Data Analysis in Medicine
- To develop knowledge of concepts underlying medical research.
- To develop practical skills needed in modern medical data analysis.
- Student is acquainted with content of the course and main problems of the field.
- Student is able to compare independent and dependent groups using parametric and non-parametric tests and confidence intervals for appropriate statistics.
- Student is able to compare independent and dependent groups using statistical tests and confidence intervals for appropriate statistics.
- Student knows basics of trial design and statistical aspects of the trial protocol. Student is able to allocate cases into groups using different types of randomisation.
- Student knows the basics of statistical estimation of medical tests accuracy and is able to cakcuate point and interval estimates for appropriate parameters.
- Student knows the idea of meta-analysys and indirect comparisons and is able to perform the basic calculations.
- Student knows the specific issues of machine learning in medicine and is able to develop regression and classification models to solve medical probems.
- Student is able to apply factor analysis or K-means or closed descriptions to solve medical problem.
- Student knows the purposes and approaches to develop multi-criteria models.
- Student knows the idea of clinical economical analysis.
- Topic 1. Introduction to data analysis in medicineEvidence based medicine, health technology assessment, role of statistical analysis, mathematical models as medical technologies
- Topic 2. Basics for quantitative data analysis in medicineDistributions, parametric and nonparametric descriptive statistics, hypothesis testing, confidence intervals, comparison of independent and dependent samples (parametric and nonparametric tests), comparison of 2 and more samples, correlation, multiple comparisons problem.
- Topic 3. Basics for qualitative data analysis in medicine.Descriptive statistics, hypothesis testing, confidence intervals, comparison of independent and dependent samples, comparison of 2 and more samples, association.
- Topic 4. Experimental trials of treatments.Design of trials, planning clinical trials of treatments, types of systematic bias, effect measures, data analysis, international guidelines for reporting.
- Topic 5. Studies of diagnostic and screening tests.Design of trials, planning trials of diagnostic and screening tests, types of systematic bias, effect measures, data analysis, international guidelines for reporting.
- Topic 6. Meta-analysis. Indirect comparison.Purposes, systematic review methodology, meta-analysis procedure, publication bias, heterogeneity, models for meta-analysis and indirect comparisons, sensitivity analysis, international guidelines for reporting.
- Topic 7. Machine learning for medicine.Specific problems of machine learning in medicine, decision support systems development approaches, general issues, regression models, classification models.
- Topic 8. Data mining for medicine.Factor analysis, K-means, closed descriptions.
- Topic 9. Multicriteria decision analysis for medicine.Purposes, approaches, stages, methods of weighting, models.
- Topic 10. Clinical economic analysis.Purposes, methods (cost-effectiveness, etc.), modeling (decision tree, Markov model).
- Interim assessment (2 module)0.2 * Class attendance + 0.4 * Exam + 0.2 * Home work + 0.2 * Intermediate test
- Pardalos P. M., Coleman T. F., Xanthopoulos P. (ed.). Optimization and Data Analysis in Biomedical Informatics. – Springer Science & Business Media, 2012.
- Riffenburgh, R. H. (2006). Statistics in Medicine (Vol. 2nd ed). Amsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=248877
- Riffenburgh, R. H. (2012). Statistics in Medicine (Vol. 3rd ed). Amsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=465058
- Pigott, T. Advances in meta-analysis. – Springer Science & Business Media, 2012.