LF:MGAM021 Analysis and Data Management - Course Information

MGAM021 Analysis and Data Management for Healthcare Specialisation

Extent and Intensity

2/1. 3 credit(s). Type of Completion: k (colloquium).

Teacher(s)

prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)

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

Prerequisites

Basic computer skills.

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

• Gerontology Nursing (programme LF, N-OS)

Course objectives

The course is oriented on practical basics of data analysis and information technologies application in medicine. 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).

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

• 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

Literature

• ALTMAN, Douglas G. Practical statistics for medical research. 1st ed. Boca Raton: Chapmann & Hall/CRC. xii, 611. ISBN 0412276305. 1991. info
• HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia. 476 s. ISBN 8020000801. 1993. info
• MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus. 839 s. ISBN 80-85297-56-6. 1994. info
• ZAR, Jerrold H. Biostatistical analysis. 4th ed. Upper Saddle River, N.J.: Prentice Hall. [941] s. ISBN 013081542X. 1999. info
• CHOW, Shein-Chung and Jen-Pei LIU. Design and analysis of clinical trials : concepts and methodologies. 2nd ed. Hoboken, N.J.: Wiley-Interscience. xiii, 729. ISBN 0471249858. 2004. info
• MCFADDEN, Eleanor. Management of data in clinical trials. New York: John Wiley & Sons. xi, 210. ISBN 047130316X. 1998. info
• MEINERT, Curtis L. Clinical trials : design, conduct, and analysis. Edited by Susan Tonascia. New York: Oxford University Press. xxvi, 469. ISBN 0195035682. 1986. 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
• McFaden, Eleanor. Managenent of data in clinical trials. 1st ed. New York: John Willey & Sons, 1998. xi, 210s, ISBN 0-471-30316-X
• 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. xii, 266. ISBN 0471901555. 1999. 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

Czech

• Enrolment Statistics (autumn 2018, recent)
  
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