Biostatistics

Course guarantor:

Aleš Tichopád

Lecturer:

Christiane Malá, Aleš Tichopád

Tutor:

Christiane Malá, Aleš Tichopád

Supervisor:

Department of Biomedical Technology

Synopsis:

The course focuses on understanding the principles of statistical thinking and their application in the processing and interpretation of biomedical data. Students will learn to plan, use, and interpret statistical methods not only for scientific work but also for managerial decision-making in the fields of healthcare, pharmacy, and biotechnology. The course focuses on the practical mastery of statistical methods and their application to biomedical data. Students will learn procedures that will enable them to independently analyze data, draw conclusions, and interpret results in the context of scientific, clinical, and managerial practice.

Requirements:

Conditions for credit:

Mandatory attendance at classes (maximum of 2 unexcused absences).

Students are required to prepare for each class in advance, complete the assigned independent tasks, and successfully pass the entrance test at the beginning of the class.

Failure to meet these conditions may result in exclusion from the exercise, which is considered an absence.

Students must successfully pass the credit test and submit the credit project during the semester. They must obtain at least 50% of the points in both parts.

Exam conditions:

Passing the credit test is a prerequisite for admission to the exam.

The exam takes the form of a written test containing theoretical questions and practical tasks corresponding to the content of the lectures and exercises.

Syllabus of lectures:

1. Introduction to the issue and overview of the application possibilities of statistics in biomedicine

2. Practical examples of the use of statistics in scientific and clinical practice

3. The need for evidence in biomedicine and the principles of working with data

4. Working with big data and the use of statistics in the era of data medicine

5. Types of variables and their significance in descriptive analysis and in the creation of scientific evidence

6. Descriptive statistics and working with different types of variables

7. Hypothesis testing, working with metric and categorical data

8. Comparing groups based on metric data

9. Public holiday

10. Statistical study design. The influence of associated variables on the results and interpretation of statistical analysis.

11. Statistics of dependencies between variables, multidimensional methods

12. Advanced methods of statistical analysis of biomedical data.

13. Working with multidimensional data, dimensionality reduction, clustering methods.

14. Artificial intelligence in biomedical data analysis, types of databases and data preparation, data operationalization.

Syllabus of tutorials:

Teaching takes place in the form of practical exercises in the R program on the following tasks:

1. Operating the R program in programming mode.

2. Processing and visualizing data in the form of tables and graphs according to data type.

3. Analysis of statistical data distribution and methods of describing the difference between mean values and variance. Graphical representation of non-parametric and parametric data

4. Normality tests and other approaches to data with non-normal distribution.

5. Estimating parameters and expressing estimation uncertainty.

6. Testing hypotheses about differences between two samples for parametric and non-parametric data.

7. Analysis of variance (ANOVA) for multiple comparisons and adjustments for multiple comparisons.

8. Credit tests

9. Public holiday

10. Advanced methods of analysis of variance and covariance using generalized linear models (GLM)

11. Regression and correlation analysis (relationships between variables)

12. Analysis of categorical data and working with contingency tables. Goodness-of-fit tests

13. Advanced methods of biomedical data analysis - survival analysis and logistic regression.

14. Statistical analysis of multidimensional data, cluster analysis, and data dimension reduction.

Study Objective:

The course focuses on acquiring practical skills in the field of statistical analysis of biomedical and clinical data using the R program. Students will learn to design and implement data analyses, correctly interpret results, and use statistical methods for scientific and managerial decision-making in healthcare. By completing the course, students will also fulfill the study requirements of the relevant fields of study.

The teaching is linked to the current research activities of the workplace, which are carried out in cooperation with a number of institutions and healthcare facilities in the Czech Republic. Students will thus become familiar with real data scenarios, including working with large clinical databases, survival analysis, multidimensional methods, and quality control in biomedical engineering.

The aim is to prepare graduates for independent professional work in research, clinical practice, and technological applications, with an emphasis on the correct use and interpretation of statistical methods in biomedicine.

Study materials:

Main study portal: https://portal.matematickabiologie.cz/

Required chapters (recommended minimum): Data analysis in R; Data analysis and management for healthcare fields, Clinical data analysis; Biostatistics for mathematical biology; Regression modeling; Multivariate methods for data analysis and classification; Statistical modeling.

Optional/supporting (in case of extended teaching): Applied survival analysis; Database systems in biomedicine (for working with large data sets); Linear and adaptive data processing (advanced data analysis methods); Artificial intelligence (trend area in data analysis general introduction); Signals and linear systems (for engineering applications measurement and quality control).

Basic literature:

Pavlík, T., Dušek, L. (2012). Biostatistics. IBA MU Brno. Free download: https://www.matematickabiologie.cz/media/3293331/pavlik-biostatistika.pdf

Recommended literature:

Motulsky, H. (2017). Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking (4th ed.). Oxford University Press. https://global.oup.com/academic/product/intuitive-biostatistics-9780190643560

Celentano, D. D., Szklo, M., & Farag, M. K. (2025). Gordis Epidemiology (7th ed.). Elsevier. https://shop.elsevier.com/books/gordis-epidemiology/celentano/9780323877756

WOLOSHIN, Steven; SCHWARTZ, Lisa M.; WELCH, Gilbert W. Know Your Chances: Understanding Health Statistics. University of California Press, 2008. ISBN978-0-520-25222-6. Freely available online at https://www.ncbi.nlm.nih.gov/

Note:

Time-table for winter semester 2025/2026:

	06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon	roomKL:B-435 Tichopád A. Malá C. 08:00–09:50 (lecture parallel1 parallel nr.1) Kladno FBMI roomKL:B-330 Tichopád A. Malá C. 12:00–13:50 EVEN WEEK (lecture parallel1) Kladno FBMI roomKL:B-137 Tichopád A. Malá C. 12:00–13:50 ODD WEEK (lecture parallel1) Kladno FBMI
Tue
Wed
Thu
Fri

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans:

• Assistive Technologies (compulsory course)
• Software Technologies (compulsory course)
• Nanotechnology (compulsory course)