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Biostatistics and Medical Data Analysis

Public syllabus for 2025-2026

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Academic overview

Programme
BD
Period
Year 1, Semester 2
Credits
5
Weeks
14

Curriculum placement

Appears in study plans

BD (2025 - 2027) Year 1, Semester 2 Elective subject, group 2

Teaching team

Course coordinator
Ioan Drăgan
Seminar coordinators
Ioan Drăgan

Learning time distribution

Total
Curriculum Lecture Practice Total Weekly Lecture Practice
42 28 14 3 2 1
Exam hours
5
Individual Study Bibliography study Field study Homework Tutoring Others
78 20 16 35 7 0
Overall
125

Learning outcomes

Knowledge

  • Getting familiar with techniques and methods used in medical data analysis./Introducerea conceptelor și metodelor de bază utilizate în procesarea datelor medicale.
  • (6a03a0952355ae3a04d2f312) Understanding how platforms specific to the processing of large volumes of data work;

Skills

  • Knowledge objectives (OC): (1) to present basic study designs and concepts in medical data analysis; (2) to introduce the principles of statistical power and sample size calculation; (3) to introduce the basics of risk and survival analysis; (4) to introduce the basics of meta-analysis. Abilitation objectives (OAb): (1) to identify the techniques appropriate to a given problem; (2) to use software specific tools for data processing; (3) to appropriately interpret results from statistical analysis.Attitude objectives (OAt): (1) to argue the utility of biostatistical methods in solving applied medical research problems.Obiective de cunoaștere (OC): (1) prezentarea designului studiilor și a conceptelor folosite în analiza datelor medicale; (2) introducerea principiilor de putere și calculul dimensiunii unui eșantion; (3) introducerea notiunilor de analiza riscului și analiza supraviețuirii; (4) introducerea notiunilor de meta-analiză.Obiective de abilitate (OAb): (1) identificarea tehnicilor potrivite pentru rezolvarea unei probleme specifice; (2) folosirea pachetelor software specifice pentru procesarea datelor; (3) interpretarea rezultatelor obținute în urma analizei statistice. Obiective de atitudine (OAt): (1) abilitatea de a argumenta utilitatea metodelor biostatistice pentru rezolvarea problemelor provenite din cercetare medicală.
  • (6a03a0952355ae3a04d2f315) Using concepts from computer science, mathematics and statistics in defining models and designing data analysis strategies and interpreting results;

Responsibility

  • The ability to effectively manage the resources involved in carrying out a project.

Online platform

classroom

Course content

Content Methods Obs
Study designs in biomedical research: experimental, observational, clinical trials. /Tipurile de design in studiile din cercetarea biomedicală: experimental, observațional, trialuri clinice. Discourse, conversation, illustration by examples 2 hours/2 ore
Validity of epidemiologic research. Concepts of bias (of both selection and information) and confounding./Validitatea cercetării epidemiologice. Conceptele de eroare sistematică=bias (din punct de vedere al selecției și al informației) si de confundatori. Discourse, conversation, illustration by examples 2 hours/2 ore
Power and sample size estimation: concepts, estimation for categorical and continuous data./Estimarea puterii statistice și a dimensiunii necesare pentru eșantioane: conceptele de bază, estimarea pentru date de tip categorie și de tip numeric. Discourse, conversation, illustration by examples 6 hours/6 ore
Methods of inference for categorical data. The chi-square and Fisher-exact tests. Stratified analysis (Mantel-Haenszel test)./Metode de inferență statistică pentru date de tip categorie. Testele chi-pătrat și Fisher-exact. Analiza stratificată (testul Mantel-Haenszel). Discourse, conversation, illustration by examples 2 hours/2 ore
Measures of effect for categorical data: the risk ratio and odds ratio. Stratified categorical data./Măsurarea efectului pentru date de tip categorie: risk ratio și odds ratio. Analiza stratificată. Discourse, conversation, illustration by examples 2 hours/2 ore
Confounding and standardization in epidemiologic studies./Factorii confundatori și standardizarea în studiile epidemiologice. Discourse, conversation, illustration by examples 2 hours/2 ore
Measure of effect for person-time data. Introduction to survival analysis. The Kaplan-Meier estimator of survival curves. The log-rank test./Măsurarea efectului pentru date de tip persoană-timp. Curbe Kaplan-Meier în analiza supraviețuirii. Testul Log-rank. Discourse, conversation, illustration by examples 6 hours/6 ore
The Cox proportional hazards modelling./Modelare de tip Cox proportional hazards. Discourse, conversation, illustration by examples 2 hours/2 ore
Statistical methods of meta-analysis: introduction, methodology; fixed and random effects models; meta-analysis of continuous and binary data./Metode statistice de meta-analiză: introducere, metodologie generală; modele de tip fixed and random effects; analiza datelor de tip numeric și categorie. Discourse, conversation, illustration by examples 4 hours/4 ore

