Biostatistics

Study Course Description

Course Description Statuss:Approved

Course Description Version:24.00

Study Course Accepted:26.08.2024 16:15:33

Study Course Information
Course Code:		SL_001		LQF level:		Level 7
Credit Points:		2.00		ECTS:		3.00
Branch of Science:		Mathematics; Theory of Probability and Mathematical Statistics		Target Audience:		Medicine
Study Course Supervisor
Course Supervisor:		Madara Miķelsone
Study Course Implementer
Structural Unit:		Statistics Unit
The Head of Structural Unit:
Contacts:		14 Balozu street, Block A, Riga, +371 67060897, statistikarsu[pnkts]lv, www.rsu.lv/statlab
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		0	Lecture Length (academic hours)		0	Total Contact Hours of Lectures		0
Classes (count)		12	Class Length (academic hours)		3	Total Contact Hours of Classes		36
Total Contact Hours								36
Study course description
Preliminary Knowledge:
Knowledge of mathematics and informatics relevant to the programme of secondary education.
Objective:
To acquire basic knowledge and skills in statistical data processing methods (descriptive statistics, methods of inferential statistics to estimate differences between groups and relationships between variables) required for the development of research work and the application of statistical indicators in their specialty.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Introduction to statistics, the role of statistics in research process. Data types, measure, data input, data preparation in MS Excel. Introduction to IBM SPSS. Basic actions with data in the IBM SPSS program.			Classes		1.00	computer room
2	Descriptive statistics.			Classes		1.00	computer room
3	Descriptive statistics of the Normal distribution. Confidence intervals.			Classes		1.00	computer room
4	Statistical hypothesis, types of statistical hypothesis. Hypothesis testing. P value. Sample size calculations. Qualitative data processing. Independent and dependent samples.			Classes		1.00	computer room
5	Parametric statistics for quantitative data. Comparison of independent and dependent samples.			Classes		1.00	computer room
6	Nonparametric statistics for quantitative and ordinal data. Comparison of independent and dependent samples.			Classes		1.00	computer room
7	Correlation analysis. Regression analysis (Linear regression).			Classes		1.00	computer room
8	Regression analysis (Binary logistic regression). ROC curves.			Classes		1.00	computer room
9	Summary and practical work with data using IBM SPSS.			Classes		1.00	computer room
10	Analysis of scientific publications.			Classes		1.00	computer room
11	Survival analysis.			Classes		1.00	computer room
12	Independent work with data using IBM SPSS.			Classes		1.00	computer room
Assessment
Unaided Work:
1. Individual work with literature – preparation for each class according to the thematic plan. 2. Individual analysis of a scientific publication - each student will search for one full text scientific publication where data analysis methods included in this course is used. After finding and reading the publication, student will give 5 to 7 minute presentation about the use of statistical methods, results and formulation of conclusions in it. 3. Independent work - each student will have to complete four tasks, including closed and open-ended questions about descriptive statistics and inferential statistics. A. After successfully accomplishing this course, please fill out the study course evaluation form to give us feedback, we will appreciate that a lot!
Assessment Criteria:
For successful integration of knowledge and to prepare for the final exam, the student performs the following activities (mandatory, not graded): 1. Participation in practical lectures. For each missed class, student must attend a session with another group under the current lecturer or study the topic independently and completing the test yourself questions in e-studies. 2. Oral presentation of the analysis of a scientific publication. The grade of the course is cumulative, where: 50% – exam - independent work. 50% – multiple-choice test with 30 theoretical and practical questions in statistics with a time limit of 45 minutes.
Final Examination (Full-Time):		Exam (Written)
Final Examination (Part-Time):
Learning Outcomes
Knowledge:		Upon completion of this course, students will have acquired knowledge that will allow to: * recognise terminology used in statistics and basic methods used in different types of publications; * be competent in commonly used data processing tools in MS Excel and IBM SPSS; * be aware of data processing criteria for various statistical methods; * interpret the most important statistical indicators accurately.
Skills:		Upon completion of this course, students will be able to: * enter and edit data in the computer programs MS Excel and IBM SPSS; * correctly prepare data for statistical processing and analysis; * choose appropriate data processing methods, including the ability to do statistical hypothesis tests; * statistically process research data using the computer programs MS Excel and IBM SPSS; * create tables and graphs in MS Excel and IBM SPSS programs for the obtained results; * describe the obtained research results correctly.
Competencies:		Upon completion of this course, students will be able to take an informed decision about the use of statistical data processing methods to achieve research aims, using the computer programs MS Excel and IBM SPSS; to use the acquired basic statistical methods in processing research data.
Bibliography
No.	Reference
Required Reading
1	Peat J. & Barton B. Medical Statistics: A Guide to SPSS, Data Analysis and Critical Appraisal. 2nd edition. John Wiley & Sons, 2014.
2	Field A. Discovering Statistics using IBM SPSS Statistics. 4th edition. Sage Publications, 2018.
3	Petrie A. & Sabin C. Medical Statistics at a Glance. 4th edition. Wiley-Blackwell, 2019.
4	Grech, V. Write a Scientific Paper (WASP): Effective graphs and tables. Early Human Development, 2019. 134, 51-54. DOI: 10.1016/j.earlhumdev.2019.05.013