Course: Biostatistics

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Course title Biostatistics
Course code KMA/BIO
Organizational form of instruction Lecture + Exercise
Level of course Bachelor
Year of study 1
Semester Summer
Number of ECTS credits 4
Language of instruction Czech
Status of course Compulsory-optional, Optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Vencálek Ondřej, doc. Mgr. Ph.D.
  • Fačevicová Kamila, Mgr. Ph.D.
Course content
1. Types of epidemiological studies 2. Frequency measures - prevalence, incidence 3. Measures of association - relative risk, odds ratio 4. Analysis of two-dimensional contingency tables - estimates, tests 5. Confounding and adjustment 6. Interaction 7. Logistic regression model 8. Maximum likelihood method 9. Practical use of logistic regression, interpretation of parameters 10. Sensitivity, Specificity, ROC curve

Learning activities and teaching methods
Lecture, Dialogic Lecture (Discussion, Dialog, Brainstorming)
Learning outcomes
The aim of the course is to acquaint students with the basic applications of statistics in biomedical research.
Students will have an overview of basic concepts related to biomedical research, will be able to use these concepts and interpret them.
Prerequisites
Knowledge of the basics of probability theory.

Assessment methods and criteria
Seminar Work

Test during the semester + final work.
Recommended literature
  • G. J. McLachlan. (1992). Discriminant analysis and statistical pattern recognition. Wiley, New York.
  • Procházka, B. (2015). Biostatistika pro lékaře. Karolinum.
  • R. G. Miller. (1981). Survival Analysis. Wiley, New York.
  • T. Le Chap. (2003). Introductory Biostatistics. Wiley, New Jersey.
  • Zvárová, J. (2004). Biomedicínská statistika. Karolinum, Praha.


Study plans that include the course
Faculty Study plan (Version) Category of Branch/Specialization Recommended year of study Recommended semester
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Data Science (2020) Category: Mathematics courses 2 Recommended year of study:2, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Mathematics (2023) Category: Mathematics courses 1 Recommended year of study:1, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Industrial Mathematics (2020) Category: Mathematics courses 2 Recommended year of study:2, Recommended semester: Summer
Faculty: Faculty of Science Study plan (Version): Applied Mathematics - Specialization in Business Mathematics (2021) Category: Mathematics courses 2 Recommended year of study:2, Recommended semester: Summer