Advanced Methods in Biostatistics   (MH3NMB)

Academic year:

2026/2027.

Attendance requirements:

There are no requirements.

ECTS:

5

Study level:

Study program:

Teachers:

Nataša Milić

Filip Lj. Andrić

Biljana Miličić

Zoran Bukumirić

Ognjen Milićević

Hours of instruction:

Weekly: two hours of lectures + two hours of exercises (2+2+0)

Goals:

Acquainting students with advanced elements of biostatistical data processing in biomedical and biochemical sciences in a clear, comprehensible, and practical way, and necessary for a) statistical thinking especially in scientific work, b) understanding and correct perception of statistical aspects in scientific publications and professional literature, c) understanding and practical application of a wide range of statistical tools relevant to the organization and interpretation of the results of clinical studies, biochemical processes, metabolomic data, etc. d) collection and preparation of data; e) proper selection and application of appropriate advanced data processing techniques; f) interpretation of statistical processing results. In this regard, the special goal of the course is to present the basic concepts of advanced data processing first of all, in an understandable and intuitive way, deviating from a rigorous mathematical explanation; special attention is devoted to defining and solving specific examples and problems that arise in practice.

Outcome:

Upon completing this course, students will be able to understand and effectively apply advanced data processing techniques in scientific research, while actively engaging with professional literature. Through independent computer-based work, students will utilize specialized software packages for advanced data analysis in biomedical and biochemical sciences. They will demonstrate proficiency in statistical significance testing, pattern recognition, and linear modeling, while also being able to draw informed conclusions and interpret the results of data analysis.

Teaching methods:

Lectures and theoretical exercises.

Extracurricular activities:

—

Coursebooks:

1. Erić-Marinković J, Dotlić R, Janošević S. i sar: Statistika za istraživače u oblasti medicinskih nauka, četvrto izdanje, Medicinski fakultet, Beograd, 2012.
2. Chernik M. R., Friis R. H.: Introductory Biostatistics for the Health Sciences, Wiley-Interscience, John Wiley & Sons, 2003.
3. B. Dawson, R. G. Trapp: Basic and Clinical Biostatistics, Lange Medical Books / McGraw-Hill Companies Inc., New York, 2004.
4. K. Varmuza, P. Filmoser: Introduction to Multivariate Statistical Analysis in Chemometrics, CRC Press, Taylor and Francis Gorup, Boca Raton, 2009.
5. Richard G. Brereton: Chemometrics: Data Analysis for the Laboratory and Chemical Plant, Wiley, 2003.

Additional material:

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Lectures:

10 points (2 hours a week)

Syllabus:

Statistical concepts and terms; Research planning and design; Research database; Data exploration and preparation (data types and variables); Statistical analysis of clinical study data involving categorical variables (contingency table significance testing: Chi-square test, McNamara test, Fisher's exact test). Statistical analysis of clinical data including different disease scales (logistic regression). Survival analysis. Analysis of multivariate data in biochemical research. Preparation and transformation of biochemical data (metabolomic, proteomic, and analytical signals generated using spectroscopic, chromatographic, and electrophoretic techniques). Exploratory analysis of metabolomic data using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Linear modeling of biochemical data (multiple linear regression (MLR), principal component regression (PCR), partial least squares (PLS) regression, linear discriminant analysis (LDA), discriminant analysis coupled with the method of partial least squares (PLS-DA), cross-validation techniques, regression model quality parameters, variable selection and assessment of optimal model complexity).

The literature study will be conducted according to the Journal Club model, or rather a discussion forum, where students, together with teachers, analyze current scientific publications relevant to the topic.

Exercises:

0 points (2 hours a week)

Syllabus:

Practical teaching follows the thematic units of theoretical teaching and is oriented towards a) practical introduction of students to advanced data analysis techniques in clinical and biochemical research, b) mastery of work skills in various software packages, c) application of advanced statistical modeling techniques, d) interpretation and critical evaluation of the obtained data processing results, e) complementary application of various techniques of advanced data processing in the identification of non-standard observations, recognition of patterns, grouping of objects and variables, selection of representative ones, etc. subsets of data, development of predictive models and their validation, identification of variables and factors of special influence.

Colloquia:

30 points

Written exam:

60 points