Course detail

Biostatistics

FEKT-BPC-STAAcad. year: 2023/2024

The course is an introduction to applied data analysis for students of biological and possibly clinical disciplines. The substance is discussed from theoretical principles (principles for statistical estimates, the existence of stochastic distribution, basic statistical tests), simple applications (one-sample and two-sample tests, correlation analysis) to the basics of stochastic modeling and experimental design (design of experiments, regression analysis, analysis of variance). Theory is always discussed in direct connection with practical examples. This course enables students to acquire the basic principles of biostatistical data analysis and prepares candidates for its sole use in their scientific work. The course will also focus on pratical training with the use of all available software tools (Statistica for Windows, SPSS).

Language of instruction

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

doc. Ing. Daniel Schwarz, Ph.D.

Department

Faculty of Medicine, Masaryk University Brno (LF MU)

Entry knowledge

Knowledge at the secondary school level of mathematics, PC, work with MS Office, particularly MS Excel is required.

Rules for evaluation and completion of the course

Compliance with the requirements for terminating exercise: participation in seminar, final exam for more than 50% points.
Written exam for more than 40% of the points.
The final exam is focused on understanding of statistical data analysis and their applications.
Participation in seminar is mandatory, two absences are permitted. In the case of multiple absences, it is possible to substitute the seminar after the agreement with teacher (ideally in another parallel group).

Aims

To provide the students with sufficient knowledge for clinical data acquisition, organisation and statistical analysis including presentation and interpretation of outcomes
After completing the course the student is able to:
• apply descriptive statistics on the given data
• select and use tools to test hypotheses
• graphically present statistical data
• clinical studies suggest
• work with databases and statistical programs

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

ALTMAN, Douglas. Practical statistics for medical research. London: Chapman and Hall, 1991. ISBN 0412276305. (EN)
HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993. ISBN 80-200-0080-1. (CS)

Recommended reading

Not applicable.

Classification of course in study plans

  • Programme BPC-BTB Bachelor's 2 year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. Daniel Schwarz, Ph.D.

Syllabus

BLOCK A: Fundamentals of data analysis
1. Statistics for clinical investigation and practice
2. Model distributions and their practical applications
3. The theory of hypotheses testing
4. Statistical tests and modelling
5. Fundamentals of multi-dimensional analyses
6. Statistical tests for evaluation of diagnostic trials
7. Fundamentals of epidemiological data analysis and population hazard evaluation

BLOCK B: Medical data management, information technology in healthcare
8. User’s approach to the computer, his profile, local data
9. Networks, internet
10. Principles of database construction and data management with the view of quality assurance (QA/QC)

BLOCK C. Planning, management and evaluation of clinical studies
11. Basic terminology, ethical and legal aspects
12. Data analysis in healthcare
13. Randomisation and continuous monitoring of the planned experiment

Laboratory exercise

26 hod., compulsory

Teacher / Lecturer

doc. Ing. Daniel Schwarz, Ph.D.

Syllabus

BLOCK A: Fundamentals of data analysis
1. Statistics for clinical investigation and practice
2. Model distributions and their practical applications
3. The theory of testing statistical hypotheses
4. Statistical tests and modelling
5. Fundamentals of multi-dimensional analysis
6. Statistical tests for evaluation of diagnostic trials
7. Fundamentals of epidemiological data analysis and population hazards evaluation

BLOCK B: Data management in healthcare, information technology application
8. User’s approach to the computer, his profile, local data
9. Networks, internet
10. Principles of database construction and data management with the view of data quality assurance (QA/QC).

BLOCK C. Planning, management and evaluation of clinical studies
11. Basic terminology, ethical and legal aspects
12. Data analysis in healthcare
13. Randomisation and continuous monitoring of the planned experiment