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Lecturer(s)
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Dušek Ladislav, PhDr. CSc.
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Hrach Karel, RNDr. Ph.D.
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Course content
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Lecture Topics: 1. Advanced methods of descriptive statistics: data aggregation and visualisation, specific types of graphs (distribution functions, box plots, ROC curves). 2. Probabilistic principles of statistical methods: types of probability distributions in clinical and managerial-organisational data. Bayesian approach. 3. Methods of statistical inference in healthcare: confidence intervals, hypothesis testing (parametric and non-parametric approaches), post-hoc analyses, statistical vs clinical significance. 4. Regression models: multiple linear regression (stepwise modelling methods), logistic regression, interpretation of coefficients. 5. Survival analysis: censored data, survival curves, Kaplan?Meier estimator, Cox proportional hazards regression model. Applications in evaluating treatment effectiveness and prognosis. 6. Exploratory data analysis: cluster analysis, principal component analysis (PCA). Examples of application to population and hospital data. 7. Advanced methods of time series analysis in healthcare: trends, seasonality, ARIMA models and their use in predicting healthcare capacity or epidemiological events. 8. Statistical process control (SPC) methods: control charts and examples of their use in monitoring quality and performance indicators in healthcare. Principles of Six Sigma strategy. 9. Selected advanced topics: big data and data mining (decision trees, neural networks, AI applications), stochastic methods (meta-analysis, bootstrapping, Monte Carlo methods). 10. Healthcare data sources (Institute of Health Information and Statistics of the Czech Republic ? ÚZIS, Czech Statistical Office ? ČSÚ). Legal and ethical aspects of statistical processing of healthcare data (informed consent, GDPR, cybersecurity).
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Learning activities and teaching methods
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unspecified
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Learning outcomes
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The course *Statistics in Healthcare Management* is focused on a comprehensive understanding of both theoretical and practical aspects of the use of advanced statistical methods in healthcare, with regard to their application in healthcare research, quality of care management, and the efficiency of healthcare organisations. In addition to theoretical knowledge, students are introduced to the use of appropriate software tools, particularly the R programming language.
After completing the course, the student will: 1. Know advanced statistical methods and their use in the analysis of healthcare data. Understand the principles of processing, interpretation, and presentation of healthcare data with regard to their structure and context. 2. Be able to independently design and conduct statistical analyses of healthcare data using appropriate methods and software tools. Critically evaluate statistical results, interpret them in a healthcare context, and present conclusions in a relevant way. Apply statistics in decision-making, planning, and improving the quality of healthcare. 3. Be able to creatively use statistical knowledge in solving both research and practical problems in healthcare. 4. Be able to effectively communicate the results of statistical analyses to both professional and non-professional audiences and support decision-making processes with them. 5. Be capable of working independently as well as in multidisciplinary teams and handling data ethically.
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Prerequisites
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unspecified
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Assessment methods and criteria
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unspecified
Seminar paper: The student will independently process a given dataset (ideally related to their dissertation project) and present the results in the form of a colloquium.
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Recommended literature
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Datové zpravodajství [online]. https://www.nzip.cz/modul/datove-zpravodajstvi.
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Vliv sledování indikátorů kvality na porodních odděleních na zdraví matek a kojenců (MATICUS) [online]. https://ichgcp.net/cs/clinical-trials-registry/NCT01459978.
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[1] Fang, Ji-Qian (ed.), 2018. Handbook of Medical Statistics. New Jersey: World Scientific..
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[1] Jones, Elinor, Simon Harden a Michael J. Crawley, 2022. The R-Book. Wiley..
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[2] Eva, J., & Darja, N. (2015). Pokročilejší metody statistické regulace procesu. Grada Publishing as..
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[2] Walters, S. J., Campbell, M. J., & Machin, D. (2021). Medical statistics: a textbook for the health sciences. John Wiley & Sons..
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[3] Meloun, M., Militký, J., & Hill, M. (2017). Statistická analýza vícerozměrných dat v příkladech. Univerzita Karlova v Praze. Nakladatelství Karolinum..
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[4] Nováková, K, Veselý P. (2021). Jazyk R a tvorba grafů. Praha: Grada Publishing.
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