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課程名稱 |
生物統計學與流行病學原理
Principles of Biostatistics and Epidemiology |
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開課學期 |
110-1 |
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授課對象 |
公共衛生學院 全球衛生碩士學位學程 |
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授課教師 |
張慶國 |
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課號 |
MGH7029 |
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課程識別碼 |
853EM0290 |
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班次 |
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學分 |
4.0 |
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全/半年 |
半年 |
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必/選修 |
必修 |
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上課時間 |
星期一6,7(13:20~15:10)星期四6,7(13:20~15:10) |
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上課地點 |
公衛118公衛118 |
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備註 |
本課程以英語授課。為學程碩/博士生必修課；外系生請洽詢老師。與安亞克合授
總人數上限：30人
外系人數限制：10人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
The aim of this course is to introduce concepts of health-related study design, data collection, and statistical analysis commonly used with practical sessions involved. This course includes three parts: The first part is "epidemiologic study design", in which an introduction about principle of study design, causal inference, and concepts of fair comparisons in science is given. The second part is "analytical method", which provides an introduction of fundamental statistical methods used to extract hidden information in the data. The third part is "practical sessions" closely attached to the on-going lectures for better understanding on the contents. Real-life examples from each aspects of global health research topics and latest literature are given and illustrated in the course.
For sessions of data analyses, example datasets are provided to students for performing statistical analyses by R, a well-known kind of freeware for statistical analysis. |
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課程目標 |
At the end of the course students should have the following core knowledge and competencies:
1. To understand the principle of statistical and causal inference.
2. To explain how random variation and bias affect public health research findings.
3. To understand the principle of epidemiologic study design in experimental and observational studies.
4. To critically appraise public health literature in terms of validity and applicability.
5. To analyze health datasets with appropriate statistical methods, to implement statistical software, and to correctly explain the analysis results. |
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課程要求 |
The slides of each lecture and scientific papers for discussion will be available on the course website for students to download before each class. |
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預期每週課後學習時數 |
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Office Hours |
另約時間 |
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指定閱讀 |
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參考書目 |
1. Epidemiologic Methods: Studying the Occurrence of Illness. 2nd Edition. Noel S. Weiss and Thomas D. Koepsell. Oxford University Press 2014.
2. Epidemiology: An Introduction. 2nd edition. Kenneth J. Rothman. Oxford University Press 2012.
3. Epidemiology (with STUDENT CONSULT Online Access). 5th edition. Leon Gordis. Elsevier Saunders 2013.
4. Modern Infectious Disease Epidemiology. 3rd edition. Johan Giesecke. CRC Press 2017.
5. R for Data Science. Garrett Grolemund and Hadley Wickham, https://r4ds.had.co.nz/
6 . Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 2nd edition. Richard McElreath. CRC Press 2020. https://www.youtube.com/watch?v=4WVelCswXo4&list=PLDcUM9US4XdNM4Edgs7weiyIguLSToZRI |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
stat |
45% |
mid-term(written exam): 15%
final(data analysis project): 30% |
2. |
epi |
45% |
mid-term(written exam): 15%
final(written exam): 30% |
3. |
Participation in discussion and attendance |
10% |
A preceding e-mail for calling sick before a lecture is essential. |
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週次 |
日期 |
單元主題 |
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第1週 |
09/14, 09/16 |
(CKC) Orientation; Introduction of Epidemiology
(CKC) The Natural History of Disease; The Dynamics of Disease Transmission |
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第2週 |
09/21, 09/23 |
(AA) Random numbers; Probability distributions (discrete vs continuous, interpretation)
(AA) Data fit; Likelihood; Confidence Intervals; Information criteria for model comparison |
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第3週 |
09/28, 09/30 |
(CKC) Measuring the Occurrence of Disease; Survey, Sampling and Prototype of Epidemic Study Designs
(AA) Basics of programming in R; Pipelines in R; ggplot; dplyr |
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第4週 |
10/05, 10/07 |
(CKC) Risk and Relative Risk
(AA) Analyzing the disease incidence in R |
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第5週 |
10/12, 10/14 |
(CKC) Causal Inferences, Bias, Confounding, and Interaction
(AA) Fitting main distributions to the data in R using maximum likelihood estimation |
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第6週 |
10/19, 10/21 |
(AA) Statistical tests
(CKC) Cohort Study 1 |
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第7週 |
10/26, 10/28 |
(CKC) Cohort Study 2 (Standardization)
(CKC) EPi practical session 1 |
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第8週 |
11/02, 11/04 |
(CKC) Case-Control Study 1
(CKC) Case-Control Study 2 (Mantel-Heanzel method for confounding adjustment; "selection bias" versus "confounding") |
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第9週 |
11/09, 11/11 |
(CKC) Epi practical session 2
(CKC & AA) Midterm exam |
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第10週 |
11/16, 11/18 |
(CKC) Diagnosis, Tests, and Screening
(CKC) Randomized Clinical Trial (RCT) 1: Principles of RCTs |
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第11週 |
11/23, 11/25 |
(AA) Regression models; Correlation analysis; Correlation vs Causation
(AA) Statistical tests / Regression models with examples in R |
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第12週 |
11/30, 12/02 |
(AA) Frequentist vs Bayesian statistics: differences and main principles
(AA) Probabilistic programming languages: structure and applications |
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第13週 |
12/07, 12/09 |
(CKC) Applying Epidemiology on Evaluation of Health Services
(CKC) Epi practical session 3 |
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第14週 |
12/14, 12/16 |
(AA) Choice of prior distributions, Posteriors; Hiererchical models
(AA) Basic example (Bernoulli trial) with analysis in frequentist and Bayesian framework |
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第15週 |
12/21, 12/23 |
(AA) Some caveats in data analysis (Truncated / censored data; compete / partial pooling)
(AA) Model fit in Bayesian framework |
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第16週 |
12/28, 12/30 |
(AA) Data analysis project
(AA) Realization of the data analysis project (office hours) |
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第17週 |
01/04, 01/06 |
(CKC) Final written exam for epi
(AA) Personal data analysis project report for stats |
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第18週 |
01/11, 01/13 |
Review |
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