Advanced Topics in Clinical Trials (2133)
  
Coordinating lecturer :Prof. dr. Tomasz BURZYKOWSKI 
  
Co-lecturer :Prof. dr. Marc BUYSE 

Language of instruction : English
Credits: 3,0
  
Period: quarter 3 (3sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Concepts of Probability and Statistics (1798) 5.0 stptn
    Introduction of Bayesian Inference (3562) 4.0 stptn
    Longitudinal Data Analysis (3765) 6.0 stptn
    Medical and Molecular Biology (3564) 6.0 stptn
    Principles of Statistical Inference (3768) 3.0 stptn
    Survival Data Analysis (0383) 3.0 stptn
 
   Advising sequentiality bound on the level of programme components
 
 
  Following programme components are advised to also be included in your study programme up till now.
    Clinical trials (0385) 5.0 stptn
 

Prerequisites

The student should be familiar with statistical inference, statistical (generalized linear, mixed effects) models, basic methods of survival analysis, basic concepts of Bayesian analysis. The student should be familiar with the fundamentals of clinical trial methodology (randomization, designs including group sequential designs, sample size calculation).


Content

The course introduces more advanced topics in design and analysis of clinical trials: Bayesian designs, causal inference, adaptive designs, meta-analysis.


Organisational and teaching methods
Organisational methods  
Collective feedback moment ✔  
Distance learning ✔  
Teaching methods  
Group work ✔  
Homework ✔  


Evaluation

Quarter 3 (3,00sp)

Evaluation method
Other evaluation method during teaching period30 %
Other:Group work with individual presentations
Transfer of partial marks within the academic year✔
Written exam70 %
Closed-book ✔
Multiple-choice questions✔
Evaluation conditions (participation and/or pass)✔
Conditions Group work is obligatory
Consequences Students get an X score if they do not meet the condition.
Additional information To get the final score, the weighted score is rounded mathematically, unless exam result is less than 50%, in which case the integer part is taken. The maximum final score is 20. To pass the course, the achieved final score has to be at least 10 (i.e., 50%). The homework scores are retained when computing the final score after the second chance exam.

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory course material
 

Photocopies of relevant book chapters and papers (available in an electronic form on BlackBoard).
 

Recommended reading
  Biostatistics in Clinical Trials,Carol K. Redmond; Theodore Colton,Wiley,9780471822110

Bayesian Approaches to Clinical Trials and Health-Care Evaluation,David J. Spiegelhalter; Keith R. Abrams; Jonathan P. Myles,Wiley,9780471499756,Available as e-book: https://onlinelibrary-wiley-com.bib-proxy.uhasselt.be/doi/book/10.1002/0 470092602

Bayesian Adaptive Methods for Clinical Trials,Scott M. Berry; Bradley P. Carlin; J. Jack Lee; Peter Muller,CRC Press,9781439825488,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/de tail.action?docID=601268&pq-origsite=summon

The Evaluation of Surrogate Endpoints,Burzykowski, Tomasz; Molenberghs, Geert; Buyse, Marc,1,Springer-Verlag New York,9780387202778,Available as e-book: https://link.springer.com/book/10.1007%2Fb138566
 

Recommended course material
 

The use of the R software is recommended. 


Learning outcomes
Master of Statistics and Data Science
  •  EC 

    • The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software.

     
  •  DC 

    • ... correctly using state-of-the-art analysis methodology.

     
  •  DC 

    • ... correctly using state-of-the-art design methodology.

  •  EC 

    • The student can work in a multidisciplinary, intercultural, and international team.

  •  EC 

    • The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application.

     
  •  DC 

    • The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.

  •  EC 

    • The student is an effective written and oral communicator, both within their own field as well as across disciplines.

     
  •  DC 

    • The student is an effective oral communicator in their own field.

     
  •  DC 

    • The student is an effective oral communicator, both within their own field as well as across disciplines.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

    • The student knows the ethical, moral, legal, policy making, and privacy context of statistics and data science, and always acts accordingly.

  •  EC 

    • The student knows the international nature of the field of statistical science and data science.

  •  EC 

    • The student knows the relevant stakeholders and understands the need for assertive and empathic interaction with them.

     
  •  DC 

    • The student can reflect on the role of the statistician and data scientist in the interaction with the stakeholders.

     
  •  DC 

    • The student can explain the consequences of his/her work for relevant stakeholders.

     
  •  DC 

    • The student can, when building an argumentation, consider different perspectives and interests.

     
  •  DC 

    • The student can identify relevant stakeholders and their interests, particularly within the programme specialization.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
2nd year Master Biostatistics J
2nd year Master Biostatistics - icp J
Exchange Programme Statistics J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.