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
Population - sample, sampling techniques
Representativity of the sample
Principles of statistical testing
Statistical hypothesis and significance level
Mathematical tools for planning surveys and experiments, sample size determination
One-sided and two-sided hypotheses
Types of variables
Descriptive statistics (characteristics of location and variability)
Probability, distribution
Point and interval estimation, standard error
Confidence interval
One-sample, two-sample, and paired tests
Parametrical and non-parametrical tests 2. Statistical methods in medical research
Testing hypotheses concerning the location (t-test, Wilcoxon test, analysis of variance)
Contingency and 2-by-2 tables, methods for comparison of proportions
Chi-square test, Fisher' and McNemar tests, test for trend
Association between two variables: correlation, regression
Advanced statistical methods in epidemiology (logistic regression, censored data, survival analysis)
Evaluation of diagnostic and screening tests (sensitivity and specificity, cut-off point)
Basic types of studies used in epidemiology and related statistical models for their evaluation
Confounding, bias, precision
Interpretation of results of statistical procedures 3. Self-study
Vital statistics, rates and ratios
Odds ratio, relative risk, attributable risk
PRACTICALS
Descriptive statistics and statistical inference: types of variables, characteristics of location and variability, standard error, histogram, point and interval estimation, confidence interval, probability distribution (binomial, Poisson, normal), population and sample, sampling methods
Statistical testing: 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, Statistical tests for categorical variables: contingency table, chi-square test, McNemar test, planning: power of statistical test, sample size determination, type I and type II errors
Statistical association: statistical association: correlation, linear regression, logistic regression, Classification and classificators
Observational trials, bias and confounding: Statistical methods in epidemiology: epidemiological measures of risk and corresponding confidence intervals, confounding, interpretation, Evaluation of data from surveillance and registries, multiplicity testing
Advanced statistical methods: Survival analysis: clinical trials, Kaplan-Meier curve, log-rank test, practical use of statistics: statistics in published medical papers
Annotation
The subject covers introduction to biostatistics, principles of statistical reasoning, testing, and interpretation of the analyses.