進階流行病學研究設計
Advanced Study Designs in Epidemiology
| 節 | 週四 |
|---|---|
7 15:30–16:20 | 進階流行病學研究設計 YT202（陽明） 3 節連堂 |
8 16:30–17:20 | |
9 17:30–18:20 |
* 根據陽明交大上課時間表所列
Course organization: The course consists of a series of lectures and laboratory exercise sessions. Active participation is required. Lectures Lecture handouts will be posted to the course website before lectures. Lab exercises (in-class discussion) Lab exercises are performed in groups of students who work at a table together. Students will be assigned to a lab group for this course. Attendance at all laboratory sessions is required. Lab exercises will be posted on the course website the week prior to the start of each lab. Lab assignments will be posted on the course website (NewE3) a week ahead of the day of lab session. Each lab group will be given time to discuss the exercise, using the questions in the exercise as a guide. After discussion, there will be an open discussion of the laboratory exercise for the entire section. If you cannot participate in a lab session, you need to submit your answers to the TA before the lab starts.
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Following the introductory epidemiology course, this advanced course is designed to expand students’ scientific understandings of modern epidemiology, and emphasizes the practices of epidemiologic data analysis and interpretation. This course covers: advanced concepts in epidemiologic causal inference, advanced study designs, epidemiologic data analysis and interpretations of different study designs. Objectives After successful completion, students are expected to - Apply DAG to analyze complex epidemiologic topics - Use appropriate study design to address epidemiology research questions - Choose appropriate approach to analyze data of different study designs - Understand the sciences and arts of model building in epidemiology - Develop an analytical plan for secondary data analysis project (proposal), and conduct it - Correctly present and interpret epidemiologic results
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| 週次 | 主題 |
|---|---|
| 第 1 週 | Course overview & Data introduction |
| 第 2 週 | Causal diagrams & DAG |
| 第 3 週 | Innovative study designsData analysis and interpretation: Survey and cross-sectional study |
| 第 4 週 | Checkpoint 1: Research Q, Causal diagram |
| 第 5 週 | Data analysis and interpretation: Cohort I |
| 第 6 週 | Data analysis and interpretation: Cohort II |
| 第 7 週 | Lab: Cohort study |
| 第 8 週 | Midterm: Group proposal due |
| 第 9 週 | Checkpoint 2: Proposal review and comments |
| 第 10 週 | Data analysis and interpretation: Multilevel data modeling |
| 第 11 週 | Data analysis and interpretation: Cross-sectional studiesData analysis and interpretation: Multilevel data modeling |
| 第 12 週 | Checkpoint 3: Result presentation |
| 第 13 週 | Causal inference in pharmacoepidemiology |
| 第 14 週 | Missingness |
| 第 15 週 | Checkpoint 4: Presentation |
| 第 16 週 | Final: Individual abstract |
| 第 17 週 | |
| 第 18 週 |
There is no required textbook, but there will be required readings for particular lectures. Nevertheless, below we list books that would be helpful. The NYMU library has electronic access to these books. - Kenneth J Rothman, Timothy L Lash, Sander Greenland. (2008) Modern Epidemiology. Wolters Kluwer. - Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch (2012) Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer. - https://stats.idre.ucla.edu/#
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