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
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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 - Critically and appropriately choose study design and analytical strategies - 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 | Intro & Project Data introduction |
| 第 2 週 | Causal diagrams & DAG |
| 第 3 週 | Other study designs |
| 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 |
| 第 8 週 | Midterm: Group proposal due |
| 第 9 週 | Checkpoint 2: Proposal review and comment |
| 第 10 週 | Data analysis and interpretation: Cross-sectional studies |
| 第 11 週 | Data analysis and interpretation: Multilevel data modeling |
| 第 12 週 | Checkpoint 3: Table 1, and other table shells |
| 第 13 週 | Pharmacoepidemiology and causal inference |
| 第 14 週 | Data 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. - Timothy L Lash et al. (2021) Modern Epidemiology, Ed. 4. Wolters Kluwer. - Hulley, S. B. (Ed.). (2007). Designing Clinical Research. Lippincott Williams & Wilkins. - Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch (2012) Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer. - For uses of statistical packages to conduct basic data analysis: https://stats.idre.ucla.edu/#
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