MIKAM021p Data Management and Analysis for Medical branches - lecture

Faculty of Medicine
spring 2019
Extent and Intensity
0.3/0/0. 1 credit(s). Type of Completion: k (colloquium).
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
Mon 3. 6. 8:00–9:40 D29/347-RCX2, 9:50–11:30 D29/347-RCX2, 11:50–13:30 D29/347-RCX2, Tue 4. 6. 8:00–9:40 D29/347-RCX2, 9:50–11:30 D29/347-RCX2
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 is oriented on practical basics of data analysis and information technologies application in medicine. Highlighted topics are related to management of data of clinical trials and data storage in hospitals. The data analysis presented during the lectures goes from the descriptive statistics through principles of statistical testing, selected statistical tests for continuous and categorical data to basics of regression modeling and power analysis. All methods are presented using practical examples and common software (Statistica for Windows, SPSS). The subject provides basic knowledge a skills about computer's network. Main objectives can be summarized as follows: to understand the network terminology; to connect personal computer to Internet; to use network services; to reduce risk of lost of data or secret information.
Learning outcomes
The student is able to perform data analysis and data management of clinical trials.
  • Week 1 Data preparation and visualisation, data transformation, quality control, outliers detection, software.
  • Week 2 statistical tests for the evaluation of diagnostical tests: discrimination analysis, study subjects typology, ROC analysis, sensitivity, specificity.
  • Week 3 Survival analysis - basics.
  • Week 4 Epidemiology and population risks evaluation - basics.
  • Week 5 Standardisation of epidemiological data, trend analyses and predictions.
  • Week 6 Connection of user to PC, operation system, PC security. Networks, e-mail, data transfer.
  • Week 7 Information servers, WWW - URL, html. Databases, authorisation in networks.
  • Week 8 Data digitalisation in clinical studies; importance of data manager, validaton of data.
  • Week 9 Personal data security, legislative aspects of informatics in medicine.
  • Week 10 Clinical studies management.
  • Week 11 Data analysis in clinical studies, designs of clinical studies - paralel design, cross over and factorial design, clinical studies phase I-IV.
  • Week 12 Descriptive statistics and hypotheses testing in clinical studies.
  • Week 13 Power analysis, sample size optimalisation.
  • Week 14 Software tools for clinical studies management, data acquisition and data analysis.
  • Week 15 Solution of tasks in data analysis - preparation for diploma theses.
  • 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, prerequisties and correct selection of methods for solution of practical examples.
Language of instruction
Further comments (probably available only in Czech)
Study Materials
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 2020, spring 2021.
  • Enrolment Statistics (spring 2019, recent)
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