# Biostatistics

Subject code: P-ITMAT-0023
Coordinator: Dr. Juhász János
Weekly number of classes: 2
Used programs: R
Assessment and evaluation: exam, specific works

### Purpose of the training

• Understand and manage random fluctuations in natural phenomena.
• Introduction of the methodology of evaluation of research and measurement results.
• Acquire the knowledge needed to understand the scientific literature.

Biostatistics is an innovative field that involves the design, analysis, and interpretation of data for studies in public health and medicine. Biostatistics experts arrive at conclusions about disease and health risks by evaluating and applying mathematical and statistical formulas to the factors that impact health. By looking at empirical data, such as the outcome of a clinical trial, you can predict whether a given medical treatment will help a sick patient. Choose this subject if you have a mathematics background and a strong interest in biology and public health. This program emphasizes statistical theory and methods and will prepare you to design, execute, and collaborate on all types of studies as well as contribute to the methodological development of biostatistics.

### Topics

1. Probability theorem, Discrete and Continuous Probability Distributions. Data scales, data reductions, data distributions, and representations. Genetic applications.
2. Decision theorem: significance level, hypothesis, type I-IT errors, and its handlings. Power analysis, sample size calculations. Confidence limits.
3. Parametric tests 1.: one sample, paired and two-sample t-tests. Effect size calculations.
4. Parametric tests IT.: the principle of the analysis of variance (ANOVA). ANOVA tables. Different ANOVA models, post-hoc tests.
5. Parametric tests IIT.: Mixed models.
6. Nonparametric tests: (sign, Wilcoxon rank-test, Mann- Whitney U-test, Friedman ANOVA, Kruskal- Wallis test). Permutation tests.
7. Contingency tables: khi-square test, Fisher-test, trend analysis.
8. Classification functions: sensitivity, specificity, OR, RR calculations. ROC analysis.
9. Linear and nonlinear regressions (GLM/GLZ models): simple, multiple, Ridge regression.
10. Logistic regression.
11. Survival analysis L: life-table and Kaplan-Maier methods.
12. Survival analysis IT.: Cox-model.
13. Multivariate exploratory techniques I: PCA, cluster, discriminant.
14. Multivariate exploratory techniques IL: classification threes, factor, item analysis.

### Exercises

1. Probability calculations.
2. Descriptive statistics: means standard deviations etc. CT calculations, histograms, power analysis.
3. Statistical hypothesis testing I.: t-tests.
4. Statistical hypothesis testing I: ANOVA methods, post-hoc tests.
5. Statistical hypothesis testing III.: Mixed-model.
6. Statistical hypothesis testing IV.: Nonparametric tests (sign, Wilcoxon rank-test, Mann- Whitney U-test, Friedman ANOVA, Kruskal-Wallis test).
7. Contingency tables I.: evaluations of contingency tables.
8. Contingency tables IL: epidemiological studies.
9. Regression analysis: correlation calculations, regression studies.
10. Logistic regression: problem-solving exercises.
11. Survival analysis I.: problem-solving exercises.
12. Survival analysis IL.: problem-solving exercises.
13. PCA, cluster, discriminant problem-solving exercises.
14. Classification threes, factor, item analysis problem-solving exercises.