Course bibliography

Bibliography:Rosner B, (2010): Fundamentals of Biostatistics (7th Edition). Duxbury Resource Center (Thompson). ISBN-13: 9780534418205.Kleinbaum DG, Klein M (2005): Survival Analysis. A Self-Learning Text, New York (US): Springer.Borenstein M, Hedges LV, Higgins JPT, Rothstein HR (2009). Introduction to Meta-Analysis. Chichester (UK): John Wiley & Sons.

Seminar content

Content Methods Obs
Study designs in biomedical applied research: web resources; research reports and publication./Tipurile de design in studiile din cercetarea biomedicală aplicată: resurse web; rapoarte de cercetare și publicarea rezultatelor. Problem-based approach, dialogue, critical analysis. 2 hours/2 ore
Power and sample size estimation: examples of real-world problems; getting familiar with R packages./Estimarea puterii statistice și a dimensiunii necesare pentru eșantioane: exemple de probleme din viața reală; familiarizarea cu pachetele R. Problem-based approach, dialogue, critical analysis 3 hours/3 ore
Analysis of categorical data: measures of effect and statistical testing. R packages and examples./Analiza datelor de tip categorie: măsurarea efectului și testarea statistică. Pachete R și exemple concrete. Problem-based approach, dialogue, critical analysis 3 hours/3 ore
Survival analysis. Cox proportional hazards modelling. R packages and examples./Analiza supraviețuirii. Modelarea de tip Cox proportional hazards. Pachete R și exemple concrete. Problem-based approach, dialogue, critical analysis 4 hours/4 ore
Meta-analysis software and resources: Review Manager; Cochrane Collaboration, systematic reviews and PRISMA, R libraries (meta, metafor, metacont)./Aplicații și resurse pentru meta-analiză: Review Manager; Cochrane Collaboration, systematic reviews și PRISMA, biblioteci/pachete R (meta, metafor, metacont). Problem-based approach, dialogue, critical analysis. 2 hours/2 ore
Bibliography:Kleinbaum DG (2015): ActivEpi Web http://www.activepi.com/Kleinbaum DG, Sullivan KM, Barker ND (2013): ActivEpi Companion Textbook, New York (US): Springer.Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA): http://www.prisma-statement.org/Cochrane Collaboration: http://www.cochrane.org/Agresti A, Franklin C, Klingenberg B, Posner M (2018). Statistics. The Art and Science of Learning from Data (4th Edition). Harlow (England): Pearson Education Ltd.

Seminar bibliography

The content is in accordance with similar courses provided at other universities and it covers the basic aspects of biostatistics for data analysis in applied biomedical research.

Corroboration

(none)

AI tools guidance

Generative AI tools are allowed to be used for both courses and seminars.

Evaluation and delivery

Activity Criteria Methods Percentage
C
  • Summative assessment of understanding and knowledge of concepts/Evaluare sumativă a înțelegerii și cunoașterii conceptelor
  • Analysis of epidemiologic models. Written test./Analiza unor modele epidemiologice. Test scris.
  • 40.0%
S
  • Formative evaluation/Evaluare formativă
  • Project
  • 60.0%

Performance standards

Knowledge of basic biostatistical concepts Ability to identify the appropriate methods for solving real-world problems.Ability to appropriately interpret results from statistical analysis is required for all three assessment criteria.Cunoașterea conceptelor de bază în biostatistică.Abilitatea de a identifica metodele potrivite pentru soluționarea problemelor concrete.Abilitatea de a interpreta corect rezultatele unei analize statistice.Nota 5 este minimul la toate cele trei componente de evaluare.

Additional info

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