MIKAM021p Data Management and Analysis for Medical branches - lecture

Faculty of Medicine
spring 2021
Extent and Intensity
0.3/0/0. 1 credit(s). Type of Completion: k (colloquium).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Denisa Krejčí (seminar tutor)
Mgr. et Mgr. Jiří Kalina, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
Silvie Doubravská (assistant)
Michaela Gregorovičová (assistant)
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 Doubravská
Supplier department: Institute of Biostatistics and Analyses - Other Departments for Educational and Scientific Research Activities - Faculty of Medicine
Prerequisites
MIKVO011p Nursing research - lecture
Basic experience with computer.
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 data analysis to students of clinical study specialisations. 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
At the end of the course the students are able to:
- define structure of dataset for statistical analysis;
- visualize the data and interpret data visualisation;
- identify correct methods of descriptive statistics;
- formulate hypothesis for statistical testing;
- select the correct statistical tests for hypotheses confirmation/refusal;
- interpret results of statistical evaluation, both analysis of own data and statistics in scientific literature;
- assess the applicability of statistical methods on various types of data.
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
  • ZAR, Jerrold H. Biostatistical analysis. 5th ed. Upper Saddle River, N.J.: Prentice Hall, 2010. xiii, 944. ISBN 9780131008465. info
  • POCOCK, Stuart J. Clinical trials : a practical approach. Chichester: John Wiley & Sons, 1999. xii, 266. ISBN 0471901555. info
  • MCFADDEN, Eleanor. Management of data in clinical trials. 1st ed. New York: John Wiley & Sons, 1998. xi, 210. ISBN 047130316X. info
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993. 476 s. ISBN 8020000801. info
  • ALTMAN, Douglas G. Practical statistics for medical research. 1st ed. Boca Raton: Chapmann & Hall/CRC, 1991. xii, 611. ISBN 0412276305. info
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
Course is finished by written exam (colloquium) aimed on principles, assumptions and correct selection of methods for solution of practical examples.
Language of instruction
Czech
Further comments (probably available only in Czech)
Information on the extent and intensity of the course: 5.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2015, Spring 2016, Spring 2017, Spring 2018, spring 2019, spring 2020.
  • Enrolment Statistics (spring 2021, recent)
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