M7985 Survival analysis

Faculty of Science
Spring 2020
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). 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
Wed 10:00–11:50 M3,01023
  • Timetable of Seminar Groups:
M7985/01: Mon 8:00–9:50 MP2,01014a, 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.
Assessment methods
Homework, oral exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
The lessons are usually in Czech or in English as needed, and the relevant terminology is always given with English equivalents.

The target skills of the study include the ability to use the English language passively and actively in their own expertise and also in potential areas of application of mathematics.

Assessment in all cases may be in Czech and English, at the student's choice.

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 2021, Spring 2022, Spring 2023, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2020/M7985