DSAK051 Clinical data analysis

Faculty of Medicine
autumn 2024
Extent and Intensity
2/0. 5 credit(s). Type of Completion: k (colloquium).
In-person direct teaching
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
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: Institute of Biostatistics and Analyses – Other Departments for Educational and Scientific Research Activities – Faculty of Medicine
Prerequisites
None - basic course.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Intensive course for PhD students, doctors or specialists of other specializations. The course is aimed on basic principles of analysis of data, especially clinical data; it should provide information on graphical presentation of data, hypothesis testing together with the basics of multivariate analysis, survival analysis and predictive modelling of clinical data. The students will be able to understand principles of statistical tests, multivariate analysis and predictive modelling and will be provided by set of information sources of data analysis (books, journals, www pages). Examples provided in SW STATISTICA for Windows are the integral part of the course.
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
  • Introduction to statistics, testing of hypotheses.
  • Tables of distribution functions. Sampling from biological populations, data processing.
  • Introduction to sampling design. Continuous, ordinal and nominal data in biology.
  • Distribution of continuous and bivariate variables - testing of hypotheses, graphical methods.
  • Application of binomial and Poisson distribution in biology.
  • One sample testing: sample mean, median, standard deviation, variance, binomial p and Poisson constant.
  • Two sample testing. Experimental design - randomized and blocked. Parametric and non - parametric methods.
  • Application of goodness-of-fit test in biology, analysis of R x C contingency tables, discrimination of categorical data.
  • Measures of similarity in ecology (covariance, correlation coefficients, similarity coefficients).
  • Analysis of variance (ANOVA): one-way and two-way model.
  • Simple linear regression. Linear regression. Introduction to multivariate linear regression.
  • Experimental design: one-way and two-way models; factorial design, randomized blocks. Laboratory and field trials. Nested design of ANOVA in genetics and ecology. Non - parametric ANOVA.
Literature
  • Zar, J.H. (1994) Biostatistical methods. Prentice Hall, London. 2nd ed.
  • J. Benedík, L. Duąek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. 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
English
Further comments (probably available only in Czech)
The course is taught annually.
Information on the per-term frequency of the course: 2 kurzy ročně.
The course is taught: in blocks.
Note related to how often the course is taught: Výuka analýzy klinických dat proběhne v učebně B11/306 v termínu 6.-10. 1. 2025 vždy v čase 15-19 hod.
Information on the extent and intensity of the course: Výuka analýzy klinických dat proběhne v učebně B11/306 v termínu 6.-10. 1. 2025 vždy v čase 15-19 hod.
The course is also listed under the following terms Spring 2000, Autumn 2000, Spring 2001, Autumn 2001, Spring 2002, Autumn 2002, Spring 2003, Autumn 2003, Spring 2004, Autumn 2004, Autumn 2005, Spring 2006, Autumn 2006, Spring 2007, Autumn 2007, Spring 2008, Autumn 2008, Spring 2009, Autumn 2009, Spring 2010, Autumn 2010, Spring 2011, Autumn 2011, Spring 2012, Autumn 2012, Spring 2013, Autumn 2013, Spring 2014, Autumn 2014, Spring 2015, Autumn 2015, Spring 2016, Autumn 2016, Spring 2017, Autumn 2017, Spring 2018, autumn 2018, spring 2019, autumn 2019, spring 2020, autumn 2020, spring 2021, autumn 2021, spring 2022, autumn 2022, spring 2023, autumn 2023, spring 2024, spring 2025.
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