M7985 Survival analysis

Faculty of Science
Spring 2027
Extent and Intensity
2/2/0. 6 credit(s). Type of Completion: zk (examination).
In-person direct teaching
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
Prerequisites (in Czech)
M7986 Statistická inference I
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
Abstract
The course focuses on statistical methods for the analysis of events occurring over time. Upon completion of the course, students will be able to (1) understand and explain methods of nonparametric and (semi)parametric statistical inference, parametric probability distributions and (semi)parametric regression models for (non)censored data; (2) implement these methods in the R language; (3) apply them to real data from medicine, clinical research, epidemiology, actuarial and financial mathematics and other areas of applied data analysis.
Learning outcomes

Student will be able:

  • to understand principles of likelihood, methods of nonparametric and (semi)parametric statistical inference, parametric probability distributions and (semi)parametric regression models for (un)censored time-to-event data (e.g. death);
  • to design and explain suitable nonparametric and parametric statistical tests and (semi)parametric regression models for (un)censored time-to-event data;
  • to apply methods of nonparametric and (semi)parametric statistical inference, parametric probability distributions and (semi)parametric regression models to real (un)censored time-to-event data;
  • to fit nonparametric and (semi)parametric regression models to (un)censored time-to-event data;
  • to implement methods of nonparametric and (semi)parametric statistical inference and statistical modelling for (un)censored time-to-event data in R.
Key topics
  • Censoring and its types.  
  • Likelihood function for censored data.  
  • Survival function and its variance, hazard, cumulative hazard, mean and median survival, mean and median residual life, point estimation, confidence intervals and confidence bands.  
  • Competing risks and cumulative incidence function.  
  • Testing of statistical hypotheses – comparison of two or more survival curves, relative risk, nonparametric principles for censored and uncensored data.  
  • Generalisation of correlation coefficients in testing of hypotheses about survival curves.  
  • Parametric probability distributions for lifetime data.  
  • Parametric regression models and accelerated failure time models.  
  • Cox proportional hazards regression model.  
  • Fitting nonparametric and (semi)parametric regression models for lifetime data.  
  • Implementation of statistical inference and statistical modelling methods in R.  
  • Examples in R language.  
  • Applications to real data from biology, medicine and other fields.
Study resources and 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
Approaches, practices, and methods used in teaching

Lectures: 2 hours per week.  

Practicals: 2 hours per week.  

Teaching is conducted either online using MS Teams or in person according to the development of the epidemiological situation, applicable restrictions and the decision of the lecturer.

Method of verifying learning outcomes and course completion requirements
Homework (projects), 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)
The course is taught annually.
The course is taught every week.
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 2024, Spring 2025, Spring 2026.
  • Enrolment Statistics (Spring 2027, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2027/M7985