M7985 Survival analysis

Faculty of Science
Spring 2024
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. PaedDr. RNDr. Stanislav Katina, Ph.D. (lecturer)
RNDr. Bc. Iveta Selingerová, Ph.D. (seminar tutor)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Contact Person: doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 19. 2. to Sun 26. 5. Mon 10:00–11:50 M6,01011
  • Timetable of Seminar Groups:
M7985/01: Mon 19. 2. to Sun 26. 5. Tue 10:00–11:50 MP1,01014, I. Selingerová
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
The main goal of the course is to become familiar with some basic principles of statistical analysis of events in time to (1) understand and explain basic principles of nonparametric and (semi)parametric statistical inference and statistical modelling for (non)censored data; (2) implement these techniques in R language; (3) be able to apply them to real data.
Learning outcomes
Student will be able:
- to understand principles of likelihood and nonparametric statistical inference and (semi)parametric statistical models for (un)censored life-time data;
- to build up and explain suitable nonparametric statistical test and (semi)parametric for (un)censored life-time data;
- to apply nonparametric statistical inference and (semi)parametric statistical models on for (un)censored life-time data;
- to implement methods of nonparametric and (semi)parametric statistical inference for (un)censored life-time data to R.
Syllabus
  • censoring a its types,
  • survival function, variance, risk, mean and median survival, mean and median residual life, point estimation, confidence intervals and bands, competing risks, cumulative incidence function,
  • testing of statistical hypotheses – comparisons of two or more survival curves, relative risk, nonparametric principles for censored data,
  • generalisation of correlation coefficients in testing of hypotheses about survival curves,
  • Cox proportional hazard regresním model,
  • implementation in R,
  • examples from biology and medicine calculated in R language.
Literature
  • KLEIN, John P. and Melvin L. MOESCHBERGER. Survival analysis : techniques for censored and truncated data. 2nd ed. New York: Springer, 2003, xv, 536. ISBN 9781441929853. info
Teaching methods
Lectures, practicals. On-line using MS Teams or full-time according to the according to the development of the epidemiological situation and the applicable restrictions.
Assessment methods
Homework, oral exam. The conditions may be specified according to the development of the epidemiological situation and the applicable restrictions.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
Přednášky budou probíhat prezenčně dle rozvrhu. V IS bude vždy k dispozici záznam textu přednášky v PDF (přednášející text píše elektronickým perem na obrazovce tabletu a tento se zobrazuje na plátně) a slajdy v PDF s TeXovaným textem. Záznamy se budou sdílet až po dané přednášce a před další přednáškou.

K získání zápočtu je potřeba aktivní účast na cvičeních (povolené jsou 2 neomluvené absence). Za omluvenou absenci se považuje výhradně absence omluvená na studijním oddělení a zavedená do informačního systému v řádném termínu (do 5 pracovních dnů od termínu konání výuky). Je to v souladu se studijním řádem, kde se v čl.9 odstavci (7) píše, že (7) Student je povinen písemně omluvit na studijním oddělení fakulty svou neúčast do 5 pracovních dnů od termínu konání výuky, jež je omlouvána.

The course is also listed under the following terms Autumn 2010 - only for the accreditation, Autumn 2010, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Spring 2016, Spring 2017, spring 2018, Spring 2019, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Spring 2025.
  • Enrolment Statistics (Spring 2024, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2024/M7985