460-6024/01 – Methodology for the Evaluation of Experimental Biomedical Data (MEBD)

Gurantor departmentDepartment of Computer ScienceCredits10
Subject guarantordoc. Dr. Ing. Eva KriegováSubject version guarantordoc. Dr. Ing. Eva Kriegová
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageCzech
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
DYS0006 Mgr. Tereza Dýšková, Ph.D.
FIL0152 Ing. Regina Fillerová, Ph.D.
FUR0029 Mgr. Jana Fürstová
KRI0203 doc. Dr. Ing. Eva Kriegová
MIK0424 Mgr. Zuzana Mikulková, Ph.D.
SCH0341 Mgr. Petra Schneiderová
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

Subject aims expressed by acquired skills and competences

The aim of the course is to deepen students' knowledge in the field of experiment setup, data analysis in biomedicine and molecular biology and interpretation of experimental data. In addition, this knowledge and skills will be further enhanced in a direction that is in line with the specific focus of its Ph.D. studies and dissertation work.

Teaching methods

Seminars
Individual consultations
Project work
Other activities

Summary

This subject provides an understanding of the fundamental and advanced concepts of probability and statistics required for experimental design and data analysis in the health sciences. Initially the subject introduces common study designs, random sampling and randomised trials as well as numerical and visual methods of summarising data. It then focuses on understanding population characteristics such as means, variances, proportions, risk ratios, odds ratios, rates, prevalence, and measures used to assess the diagnostic value of a clinical test. Finally, after determining the sampling distributions of some common statistics, confidence intervals will be used to estimate these population characteristics and statistical tests of hypotheses will be developed. The presentation and interpretation of the results from statistical analyses of typical health research studies will be emphasised.

Compulsory literature:

• Brandt, S. Data Analysis (2014), Springer, Berlin • Sokal R. and Rohlf. F.J. Biometry: The Principles and Practices of Statistics in Biological Research, 4th Edition (2012), W. H. Freeman and Co., New York • McDonald, J.H. Handbook of Biological Statistics, 3rd Edition (2015), Sparky House Publishing, Baltimore, Maryland

Recommended literature:

• Rosner, B. Fundamentals of Biostatistics, 8th Edition (2016), Cengage Learning, Boston, Massachusetts

Way of continuous check of knowledge in the course of semester

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.

E-learning

Other requirements

The student prepares and presents the work on a given topic.

Prerequisities

Subject has no prerequisities.

Co-requisities

Subject has no co-requisities.

Subject syllabus:

• Descriptive statistics and graphical presentation. • Normal distribution. • Central limit theorem. • Sampling. Sample size calculation. • Statistical inference confidence interval and hypothesis testing. • Principles in design of experiments. • Statistical inference for two populations (paired and independent). • One-way analysis of variance (ANOVA) and multiple comparisons with fixed effects and random effects. • Non parametric statistics: Wilcoxon signed-rank test, Mann-Whitney test and Kruskal-Wallis test. • Two way ANOVA, interactions and multiple comparisons. • Three way ANOVA. • Split plot design. • Hierarchical models. Repeated measures. Mixed models. • Chi-square test for independence. • Spearman and Pearson correlation. • Simple linear regression and statistical inference. Multiple linear regression and statistical inference. Non linear regression. • Analysis of covariance (ANCOVA). • Survival analysis. • Design of experiments: factorial design and optimal design. • Calculation of size determination. • Sampling methods (Bootstrap, Jackknife, permutations and Monte-Carlo). • Interpretation of experimental data.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (P0588D140003) Bioinformatics and Computational Biology K Czech Ostrava Choice-compulsory type B study plan
2021/2022 (P0588D140003) Bioinformatics and Computational Biology P Czech Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140003) Bioinformatics and Computational Biology P Czech Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140003) Bioinformatics and Computational Biology K Czech Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner