Advanced Statistics
| Title | Προχωρημένες γνώσεις Στατιστικής / Advanced Statistics |
| Code | E1 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Spring |
| Coordinator | Anna-Bettina Haidich |
| Common | No |
| Status | Active |
| Course ID | 600018994 |
Programme of Study: PPS Medical Research Methodology
Registered students: 33
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Elective Courses | 1 | 1 | 4 |
| Academic Year | 2025 – 2026 |
| Class Period | Spring |
| Faculty Instructors | |
| Instructors from Other Categories |
|
| Weekly Hours | 4 |
| Total Hours | 96 |
| Class ID | 600290274
|
Course Type 2021
Skills Development
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600290274
- eLearning: https://elearning.auth.gr/course/view.php?id=14874
Language of Instruction
- Greek (Instruction)
- English (Instruction, Examination)
Prerequisites
Required Courses
- C1 Introduction to Statistics
Learning Outcomes
Knowledge
Upon successfully completing this course, students will be familiar with:
• Simple and multiple linear models applied to medical data (linear, logistic, Poisson, Cox)
• Assumptions and conditions that should be met for the application of these models
• The goodness of fit test to evaluate the performance of the models
• The necessary conditions that must exist in order to qualify as one or more variables as confounding in a relationship of outcome and exposure
• The concept of effect modification, and how it differs from confounding
• The usefulness of multiple regression techniques to analyze the relationship of an exposure to a predictive variable in the possible presence of confounding factors by using adjusted analysis
• Sample size calculation for multivariable models
Capacities
The course participants upon completion will be able to:
• Have a methodology in order to build appropriate multivariable linear models
• Investigate the existence of confounding or effect modification in medical research and manage them appropriately
• Interpretation of the results obtained from the corresponding models
• Using the fast-growing and evolving R software as a tool for statistical analysis and the creation of elegant graphs
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Work in teams
Course Content (Syllabus)
1. Data investigation and editing, guidance for constructing graphs using R program (practice R)
2. Linear relationship between two quantitative variables (Scatter plots, Pearson’s & Spearman’s correlation) (practice R)
3. Theory of Linear Regression Models and their importance in medical research and practice
4. Confounding and effect modification
5. Simple and multiple linear regression analysis and applications in medical data (in practice R)
6. Simple and multiple logistic regression analysis and applications in medical data (in practice R)
7. Simple and multiple Poisson regression analysis and applications in medical data (in practice R)
8. Simple and multiple Cox regression and applications in medical data (in practice R)
Keywords
Linear Models, Linear regression, Logistic regression, Cox Proportional Hazards Model
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- Interactive excersises
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
• Use of ICT in Teaching - Moodle Virtual learning environment (VLE) (asynchronous learning, wikis, Online Discussion Fora, Educational Portfolio, assignment submission, assessment process)
• Use of ICT in Communication with students (email, instant messaging via Moodle)
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 40 | 1.6 | |||
| Laboratory Work | 20 | 0.8 | |||
| Reading Assigment | 20 | 0.8 | |||
| Written assigments | 50 | 2 | |||
| Total | 130 | 5.2 |
Student Assessment
Description
• Written assignment, in English, approximately 2,500 words long, to be submitted by each student at the end of the course
• Assessment of knowledge at the beginning and the end of the course with short-answer questions and essays development
• Weekly quizes, with multiple choice questions
• Assessment based on comments submitted by each student in online discussion fora
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative, Summative)
- Written Assignment (Formative, Summative)
- Performance / Staging (Formative, Summative)
Bibliography
Additional bibliography for study
1. Aho, Ken A. Foundational and applied statistics for biologists using R. CRC Press, 2013.
2. Bland, Martin. An introduction to medical statistics. 3rd Edition. Oxford University Press, 2000.
3. Crawley, Michael J. Statistics: an introduction using R, 2nd Edition. John Wiley & Sons, 2014.
4. MacFarland, Thomas W. Introduction to Data Analysis and Graphical Presentation in Biostatistics with R. Springer, 2014.
5. Daniel, Wayne W., and Chad L. Cross. Biostatistics: A Foundation for Analysis in the Health Sciences: A Foundation for Analysis in the Health Sciences. Wiley Global Education, 2012.
6. Logan M. Biostatistical Design and Analysis Using R: A Practical Guide. Wiley-Blackwell, 2010.
7. Aviva Petrie, Caroline Sabin. Medical Statistics at a Glance, 3rd Wiley 2009.
8. David G. Kleinbaum, Mitchel Klein. Survival Analysis: A self-learning text. 3rd Edition. Springer 2012.
9. David G. Kleinbaum. Logistic Regression: A self-learning text. 3rd Edition. Springer 2010.
10. Faraway, J. J. (2014). Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science) (2nd ed.). Chapman and Hall/CRC.
Last Update
21-12-2021
