MIAM021p Data Management and Analysis for Medical branches - lecture

Faculty of Medicine
spring 2021
Extent and Intensity
1/0/0. 1 credit(s). Type of Completion: k (colloquium).
Taught online.
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Michal Svoboda (lecturer)
RNDr. Michaela Cvanová (seminar tutor)
Mgr. Michal Uher (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
Supplier department: Institute of Biostatistics and Analyses - Other Departments for Educational and Scientific Research Activities - Faculty of Medicine
Tue 2. 3. 11:00–13:30 D29/347-RCX2, Tue 9. 3. 11:00–13:30 D29/347-RCX2, Tue 16. 3. 11:00–13:30 D29/347-RCX2, Tue 23. 3. 11:00–13:30 D29/347-RCX2, Tue 30. 3. 11:00–13:30 D29/347-RCX2, Tue 6. 4. 11:00–13:30 D29/347-RCX2, Tue 13. 4. 11:00–13:30 D29/347-RCX2, Tue 20. 4. 11:00–13:30 D29/347-RCX2, Tue 27. 4. 11:00–13:30 D29/347-RCX2, Tue 4. 5. 11:00–13:30 D29/347-RCX2
Prerequisites (in Czech)
MIVO011p Nursing research - lecture
Předpokladem je pouze základní zkušenosti s prací na PC.
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.
  • A. Data analysis 1. Basic principles of statistical analysis. Probability in presentation of analysis results. Basics of experimental design and hypothesis testing.; Nominal, ordinal and continuous data in clinical research and their visualization. Special characteristics of clinical data and their subsequences for analysis. Description of data, descriptive statistic, distribution. Calibration, prognosis, models.; 2. Statistical distributions and their usage as model distribution (normal, log-normal, binomial, Poisson, Student, F, Chi square); Confidence intervals, estimation of statistical parameters and their presentation. Estimation of arithmetic mean, geometric mean, median and variability. Statistical summary of discrete and continuous data. 3. Univariate analysis of continuous data. One-sample and two-sample test. T-test for dependent and independent data. Basics of analysis of variance one way and multi-way ANOVA, post-hoc tests. Non parametric tests (Mann-Whitney test, Wald-Worowitz test, Kolmogorov-Smirnov two-sample test, Kruskal-Wallis test). Visualization and presentation of results of statistical tests.; * Univariate analysis of discrete data. One-sample and two-sample test. Presentation and estimation of percentages data. Binomial test, Fisher exact test, goodness of fit test, analysis of frequency tables. 4. Basics of correlation and regression analysis. Parametric and non-parametric correlation. Linear regression. Application and visualization of correlation and regression. Basic principles of polynomial and non-linear regression.; Basic principles of multivariate and logistic regression. Multivariate and logistic regression as predictive tools for clinical data. Quality of models and their problems. Multivariate regression in prediction of clinically important parameters example. Logistic regression individualized prediction of patients. Presentation of predictive models. 5. Survival analysis. Probability of survival. Kaplan-Meier survival analysis and parameters estimates /median survival times.../. Range of approaches for comparison of two or more survival curves /Log-rank test, hazard ratio, log rank for trends, confidence intervals for survival probability/. "Cohort life tables" and their analysis of survival. Modeling of survival, Cox regression. Examples and application. Design of studies focused on survival analysis quantitative aspects of experimental design, samples size estimation. Survival analysis for stratified clinical trials. EORTC standards for experimental design of survival analysis. Internet and survival analysis: consultation on trials aimed on survival analysis, software for survival analysis. Nomograms for design of survival analysis trials. 6. Multivariate analysis of clinical data; introduction into modern method for analysis of huge amounts of data. Principles of multivariate methods and their application for clinical data analysis. Multivariate and univariate data analysis mutual collaboration or discrepancy? Multivariate data exploration, available tests for multivariate distribution. Multivariate similarity/distance of objects or variables review of important metrics. Dynamic regression models. Neural networks as a possible modeling technique. Data mining and automated analysis of data. Experiments optimizing; application of multivariate methods in sampling design. B. Information technologies 7. Network - data transfer, hardware, software; Network - Internet. Types of nets, IP - network. Internet; How to connect to the Internet 8. Client x server architecture, Clients, Servers, Services; Network services. FTP - file transfer, Sharing disks and printers, E-mail, services SMTP, POP3, IMAP. Other services, Remote desktop, telnet, talk, Skype; 9. Authorization, Authentication, Login, Password, Cryprography; Security, reducing risk of networks transfer and communication C. Management of clinical trial 10. Terminology, legal topics 11. Data analysis in clinical trial, design of experiment, power analysis 12. Randomisation and monitoring of clinical trials
    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
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994. 839 s. ISBN 80-85297-56-6. info
  • ZAR, Jerrold H. Biostatistical analysis. 4th ed. Upper Saddle River, N.J.: Prentice Hall, 1999. [941] s. ISBN 013081542X. info
  • CHOW, Shein-Chung and Jen-Pei LIU. Design and analysis of clinical trials : concepts and methodologies. 2nd ed. Hoboken, N.J.: Wiley-Interscience, 2004. xiii, 729. ISBN 0471249858. info
  • MCFADDEN, Eleanor. Management of data in clinical trials. 1st ed. New York: John Wiley & Sons, 1998. xi, 210. ISBN 047130316X. info
  • Clinical trials : design, conduct, and analysis. Edited by Curtis L. Meinert - Susan Tonascia. [1st ed.]. New York: Oxford University Press, 1986. xxvi, 469. ISBN 0195035682. info
  • Norleans M. X. Statistical methods for clinical trials. Marcel Dekker. 2001. 257 pp.
  • Předpis 472/2000 Sb., Vyhláška Ministerstva zdravotnictví a Ministerstva zemědělství, kterou se stanoví správná klinická praxe a bližší podmínky klinického hodnocení léčiv
  • MCFADDEN, Eleanor. Management of data in clinical trials. 1st ed. New York: John Wiley & Sons, 1998. xi, 210. ISBN 047130316X. info
  • Předpis 101/2000 Sb., Zákon o ochraně osobních údajů a o změně některých zákonů
  • POCOCK, Stuart J. Clinical trials : a practical approach. Chichester: John Wiley & Sons, 1999. xii, 266. ISBN 0471901555. 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: 15.
Listed among pre-requisites of other courses
The course is also listed under the following terms Spring 2012, Spring 2013, Spring 2014, Spring 2015, Spring 2016, Spring 2017, Spring 2018, spring 2019, spring 2020, spring 2022.
  • Enrolment Statistics (spring 2021, recent)
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