Linear Models
| Title | Γραμμικά Μοντέλα / Linear Models |
| Code | ΜΙΣΤΑ004 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Anna-Bettina Haidich |
| Common | No |
| Status | Active |
| Course ID | 600020025 |
Programme of Study: PPS Health Statistics and Data Analytics (2020-today)
Registered students: 28
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| Core | Compulsory Course | 1 | 1 | 7.5 |
| Academic Year | 2024 – 2025 |
| Class Period | Winter |
| Faculty Instructors |
|
| Instructors from Other Categories |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600259717
|
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/1/600259717
- eLearning: https://elearning.auth.gr/course/view.php?id=15593
Language of Instruction
- Greek (Instruction)
- English (Instruction, Examination)
Prerequisites
Required Courses
- ΜΙΣΤΑ001 Introduction to Data Analytics
- ΜΙΣΤΑ002 Basic Principles of Statistics
Learning Outcomes
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
General Competences
- Apply knowledge in practice
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)
Educational Material Types
- Notes
- Slide presentations
- Video lectures
- Multimedia
- 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
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 45 | ✓ | |||
| Laboratory Work | 60 | ✓ | |||
| Reading Assigment | ✓ | ||||
| Exams | 65 | ✓ | |||
| Total | 170 |
Student Assessment
Description
Weekly quizes, with multiple choice questions
Assessment based on comments submitted by each student in online discussion for a
Final exam with multiple choice questions
Student Assessment methods
- Written Exam with Multiple Choice Questions (Formative, Summative)
- Performance / Staging (Formative, Summative)
- Written Exam with Problem Solving (Formative, Summative)
- Labortatory Assignment (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
22-02-2023
