LF:DSAN01 Data analysis for Neuroscience - Course Information
DSAN01 Data analysis for NeuroscienceFaculty of Medicine
- Extent and Intensity
- 2/0. 5 credit(s). Type of Completion: k (colloquium).
- prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Eva Koriťáková, 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
- PROGRAM ( D - NE4 ) || PROGRAM ( D - NR4 ) || PROGRAM ( D - PC4 ) || PROGRAM ( D - RA4 )|| PROGRAM ( D - FA4 )
Basic knowledge of biostatistics and data analysis. It is an advantage to attend more theoretical course of Clinical data analysis beforehand.
- Course Enrolment Limitations
- The course is only offered to the students of the study fields the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20
- fields of study / plans the course is directly associated with
- there are 20 fields of study the course is directly associated with, display
- Course objectives
- The course is based on principles of basic methods of medical data analysis with respect to particularities of data files in the neuroscience research. The main emphasis will be laid on correct application of the methods and interpretation of results. Theory will be followed by practical demonstrations in software STATISTICA or another software which is freely available at Masaryk University.
- 1.How to describe and visualize medical data correctly: Types of medical data and their visualization. Descriptive statistics – mean, median, quantiles, variance, standard deviation, confidence intervals. Normal distribution and derived distributions – chi-square distribution, Student’s t-distribution, etc. Data transformation – normalisation, standardization, categorization.
- 2.How to test medical data correctly: Formulation of hypotheses in medical research – the null and alternative hypotheses. The significance level and power analysis. p-value. Proper choice of the type of the test in various situations. One-sample tests – z-test, one-sample t-test, paired t-test.
- 3.How to use parametric and non-parametric tests I: Two-sample t-test. Non-parametric tests – Wilcoxon test, Mann-Whitney test etc. F-test.
- 4.How to use parametric and non-parametric tests II: Analysis of variance (ANOVA) and its assumptions. Multiple testing problems and correction procedures – Bonferroni correction, FDR. Correct application of these corrections. Kruskal-Wallis test.
- 5.How to analyse categorical and binary data I: Analysis of contingency tables – Pearson’s chi-square test, Fisher’s exact test, McNemar’s test. Relative risk and odds ratio. Binomial and Poisson distribution.
- 6.How to analyse categorical and binary data II: Analysis of diagnostics tests – sensitivity, specificity, positive and negative predictive values, likelihood ratio. Examples of correct and incorrect analyses of power of diagnostic tests. Detection of diagnostic cut-off points using ROC curves.
- 7.How to analyse associations between continuous variables and how to use basic regression models: Basics of correlation analysis – Pearson’s and Spearman’s correlation coefficients. Basics of regression analysis – linear regression, regressing out covariates.
- 8.How to analyse survival of patients: Survival analysis. Cox regression.
- Zar, J.H. (1998) Biostatistical analysis. London: Prentice Hall, 4th ed.
- HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993. 476 s. ISBN 8020000801. info
- Benedík, J., Dušek, L. (1993) Sbírka příkladů z biostatistiky. Brno: Konvoj.
- Teaching methods
- Teaching is interactive and based on solving real problems and examples. The examples and study materials will be available beforehand. During the first lecture, students will be told to prepare examples of problems in their analyses (PhD theses, research activities etc.). These problems will be discussed and solved in following lectures.
- Assessment methods
- Course is finished by colloquium, consisting of analyses of sample data files using computer.
- Language of instruction
- Further comments (probably available only in Czech)
- Study Materials
The course is taught annually.
The course is taught: in blocks.
Note related to how often the course is taught: 8 dnů po 3 hod.