Survival Data Analysis DL (3632)
  
Coordinating lecturer :Prof. dr. Tomasz BURZYKOWSKI 

Language of instruction : English
Credits: 3,0
  
Period: semester 1 (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 DL (3220) 5.0 stptn
    Generalized Linear Models DL (5465) 6.0 stptn
 

Prerequisites

The student should be familiar with statistical inference and statistical (generalized linear, mixed effects) models.


Content

The course provides an introduction to the survival analysis.

Topics:

  • basics (censoring mechanisms, characteristics of the time-to-failure distribution, etc.);
  • basic time to failure distributions (exponential, Weibull);
  • Kaplan Meier estimator;
  • tests for comparing of survival curves (logrank, Gehan's, logrank test for trend, extensions);
  • proportional hazards model (estimation, diagnostics);
  • parameteric models;
  • marginal models for multivariate and correlated failure-time data;
  • competing risks.


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


Evaluation

Semester 1 (3,00sp)

Evaluation method
Written evaluation during teaching period25 %
Transfer of partial marks within the academic year✔
Homework✔
Other evaluation method during teaching period5 %
Other Quizzes
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
 

Leture notes and reading materials provided by the instructor
 

Recommended reading
  Modeling Survival Data in Medical Research,Collett D,2,Chapman and Hall/CRC,9781584883258,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/detail.action?docID=5345 205&pq-origsite=summon

Modeling Survival Data: Extending the Cox Model,Terry M. Therneau Patricia M. Grambsch,9781441931610
 

Recommended course material
 

R and SAS are recommended softwares for 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.

     
  •  DC 

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

     
  •  DC 

    • ... correctly using state-of-the-art software.

  •  EC 

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

  •  EC 

    • The student has the habit to assess data quality and integrity. 

  •  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.

     
  •  DC 

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

     
  •  DC 

    • The student is able to correctly use the theory methodologically, 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.

  •  EC 

    • The student is capable of acquiring new knowledge.

  •  EC 

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

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
second year Data Science - distance learning J
second year Master Bioinformatics - distance learning J
second year Master Biostatistics - distance learning J
second year Quantitative Epidemiology - distance learning N



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