| Course Type | Elective Course |
|---|
| Course Level | Master's Degree Without Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 8 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- They will remember current basic knowledge of Biostatistics and learn and apply more advanced Biostatistics analysis.
- They will gain basic and advanced skills in the calculation of sample and sample size used in the design and analysis of clinical studies, Logistic regression, survival analysis, multivariate regression models, multivariate ANOVA models, analysis of repeated measures and categorical data, statistical methods used in the evaluation of diagnostic tests.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | SABE 605 BIOSTATISTICS, SABE 606 RESEARCH METHODS |
|---|
| Course Description | In the Advanced Biostatistics Analysis course, students will develop the basic statistical skills they have gained in their current education. They will learn to apply Advanced Quantitative Methods and more advanced techniques to real data. They will learn to enter, organize, analyze, report and interpret their data using the SPSS package program. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Recall of current statistical information and an overview of advanced Biostatistics methods in the literature and the necessity of these methods |
|
2nd Week |
Data cleaning and preparing data for analysis: Outliers, missing observations and coding, multi-option variables |
|
3rd Week |
An overview of the use of basic graphic methods and tables |
|
4th Week |
Analysis of Repeated Measurements (parametric and nonparametric methods) |
|
5th Week |
ANOVA Models: One-way, two-way analysis of variance models, generalized linear models (GLM), MANOVA models |
|
6th Week |
Univariate, multivariate regression models |
|
7th Week |
Logistic regression analysis, Cox regression analysis |
|
8th Week |
Survival analysis |
|
9th Week |
Analysis of categorical data |
|
10th Week |
Basic approaches on validity and reliability in studies using 10th Week Scales and examples of calculating scores from data |
|
11th Week |
Statistical methods used in the evaluation and decision making of Diagnostic tests: ROC Curve |
|
12th Week |
Basic approaches in calculating sample and sample size |
|
13th Week |
Randomization and methods in clinical trials |
|
14th Week |
Basic principles and mistakes in reporting and interpreting results |
|
| Recommended or Required Reading | Course materials and notes, articles given in the course are sufficient. |
|---|
| Planned Learning Activities and Teaching Methods |
Derslerin teorik kısmı power point sunusu şeklinde, uygulamaları ise SPSS 25.0 programı kullanılarak gösterilecektir. Lecture, Discussion, Questions/Answers, Practice, Proje, Problem Solving, Presentation yöntemleri uygulanacaktır.
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Total(%) |
|
0 |
| Contribution of In-term Studies to Overall Grade(%) |
|
0 |
| Contribution of Final Examination to Overall Grade(%) |
|
100 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
10 |
140 |
| Preparation for Final exam |
1 |
10 |
10 |
| Course hours |
14 |
4 |
56 |
| Preparation for Midterm exam |
1 |
5 |
5 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2.5 |
2.5 |
| Homework |
|
|
|
| Performance Practice |
1 |
14 |
14 |
| Article Presentation |
1 |
10 |
10 |
| Total Work Load | 238.5 |
| Total Work Load / 30 (h) | 7.95 |
| ECTS Credit of the Course | 8 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
