CourseSpecifications
Valid as from the academic year 2024-2025

Survival Analysis (C002950)

Course size
Credits 5.0(nominal values; actual values may depend on programme)
Study time 150 h

Course offerings and teaching methods in academic year 2024-2025
A (semester 2)
EnglishGent

Lecturers in academic year 2024-2025
Goetghebeur, Els

seminarlectureindependent work

WE02

Offered in the following programmes in 2024-2025
Master of Science in Teaching in Science and Technology(main subject Mathematics)
Master of Science in Mathematics
Master of Science in Statistical Data Analysis
Exchange Programme in Mathematics (master's level)

lecturer-in-charge
crdts
5555offering
AAAA

Teaching languages
EnglishKeywords
Competing risks, Hazards, Kaplan-Meier curve, Martingale residuals, Proportional hazards
model, Non-informative censoring, SAS, Survival analysis, Theory of risks, Time-varying
covariates
Position of the course
The course builds on methods and skills acquired in the courses of `Principles of statistical data
analysis’,`Analysis of Continuous Data’ and `Categorical Data Analysis’.
Students will understand the key concept of non-informative censoring and its implications forsurvival analysis. Students will learn how to design and analyze studies of times until theoccurrence of an event, such as death or the next cause-specific failure of a system, based on
non-informatively right-censored data.
Contents• Informative and non-informative censoring.
• Parametric models and maximum likelihood theory for estimating survival functions and• hazards based on non-informatively censored data.
• The Kaplan-Meier estimator of the survival function as a non-parametric maximum likelihood• estimator.
• The (weighted) logrank test for non-parametric comparisons of hazards between groups.
• Designing a two groups comparison with logrank test.
• Proportional hazards models for the combined effect of several prognostic factors on hazards• over time.
• Maximum partial likelihood as a heuristic estimation method.
• Martingale and Schoenfeld residuals as a diagnostic tool
• The extended proportional hazards model with time-varying covariates• Accelerated failure time models• Cause-specific hazards• Marginal models for repeated events• Data-analysis by means of the software packages SAS or R.
Initial competences
Students have successfully completed `Principles of statistical data analysis’, `Analysis of(Approved)

1

Continuous Data’ and `Categorical Data Analysis’ or acquired the competences reached there
in some other way.
Final competences
1 The student knows the definition and practical importance of non-informative censuring and
1 can evaluate its plausibility in a specific context.
2 He/she can draw Kaplan-Meier curves and perform (weighted) logrank tests to compare
1 survival curves between groups in a powerful manner.
3 The student can design a comparative study of survival times and calculate the required
1 sample size.
4 He/she will interpret the proportional (PH) hazards model correctly, can fit it to a given data
1 set, can build a model and draw justified conclusions, both in the formal and the practical
1 sense.
5 He/she can construct a prognostic score and make predictions with known error margins,
1 based on the PH model.
6 The student can check the fit of the model and propose necessary extensions or alternatives
1 if needed.
7 He she has been introduced to the analysis of repeated events and has acquired a basis
1 from which he/she can further explore the rich literature on this topic.
8 He she can appropriately report on the results of a survival analysis.
Conditions for credit contract
Access to this course unit via a credit contract is determined after successful competences assessment
Conditions for exam contract
This course unit cannot be taken via an exam contract
Teaching methods
Seminar, Lecture, Independent work
Study material
Type: Slides
Name: survival analysis
Indicative price: € 10Optional: noLanguage : EnglishAvailable on Ufora : Yes
Online Available : Yes
Available in the Library : No
Available through Student Association : No
Additional information: The slides are free of charge, the indicated cost is for printing the slides (if so desired)
References
D. Collett. `Modeling Survival Data in Medical Research’, 4th Edition, Chapman & Hall/CRC,
2023.
J. D. Kalbfleisch R. L. Prentice. `The Statistical Analysis of Failure Time Data', WileyInterscience; 2nd edition, 2002.
T. M. Therneau P. Grambsch. `Modeling Survival Data: Extending the Cox Model' (Statistics forBiology and Health), Springer Verlag, 2000.
P. D. Allison. `Survival Analysis Using the SAS System: A Practical Guide', 2nd edition, SASPublishing, 2010.
D. Kleinbaum, G. David, M. Klein. `Survival Analysis. A Self-Learning Text', Third Edition,Springer, 2012.
Course content-related study coachingStudents are coached during PC labs. Through the electronic learning environment they canexchange questions and answers outside lecture hours. A project will provide the students withpractical experience in data analysis, with feedback provided.
Assessment moments
end-of-term and continuous assessment
Examination methods in case of periodic assessment during the first examination period
Written assessment with open-ended questions
Examination methods in case of periodic assessment during the second examination period
Written assessment with open-ended questions(Approved)

2

Examination methods in case of permanent assessment
Oral assessment, Assignment
Possibilities of retake in case of permanent assessment
examination during the second examination period is possible
Extra information on the examination methods
The knowledge and problem solving skills of the students are tested.
Periodic evaluation: open book exam.
Permanent evaluation takes the form of a more involved data analysis project, which is alsopresented orally.
Failed students can retake the exam in a second session. This will then also involve a projectwith oral defense in addition to the written exam.Partial results for which the student scored atleast half of the points, can be transferred to the next examination period within the sameacademic year.
Partial results will never be rounded.
Calculation of the examination mark
Periodic evaluation (50%) and permanent evaluation (50%).
Students must pass both parts to pass the course.
Failed students can retake the exam in a second session.

(Approved)3