinuity (1) 杜裕康老師 |
第14週 |
5/19 |
Regression discontinuity (2) 杜裕康老師 |
第15週 |
5/26 |
Student presentation (1) |
第16週 |
6/2 |
彈性補充教學 |
第17週 |
6/9 |
Student presentation (2) |
第18週 |
6/16 |
彈性補充教學 |
|