Clinical trials DL (3633)
  
Coordinating lecturer :Prof. dr. Marc BUYSE 
  
Co-lecturer :Prof. dr. Tomasz BURZYKOWSKI 

Language of instruction : English
Credits: 5,0
  
Period: quarter 3 (5sp)
  
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 DL (3220) 5.0 stptn
    Generalized Linear Models DL (5465) 6.0 stptn
    Longitudinal Data Analysis DL (3784) 6.0 stptn
    Medical and Molecular Biology DL (5466) 6.0 stptn
    Principles of Statistical Inference DL (3787) 3.0 stptn
    Survival Data Analysis DL (3632) 3.0 stptn
 

Prerequisites

The student should be familiar with statistical inference, statistical (generalized linear, mixed effects) models, basic methods of survival analysis.


Content

Topics: Phase I trial designs; Phase II trials designs; Phase III Trial objectives (measures of treatment effect; statistical inference: hypotheses, significance and power; testing for difference or benefit; testing for equivalence or non-inferiority); Treatments (controls (placebo, active control); experimental groups; randomization); Designs (multiple-comparison designs; dose-finding designs; factorial designs; cross-over designs); Patient selection (target population and eligibility criteria; analysis populations; subset analyses; prognostic and predictive factors); Endpoints (follow-up and patient assessments; safety endpoints; efficacy endpoints);Sample size (Type I and Type II errors; sample-size calculations for binary, normal and survival endpoints); Interim analyses (sequential and group-sequential designs; Data Monitoring Committees).


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


Evaluation

Quarter 3 (5,00sp)

Evaluation method
Written evaluation during teaching period5 %
Transfer of partial marks within the academic year✔
Other evaluation method during teaching period25 %
Other Homework projects with inidividual presentations
Transfer of partial marks within the academic year✔
Written exam70 %
Closed-book ✔
Multiple-choice questions✔
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 quizzes and 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
  Clinical Trials: A Methodologic Perspective, Steven Piantadosi, 2, Wiley, 9780471727811,,Available as e-book: https://ebookcentral-proquest-com.bib-proxy.uhasselt.be/lib/ubhasselt/de tail.action?docID=5098734&pq-origsite=summon

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

Cancer Clinical Trials: Methods and Practice, Marc E. Buyse; Maurice J. Staquet; Richard J. Sylvester, Oxford University Press, 9780192617651,

Group Sequential Methods with Applications to Clinical Trials, Christopher Jennison; Bruce W. Turnbull, Chapman and Hall/CRC, 9780849303166,

Fundamentals of Clinical Trials, Friedman, L.M.; Furberg, C.D.; DeMets, D.; Reboussin, D.M.; Granger, C.B., 5, Springer, 9783319185385,,Available as e-book: https://link-springer-com.bib-proxy.uhasselt.be/book/10.1007%2F978-3-319 -18539-2
 

Recommended course material
 

The use of R software is recommended in this course. 


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.

  •  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
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second year Master Biostatistics - distance learning N
second year Quantitative Epidemiology - distance learning J



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