Course catalogue doctoral education - HT22

  • Application can be done between 2022-04-19 and 2022-05-16
Application closed
Title Biostatistics III: Survival analysis for epidemiologists
Course number 2992
Programme Epidemiology
Language English
Credits 1.5
Notes The course meets the requirements for a general science course.

Date 2021-11-08 -- 2021-11-17
Responsible KI department Department of Medical Epidemiology and Biostatistics
Specific entry requirements Epidemiology I, Introduction to epidemiology; Biostatistics I, Introduction for epidemiologists; Biostatistics II, Logistic regression for epidemiologists or equivalent courses.
Purpose of the course This course focuses on the application of survival analysis methods to epidemiological studies.
Intended learning outcomes After successfully completing this course students should be able to:
- propose a suitable statistical model for assessing a specific research hypothesis using data from a cohort study, fit the model using standard statistical software, evaluate the fit of the model, and interpret the results.
- explain the similarities and differences between Cox regression and Poisson regression.
- discuss the concept of timescales in statistical models for time-to-event data, be able to control for different timescales using standard statistical software, and argue for an appropriate timescale for a given research hypothesis.
-discuss the concept of confounding in epidemiological studies and be able to control/adjust for confounding using statistical models.
- apply and interpret appropriate statistical models for studying effect modification and be able to reparameterise a statistical model to estimate appropriate contrasts.
- critically evaluate the methodological aspects (design and analysis) of a scientific article reporting a cohort study.
Contents of the course This course introduces statistical methods for survival analysis with emphasis on the application of such methods to the analysis of epidemiological cohort studies. Topics covered include methods for estimating survival (life table and Kaplan-Meier methods), comparing survival between subgroups (log-rank test), and modelling survival (primarily Poisson regression and the Cox proportional hazards model). The course addresses the concept of 'time' as a potential confounder or effect modifier and approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification.

Teaching and learning activities Lectures, exercises focusing on analysis of real data using the free statistical software R, exercises not requiring statistical software, group discussions, literature review.
Compulsory elements The individual examination
Examination The course grade is based solely on a take-home examination. The focus of the exam will be on understanding concepts and their application to analysis of epidemiological studies rather than mathematical detail.
The course examination will be held within two weeks of the final day of the course. Students who do not obtain a passing grade in the first examination will be offered a second examination within 2 months of the final day of the course. Students who do not obtain a passing grade at the first two examinations will be given top priority for admission the next time the course is offered. If the course is not offered during the following two academic terms then a third examination will be scheduled within 12 months of the final day of the course.
Literature and other teaching material We have not assigned any compulsory texts since experience has shown that course participants have widely varying preferences. We will provide extensive course notes and many participants do not find a great need for additional texts. A large number of textbooks are available and we suggest students interested in additional reading to identify a textbook at a technical level suitable for them. Many general textbooks in medical statistics contain a chapter on survival analysis.
This course has a heavy emphasis on application rather than theory; although the course software is R users of other software packages may prefer a textbook specifically designed for users of that software. Very few books are targeted at epidemiologists (e.g., you won't find Poisson regression mentioned in many books). The definitive text for epidemiologists is Breslow and Day (1987) although it is rather advanced.

Suggested course literature:

Cleves M et al. An Introduction to Survival Analysis Using Stata, 3rd edition. College Station: Stata Press; 2010.

Breslow NE, Day NE. Statistical Methods in Cancer Research: The Design and Analysis of Cohort Studies. Lyon: IARC Scientific Publication; 1987. Free to download from

Number of students 8 - 25
Selection of students Eligible doctoral students will be prioritized according to 1) the relevance of the course syllabus for the applicant’s doctoral project (according to written information), 2) date for registration as a doctoral student (priority given to earlier registration date). To be considered, submit a completed application form. Give all information requested, including a short description of current research training and motivation for attending, as well as an account of previous courses taken.
More information The course will be held November 8, 10, 12, 15 and 17. The statistical software R will be used throughout the course. It is strongly recommended to have taken an introductory course in R or to have equivalent experience prior to taking this course. We have provided a self assessment test ( for you to confirm that you have understood the central concepts. We advise all potential applicants to take the test prior to applying to Biostatistics III. If you attempt the test under examination conditions (i.e., without referring to the answers) we would recommend: 1. if you score 70% or more then you possess the required prerequisite knowledge 2. if you score 40% to 70% you should revise the areas where you lost marks 3. if you score less than 40% you should, at minimum, undertake an extensive review of central concepts in statistical modelling and possibly consider studying intermediate level courses (e.g., Biostatistics II) before taking Biostatistics III.
Additional course leader
Latest course evaluation Course evaluation report
Course responsible Mark Clements
Department of Medical Epidemiology and Biostatistics
Contact person Gunilla Nilsson Roos
Institutionen för medicinsk epidemiologi och biostatistik
08-524 822 93