| Title: |
Modern Statistical Methods in Public Health |
| Country: |
China
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| Institution: |
China - School of Public Health, Fudan University, Shanghai
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| Course coordinator: |
Prof. Henry S. Lynn
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| Date start: |
2019-07-08 |
| Date end: |
2019-07-19 |
| About duration and dates: |
10 weekdays of on-site learning and practice (70 hours total) plus 10 hous of reading before begining of course. |
| Classification: |
advanced optional
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| Mode of delivery: |
Face to face
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Course location:
Department of Biostatistics
School of Public Health, Fudan University
138 Yixueyuan Road, Shanghai 200032, China |
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| ECTS credit points: |
3 ECTS credits
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| Language: |
English
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Description:
This course introduces students to a variety of advanced statistical methods employed in public health research. Case studies from five public health areas, namely 1) clinical trial, 2) occupational health, 3) epidemiology and community health, 4) child and adolescent health, and 5) environmental health, will be introduced and used to illustrate how to apply, interpret, and evaluate different statistical methods.
This course does not require advanced mathematical background (e.g., calculus). It is designed for the public health practitioner who needs to apply statistical methods in research or to appraise their use in the literature.
At the end of the course students will be able to:
1. Discern what types of research questions the methods can address.
2. Recognize the underlying assumptions and data structures for the appropriate usage of the statistical methods.
3. Interpret and evaluate the results from the statistical methods.
4. Decipher statistical software output generated from the statistical analyses. |
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Assessment Procedures:
Take-home written assignments (2 x 15%) 30%
Independent study report and presentation 30%
Open-book written examination (2 hrs) 40% |
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Content:
1) Summary of Evidence-based Research: Data Collection/Management, Visualization, and Inference
2) Bayesian Analysis
a) Estimating Treatment Effect in a Multi-center Randomized Clinical Trial
b) Inference Using Prior and Posterior Distributions
3) Variable Selection
a) Developing a Screening Tool for Occupational Low Back Pain
b) Model Validation (Cross-validation, Bootstrapping)
c) LASSO Regression
d) Classification Tree
4) Multi-level Regression Models
a) Evaluating Rates of Change in a Clustered Cohort Study
b) Generalized Linear Models
c) Mixed Effects Models
5) Reference Centile Curves
a) Evaluating BMI in Adolescents
b) Box-Cox Transformation and Cubic Spline Smoothing
c) LMS Method
6) Generalized Additive Models
a) Predicting Disease Prevalence Using Serial Air Quality Measurements
b) Autocorrelation and Seasonal Trends |
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Methods:
The 36-hr classroom lectures will cover the six modules listed in the contents. Modules 2, 3 and 4 will each use two 6-hr sessions, while the rest of the modules will each use one 6-hr session.
Five case studies are used to demonstrate the application of advanced statistical methods in addressing research questions in public health. For each case study, traditional methods of analysis will be contrasted with modern statistical techniques to highlight strengths and advantages. Relevant literature and exercises on these examples will be distributed beforehand. Students will be engaged in group discussions and asked to share their findings in oral presentations. Multiple choice questions will be administered at the end of the lectures to reinforce the main points covered.
In the nine 2-hr hands-on data analysis lab sessions, teaching assistants will demonstrate how to program statistical software using R and implement each of the five major statistical analyses. Students will practise on specific data examples by following instructions from the teaching assistants.
For the study report, the students will be asked to review and critique publications that apply one of the statistical methods taught in class to answer a public health research question. Their report will be submitted as a written assignment and presented in class. |
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Prerequisites:
At least 3 ETCS credit points of biostatistics and 3 ETCS credit points of epidemiology. Students should have an understanding of basic statistical methods such as multiple regression, ANOVA, rank tests, logistic regression, and survival analysis. The level of proficiency should be similar to that covered in Bernard Rosner’s textbook Fundamentals of Biostatistics, 2015, 8th edition, Thomson Higher Education. Prior knowledge of the R statistical computing software is not required but desirable and students should have experience in using at least one major statistical software package (e.g., Stata, SAS, SPSS).
TOEFL Score at least 550 or ELTS equivalent. |
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Selection:
Meet the prerequisites. First come, first serve. |
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Scholarships:
Available depending on demonstrated need. |
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Major changes since initial accreditation:
The course originally listed 60 SIT contact hours and did not account for the additional time students needed to do assignments and prepare for the examination. Thus, the number of SIT hours has been updated to 80 SIT hours to more accurately reflect the students’ time involvement. In addition, 10 SIT hours have been allotted for individual study on basic R programming and fundamental statistical reasoning. |
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Student evaluation:
Over the past several years, comments overall were very positive with almost all the students pleased with the lectures and range of topics covered. From 2015 to 2017, the overall quality of the course received a rating of 4.78-4.88 (out of 5), and 80% of the students also strongly agree that they will recommend the course to others. |
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Lessons learned:
So far we have had a number of troped students inquire about the course but only one troped student managed to enroll in the course. The main reason may be because she is originally from Shanghai and was returning to visit. |
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tropEd accreditation:
Initially accredited in December 2010 and re-accredited in February 2015. Re-accreditation in June 2019, Umea, Sweden. This accreditation is valid until June 2024. |
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Remarks:
Students should allow 2-3 months before commencement of the course to secure visas. |
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| Date Of Record Creation: |
2019-06-17 09:13:58 (W3C-DTF) |
| Date Of Record Release: |
2019-06-17 13:36:49 (W3C-DTF) |
| Date Record Checked: |
2019-06-17 (W3C-DTF) |
| Date Last Modified: |
2019-07-10 09:30:15 (W3C-DTF) |
