Course detail

Biostatistics

FEKT-ASTAAcad. year: 2019/2020

The course is an introduction to applied data analysis for students of biological and possibly clinical disciplines. The substance is discussed from theoretical principles (principles for statistical estimates, the existence of stochastic distribution, basic statistical tests), simple applications (one-sample and two-sample tests, correlation analysis) to the basics of stochastic modeling and experimental design (design of experiments, regression analysis, analysis of variance). Theory is always discussed in direct connection with practical examples. This course enables students to acquire the basic principles of biostatistical data analysis and prepares candidates for its sole use in their scientific work. The course will also focus on pratical training with the use of all available software tools (Statistica for Windows, SPSS).

Department

Faculty of Medicine, Masaryk University Brno (LF MU)

Learning outcomes of the course unit

After completing the course the student is able to:
• apply descriptive statistics on the given data
• select and use tools to test hypotheses
• graphically present statistical data
• clinical studies suggest
• work with databases and statistical programs

Prerequisites

Knowledge at the secondary school level of mathematics, PC, work with MS Office, particularly MS Excel is required.

Co-requisites

Not applicable.

Recommended optional programme components

Not applicable.

Recommended or required reading

ALTMAN, Douglas. Practical statistics for medical research. London: Chapman and Hall, 1991. ISBN 0412276305. (EN)
HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993. ISBN 80-200-0080-1. (CS)
MELOUN, Milan a Jiří MILITKÝ. Statistické zpracování experimentálních dat. 1. vyd. Praha: Plus, 1994. ISBN 80-85297-56-6. (CS)
ZAR, Jerrold. Biostatistical analysis. New Jersey: Prentice Hall, 1984. ISBN 978-0321656865. (EN)
CHOW, Schein-Chung a Jen-Pei LIU. Design and analysis of clinical trials: concepts and methodologies. 2. vyd. John Wiley and Sons, 1998. ISBN 978-0-471-24985-6. (EN)
McFADDEN, Eleanor. Management of data in clinical trials. 2. vyd. John Wiley and Sons, 2007. ISBN 978-0-470-04608-1. (EN)
MEINERT, Curtis. Clinical trials: design, conduct, and analysis. Oxford University Press, 1996. ISBN 0195035682. (EN)
Vyhláška o správné klinické praxi a bližších podmínkách klinického hodnocení léčivých přípravků. In: Sbírka zákonů České Republiky, 2008, částka 72, s. 3246-3297. Dostupná z: http://aplikace.mvcr.cz/sbirka-zakonu/SearchResult.aspx?q=2010&typeLaw=zakon&what=Rok&stranka=8. (CS)
Zákon 101/2000 Sb., o ochraně osobních údajů a o změně některých zákonů. In: Sbírka zákonů České Republiky, 2000, částka 32. Dostupný z: http://www.mpsv.cz/ppropo.php?ID=z101_2000o. (CS)

Planned learning activities and teaching methods

Teaching methods depend on the type of course unit as specified in the article 7 of BUT Rules for Studies and Examinations.

Assesment methods and criteria linked to learning outcomes

Compliance with the requirements for terminating exercise: participation in seminar, final exam for more than 50% points.
Written exam for more than 40% of the points.
The final exam is focused on understanding of statistical data analysis and their applications.

Language of instruction

Czech

Work placements

Not applicable.

Course curriculum

1. Introduction to statistics, hypothesis testing. Stochastic distribution, distribution function, frequency tables, percentiles. Tables of model distribution. Withdrawals from biological populations, data processing. Introduction to planning choices.
2. Continuous, ordinal and nominal data in biology. Estimates of selection parameters. The percentages and indices as derived biological data.
3. The distribution of continuous variables - testing hypotheses, graphical methods. The distribution of binary variables - testing hypotheses, graphical methods.
4. One-sample tests. Testing hypotheses about population parameters selection: sample mean, median, standard deviation, variance. Sampling and experimental design to test the parameters of the selection populations.
5. Application of binomial and Poisson distribution in biology, modeling using the binomial distribution. One-sample tests of binomial parameter pa Poisson's constant.
6. Comparing the two selection parameters populations. Experimental plans - a completely randomized pair. Parametric and nonparametric methods. Formal presentation of the comparison of two sample populations in the literature. Graphical methods.
7. The analysis of binary and ordinal data. Goodness: genetics, molecular biology, ecology. Analysis of R x C contingency tables, discrimination of categorical data. Binomial test and a test of homogeneity of binomial frequency.
8. Correlation analysis. Parametric and serial correlation (covariance, correlation coefficients, coefficients of similarity). Correlation and covariance matrix. Partial correlations.
9. Analysis of variance (ANOVA) models a simple classification for experimental and ecological data. Non-parametric methods of analysis of variance.
10. Way classification ANOVA, testing the interaction of one or more experimental interventions, formal presentation of the results of analysis of variance.
11. Introduction to regression analysis. Regression analysis of the line. Analysis of variance in the regression analysis line. Linear regression. Residual analysis of regression models.

Aims

To provide the students with sufficient knowledge for clinical data acquisition, organisation and statistical analysis including presentation and interpretation of outcomes

Specification of controlled education, way of implementation and compensation for absences

Participation in seminar is mandatory, two absences are permitted. In the case of multiple absences, it is possible to substitute the seminar after the agreement with teacher (ideally in another parallel group).

Classification of course in study plans

  • Programme BTBIO-A Bachelor's

    branch A-BTB , 2. year of study, winter semester, 5 credits, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Laboratory exercise

26 hours, compulsory

Teacher / Lecturer