Survival Data Analysis DL 1 (3632)
Coordinating lecturer:Prof. dr. Steven ABRAMS 

Credits: 3,0
Study load hours: 81
Period: semester 2 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
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 survival analysis.

Topics:
- Basics of time-to-event data (censoring mechanisms, characteristics of time-to-event distributions, truncation, etc.);
- Basic paramatric time-to-event distributions (exponential, Weibull, Gompertz, lognormal, loglogistic, ...);
- Nonparametric estimators for the survival function under right censoring, i.e., Kaplan-Meier estimator;
- Nonparametric estimation of the (cumulative) hazard function (Nelson-Aalen estimator);
- Parametric estimators for the survival function and hazard;
- Estimation of survival quantities under other censoring mechanisms (interval censoring); 
- Tests for comparing survival curves and hazards (logrank, Gehan's, logrank test for trend, Fleming-Harrington tests);
- Basic principles of regression analysis for time-to-event data.


Compulsory course material
 

Leture notes and reading materials provided by the instructor
 

Recommended reading
 

Survival Analysis: Techniques for Censored and Truncated Data, Klein and Moeschberger, Second Edition.

Modeling Survival Data in Medical Research,Collett D,2,Chapman and Hall/CRC,9781584883258,Available as e-book: https://ebookcentral.proquest.com/lib/ubhasselt/deta il.action?docID=5345205&pq-origsite=summon
 

Mandatory software
 

R and SAS


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


Evaluation

Semester 2 (3,00sp)

Evaluation method
Written evaluation during teaching period25 %
Transfer of partial marks within the academic yearYes, with condition
Conditions transfer of partial marks within the academic yearThe student needs to pass this component of evaluation.
Homework✔
Other evaluation method during teaching period5 %
Other Quizzes
Transfer of partial marks within the academic yearYes, no resit exam
Written exam70 %
Closed-book ✔
Multiple-choice questions✔
Off campus online evaluation/exam✔
For the full evaluation/exam✔
Explanation (English)Yes
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


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
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 critically appraise methodology and challenge proposals for and reported results of data analysis.

  •  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 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.

     
  •  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 in an application context, thus contributing to scientific research within the field of application.

  •  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.

 
Included in these programmesTolerance3
1st year Master Statistics - Bioinformatics DL option biostatistics /epidemiology compulsory Y
1st year Master Statistics - Biostatistics DL basis compulsory Y
1st year Master Statistics - Data Science DL option biostatistics /epidemiology compulsory Y
1st year Master Statistics - Epidemiology DL basis compulsory Y
second year Master Biostatistics - distance learning Y
second year Quantitative Epidemiology - distance learning Y



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.