MIAM021s Data Management and Analysis for Medical branches - practice

Faculty of Medicine
spring 2023
Extent and Intensity
0/1/0. 1 credit(s). Type of Completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
Mgr. Renata Chloupková (seminar tutor)
RNDr. Michal Svoboda (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Contact Person: Silvie Koutná
Supplier department: Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Timetable
Tue 14. 2. 11:00–13:30 D29/347-RCX2, Tue 21. 2. 11:00–13:30 D29/347-RCX2, Tue 28. 2. 11:00–13:30 D29/347-RCX2, Tue 7. 3. 11:00–13:30 D29/347-RCX2, Tue 14. 3. 11:00–13:30 D29/347-RCX2, Tue 21. 3. 11:00–13:30 D29/347-RCX2, Tue 28. 3. 11:00–13:30 D29/347-RCX2, Tue 4. 4. 11:00–13:30 D29/347-RCX2, Tue 11. 4. 11:00–13:30 D29/347-RCX2, Tue 18. 4. 11:00–13:30 D29/347-RCX2
Prerequisites
MIVO011s Nursing research - practice
None - basic course
Course Enrolment Limitations
The course is only offered to the students of the study fields the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course introduces practical data analysis to students of clinical study specialisations. The course accompanies lectures of MIAM021p and shows the computation of presented methods on PC using statistical software. Students will be trained in all aspects of data processing and its statistical analysis (descriptive statistics, one sample and two sample tests, categorical data analysis, ANOVA, correlation analysis, data visualisation).
Learning outcomes
Students will be able after the course to use the folowing data analysis methods:
- Descriptive statistics, data visualisation.
- Distribution of continuous variables.
- One sample tests - parametrical and nonparametrical.
- Two sample tests - parametrical and nonparametrical.
- Analysis of variance (ANOVA), Kruskal-Wallis test.
- Analysis of contingency table.
- Corelation, linear regression.
Syllabus
  • 1. Data processing – principles of correct data manipulation. MS Office Excel – appropriate tool for storing, organizing, and manipulating data.
  • 2. Introduction to statistics. Data types in medicine and biology; nominal, ordinal, continuous variable. Visualization of quantitative and qualitative (categorical) variables.
  • 3. Descriptive statistics. Mean, median, quantiles, variance. Frequency table.
  • 4. Distribution of continuous variables. Normal distribution, log-normal distribution.
  • 5. Principles of hypotheses testing. Definition of null and alternative hypothesis. Significance level. Type I and type II error.
  • 6. Graphical examining of normal distribution (histogram, normal-probability plot). Shapiro-Wilk test – a test of normality.
  • 7. Parametrical tests: t-tests. One-sample t-test, two-sample t-test, t-test for dependent samples.
  • 8. Analysis of variance ANOVA.
  • 9. Nonparametrical tests: one-sample Wilcoxon test, Mann-Whitney U test, Wilcoxon test for dependent samples, Kruskal-Wallis test.
  • 10. Definition of contingency table and its analysis: Pearson chi-square test, Fisher exact test, McNemar test.
  • 11. Correlation. Pearson correlation coefficient, Spearman correlation coefficient.
  • 12. Introduction to regression analysis. Linear regression.
Literature
    required literature
  • ALTMAN, Douglas G. Practical statistics for medical research. 1st ed. Boca Raton: Chapmann & Hall/CRC, 1991, xii, 611. ISBN 0412276305. info
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • ZAR, Jerrold H. Biostatistical analysis. 4th ed. Upper Saddle River, N.J.: Prentice Hall, 1999, [941] s. ISBN 013081542X. info
Teaching methods
Practical training using computers
Assessment methods
Exam based on correct application of statistical methods.
Language of instruction
Czech
Further comments (probably available only in Czech)
Information on the extent and intensity of the course: 15.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2016, Spring 2017, Spring 2018, spring 2019, spring 2020, spring 2021, spring 2022, spring 2024, spring 2025.
  • Enrolment Statistics (spring 2023, recent)
  • Permalink: https://is.muni.cz/course/med/spring2023/MIAM021s