Data Analysis in Clinical Research in Collaboration with AstraZeneca 2400-ZEWW1025
The course introduces students to data analysis in clinical research, combining theoretical foundations with practical applications. Students will explore statistical methods, software tools, and the organizational context of clinical trials. The program emphasizes both methodological rigor and the professional realities of drug development.
Course Outline
1. Basics of R
2. Basics of Python
3. Descriptive statistics on sample clinical datasets + R basics
4. Descriptive statistics on sample clinical datasets + Python basics
5. Fundamentals of hypothesis testing
6. Selected statistical tests and analysis of variance (ANOVA)
7. Introduction to survival analysis
8. Introduction to clinical trials (overview of trials, role of the clinical statistical programmer, departments involved, documents such as CSR, protocol, SAP)
9. Data in clinical trials (raw data, CDISC standards: SDTM, AdaM)
10. Informed consent, randomization, blinding, double programming, TLFs
11. Statistical aspects of clinical trial design
• Types of trials: superiority, non-inferiority, bioequivalence
• Clinical endpoints: time-to-event, binary, continuous
• Sample size calculation: significance level, statistical power, Type I and II errors
• Additional analyses (early success, futility, multiple endpoints, multiple testing procedures)
12. Statistical methods for evaluating trial outcomes
• Parametric tests
• Survival analysis
• ANOVA
13. Supplementary topics
14. Additional topics
Student Workload Estimate
Exercises: 30h
Consultations: 1h
Preparation for exercises: 14h
Homework: 30h
Total: 75h (31 contact hours + 44 independent study hours)
Type of course
Prerequisites (description)
Course coordinators
Learning outcomes
A) Knowledge
Understands the basics of data analysis in clinical research and the structure of clinical trials. Knows the advantages and limitations of methods used in clinical data analysis. Understands fundamental techniques and tools for evaluating the effectiveness of new drugs or therapies.
B) Skills
Can use statistical and econometric software for clinical data analysis. Able to analyze clinical data with basic statistical and econometric tools. Can apply appropriate research methods to assess drug effectiveness. Can use functions and scripts prepared by other researchers and analysts. Can select analytical tools to solve problems in clinical research. Can perform computational and analytical operations to prepare clinical trial reports.
Interprets results and prepares analytical reports.
C) Social Competences
Is prepared for continuous learning and skill development. Is prepared for communication data effectively using tables and charts. Is prepared for independent knowledge expansion. Is prepared for collaboration with existing programs and develops tools usable by others in clinical research. Is prepared for evaluation the applicability of selected tools to specific problems. Is prepared for understand the limitations of IT techniques in complex clinical studies.
SU05, SU06, SK01, SK03, SU04, SU03, SU02, SU01, SW03, SW02, SW01, SW04, SW05, SK02, SK04
Assessment criteria
Assessment Methods
• 100% based on homework assignments.
Uzyskanie zaliczenia przedmiotu wymaga obecności na zajęciach (dopuszczalne są dwie nieobecności) i ich zaliczenia; wykonywaniu prac domowych; zaliczenie ćwiczeń wymaga uzyskania m.in. połowy punktów z prac domowych;
Grade scale:
• [0%-50%) – unsatisfactory
• [50%-60%) – satisfactory
• [60%-70%) – satisfactory +
• [70%-80%) – good
• [80%-90%) – good +
• [90%-100%] – very good.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: