F7AMBMPV - Mathematical Methods in Research

The course deals with the following topics: methods of statistical analysis intended primarily for medical research - clinical, biological, biochemical, biophysical and other studies, methods of descriptive and inductive statistics, statistical epidemiological methods, hypothesis testing, group comparison (parametric and non-parametric methods), ANOVA, correlation and simple regression analysis, multivariate regression models, multivariate linear models, logistic regression, discriminant analysis, survival analysis etc., model calculations and interpretation of results.

Requirements for assessment:

Active participation in seminars. Excused absence from a maximum of 3 seminars. All assignments submitted according to the instructions.

Course evaluation:

Seminars: 20 points + bonus

Final exam: 80 points (50 points for the practical part, 30 points for the theoretical part)

The exam is written. The focus of the exam is on the statistical processing of specific data on a computer and the explanation of this solution. Another part of the exam is theoretical and analytical. Only students who have obtained an assessment can apply for the exam. The course is graded according to the ECTS scale based on the points obtained in the exam and during the semester.

1.Statistical procedures in medicine

2. Descriptive statistics I

3.Descriptive Statistics II

4.Hypothesis testing paired and two-sample tests

5.Hypothesis testing multivariate analysis

6.One-way analysis of variance

7.Contingency tables

8.Correlation and simple regression

9.Multivariate regression analysis

10.Discriminatory analysis

11.Logistic regression

12.ROC analysis and benefit-cost analysis

13.Survival analysis

14.Principal component analysis and factor analysis

1.Introduction to the R studio program, installation, overview of basic functionalities

2.Dataset upload, dataset editing in R

3.Basics of working in R II, long and wide format, descriptive statistics I in R

4.Basics of working in R II, descriptive statistics II in R

5.Graphical data presentation in R

6.Outliers, Assumptions of statistical tests in R

7.Data normality verification, normality testing in R

8.Testing hypotheses for 1 and 2 samples in R

9. Testing hypotheses for more than 2 samples (independent design) in R

10.Testing hypotheses for more than 2 samples (repeated meassures design) in R

11.Testing hypotheses with multiple factors in R

12.Linear regression in R

13.Statistical analysis of categorical data

14.Interpretation of results

Required:

MOTULSKY, Harvey. Intuitive biostatistics: a nonmathematical guide to statistical thinking. 3rd ed. New York: Oxford University Press, 2014. ISBN 978-0-19-994664-8.

VU, Julie and David HARRINGTON. Introductory Statistics for the Life and Biomedical Sciences [online]. OpenIntro Inc., 2021. Available from: https://www.openintro.org/.

Recommended:

D'AGOSTINO, R. B., ed. Tutorials in biostatistics. Volume 1, Statistical methods in clinical studies [electronic source]. Hoboken: Wiley, 2005. ISBN 9780470023679.

D'AGOSTINO, R. B., ed. Tutorials in biostatistics. Volume 2, Statistical modelling of complex medical data [electronic source]. Hoboken: Wiley, 2005. ISBN 9780470023723.

RAYAT, Charan Singh. Statistical Methods in Medical Research [online]. Singapore: Springer Singapore, 2018. DOI: http://dx.doi.org/10.1007/978-981-13-0827-7. ISBN 9789811308277.

• Prospectus - magisterský (!)
• Biomedical and Clinical Engineering (compulsory course)