Bi8680 Advanced methods of applied survival analysis

Faculty of Science
Spring 2017
Extent and Intensity
2/0/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: graded credit.
Teacher(s)
doc. Mgr. Zdeněk Valenta, M.Sc., M. S., Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: doc. Mgr. Zdeněk Valenta, M.Sc., M. S., Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 20. 2. to Mon 22. 5. Wed 14:00–17:50 D29/347-RCX2
Prerequisites
- Course "Advanced Methods of Applied Survival Analysis" (PMAAP) informally extends the theme of the course Bi8678 "Applied Survival Analysis";
- Basic familiarity with survival analysis is assumed
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
- An alternative title for the course could be "Multivariate methods of applied survival analysis";
- At the end of the course the student will be familiar with basic attributes of multivariate methods of survival analysis, including the following:
(i) Multi-state models for the analysis of survival;
(ii) Models of competing risks;
(iii) Frailty models (i.e. "Mixed-effect models with random effects) for the analysis of survival
Learning outcomes
Upon finishing the course the student will:
- Be able to appreciate the notion of multivariate models for survival analysis;
- Be familiar with basic attributes of multivariate methods for survival analysis;
- Explain and practically apply multi-state models for survival analysis;
- Understand and be able to apply appropriate models in the context of of competing risks, understand the concept of cumulative incidence function, regression on cause-specific hazards and sub-distribution hazards;
- Understand the concept of frailty models (i.e. "mixed models with random effects) for the survival analysis and be able to use the models for the analysis of correlated survival data
Syllabus
  • Syllabus of the PMAAP course includes the following:
  • Parallel and longitudinal data structures
  • Basic types of multivariate survival data
  • - Basic parallel data;
  • - Recurrent events;
  • - Repeated measurements in a designed experiment;
  • - Following multiple different events over time;
  • - “Cause of death data”
  • Dependence structures
  • - Probability mechanisms
  • - Dependence time frame
  • Bivariate dependence measures
  • Probability aspects of multi-state models
  • Statistical inference for multi-state models
  • Shared frailty models
  • Shared frailty models for recurrent events
  • Multivariate frailty models
  • Competing risk models
  • Examples in R language. Applications using real data from biology and medicine.
Literature
  • PUTTER, H, M FIOCCO and RB GESKUS. Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine. CHICHESTER: JOHN WILEY & SONS LTD, 2007, vol. 26, No 11, p. 2389-2430. ISSN 0277-6715. Available from: https://dx.doi.org/10.1002/sim.2712. info
  • MARTINUSSEN, Torben and Thomas H. SCHEIKE. Dynamic regression models for survival data. New York: Springer, 2006, xiii, 470. ISBN 0387202749. info
  • THERNEAU, Terry M. and Patricia M. GRAMBSCH. Modeling survival data : extending the Cox model. 2nd print. New York: Springer-Verlag, 2001, xiii, 350. ISBN 0387987843. info
  • HOUGAARD, Philip. Analysis of multivariate survival data. New York: Springer Verlag, 2000. info
  • FINE, JP and RJ GRAY. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association. Alexandria: Amer Statistical Assoc, 1999, vol. 94, No 446, p. 496-509. ISSN 0162-1459. Available from: https://dx.doi.org/10.2307/2670170. info
Teaching methods
Lectures, discussions, team project
Assessment methods
Final written test (30 questions, each contributing 1 point, to pass student has to earn at least 25 points), group/team project, an oral exam in case of failing the final written test
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022.
  • Enrolment Statistics (Spring 2017, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2017/Bi8680