Medical Statistics Methodology I
| Title | Ιατρική Στατιστική Μεθοδολογία Ι / Medical Statistics Methodology I |
| Code | ΙΜΑ009 |
| Faculty | Health Sciences |
| School | Medicine |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Adriani Nikolakopoulou |
| Common | Yes |
| Status | Active |
| Course ID | 200000461 |
Programme of Study: PMS Ereunītikī Methodología stīn Iatrikī kai stis Epistīmes Ygeías (2021-sīmera)
Registered students: 25
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | 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 | 600261822
|
Course Type 2021
Skills Development
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600261822
- eLearning: https://elearning.auth.gr/course/view.php?id=9588
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Learning Outcomes
Knowledge
Upon successfully completing this course, students will be familiar with:
• The main differences between the types of studies of comparing populations
• The appropriate summary measure of variables for quantitative and qualitative data
• The difference between a sample and the population from which it came
• The characteristics of the normal distribution and the difference from the asymmetric distribution
• The sampling distribution and the concept of standard error of the mean
• The methodology of hypothesis testing, the concepts of p value, the level of significance and confidence interval, the types of I and II errors
• The basic parametric and non-parametric tests through real examples in the health and life sciences
• In which data can be applied the survival analysis and how to conduct such an analysis
Capacities
The course participants upon completion will be able to:
• Understand and compute the descriptive statistical measures that appear in the medical scientific articles
• Formulate and interpret graphs appropriately
• Calculate association measures such as mean differences, risk differences, relative risks, odds ratios and incidence rate ratios related reason and impact-rates
• Calculate the appropriate sample size of a survey
• Use 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. Different types of data, quantitative and qualitative
2. Summary measures for quantitative and qualitative data (practice in R)
3. Graphs for quantitative and qualitative data (practice in R)
4. The normal (Gaussian) distribution (practice in R)
5. Measures of association: mean differences, risk differences, relative risks, odds ratios and incidence rate ratios
6. Confidence intervals for measures of association
7. Hypothesis testing- paired and two-sample t-tests: Mann-Whitney U test and Wilcoxon Signed Ranks test (practice in R)
8. Hypothesis testing -tests for more than two samples: ANOVA and Kruskal-Wallis test (practice in R)
9. Tests for categorical variables: χ^2, Fisher’s exact test, Mc Nemar’s test (practice in R)
10. Survival analysis: Log-rank test and Kaplan-Meier plots (practice in R)
11. Power and sample size calculation
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
Description
• Weekly quizes, with multiple choice questions
• Assessment based on comments submitted by each student in online discussion for a
Final exams with multiple choice questions
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 65 | ||||
| Laboratory Work | 40 | ||||
| Reading Assigment | 20 | ||||
| Exams | 65 | ||||
| Total | 190 |
Student Assessment
Description
Midterm and final exams 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)
Bibliography
Additional bibliography for study
Bougioukas, KI & Haidich, A-B. (2019). Medical Biostatistics: Basic Concepts. In V. Papademetriou, E. A. Andreadis & C. Geladari (Eds.), Management of hypertension: Current practice and the application of landmark trials (pp. 19–53). Springer 2019. doi:10.1007/978-3-319-92946-0_2
2. Aho, Ken A. Foundational and applied statistics for biologists using R. CRC Press, 2013.
3. Bland, Martin. An introduction to medical statistics. 3rd Edition. Oxford University Press, 2000.
4. Crawley, Michael J. Statistics: an introduction using R, 2nd Edition. John Wiley & Sons, 2014.
5. MacFarland, Thomas W. Introduction to Data Analysis and Graphical Presentation in Biostatistics with R. Springer, 2014.
6. 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.
7. Logan M. Biostatistical Design and Analysis Using R: A Practical Guide. Wiley-Blackwell, 2010.
8. Shahbaba, B. (2011). Biostatistics with R: An Introduction to Statistics Through Biological Data (Use R!) (2012th ed.). Springer.
9. Bowers, D. (2019). Medical Statistics from Scratch: An Introduction for Health Professionals (4th ed.). Wiley.
Last Update
21-12-2021
