Syllabus
LECTURES 1. Statistical concepts and terms, statistical inference
Statistics in medical sciences
Logic of statistical reasoning (observations vs. hypotheses)
Important steps in the application of statistics
Types of variables
Descriptive statistics (characteristics of location and variability)
Probability, distribution
Population - sample, sampling techniques
Representativity of the sample
Point and interval estimation, standard error
Confidence interval
Principles of statistical testing
Statistical hypothesis and significance level
One-sided and two-sided hypotheses
One-sample, two-sample, and paired tests
Parametric and nonparametric tests
Testing hypotheses concerning the location (t-test, Wilcoxon test, analysis of variance)
Interpretation of results of statistical procedures 2. Statistical methods in medical research
Contingency and 2-by-2 tables, methods for comparison of proportions
Chi-square test, Fisher’s and McNemar’s tests
Basic types of studies used in epidemiology and related statistical models for their evaluation
Vital statistics, rates and ratios
Odds ratio, relative risk, attributable risk
Confounding, bias, precision
Association between two variables: correlation, regression
Advanced statistical methods in epidemiology (logistic regression, censored data, survival analysis)
Mathematical tools for planning surveys and experiments, sample size determination
PRACTICALS 1. Statistical concepts: types of variables, probability distribution (binomial, Poisson, normal), population and sample, sampling methods, characteristics of location and variability, standard error, histogram, point and interval estimation, confidence interval 2. Statistical inference: testing statistical hypotheses, p-value, significance level
Statistical tests for continuous variables: parametric and nonparametric tests, t-test and Wilcoxon test (one-sample, two-sample, paired), analysis of variance (ANOVA), F-test 3. Statistical tests for categorical variables: contingency table, chi-square test, McNemar’s test
Statistical methods in epidemiology: epidemiological measures of risk and corresponding confidence intervals, confounding, interpretation, evaluation of data from surveillance and registries 4. Statistical association: correlation, linear regression, logistic regression
Survival analysis: clinical trials, Kaplan-Meier curve, log-rank test
Planning surveys: power of statistical test, sample size determination, type I and type II errors
Practical use of statistics: statistics in published medical papers
Credit test
Annotation
The subject covers introduction to biostatistics, principles of statistical reasoning, testing, and interpretation of the analyses.