MEDFL5335 – Modern methods for analyzing survival and time to event data
Course description
Schedule, syllabus and examination date
Course content
The analysis of survival data and other types of time-to-event data are central in modern medical research and a number of other fields. A large number of methods for analysing time-to-event data have been developed, but many researchers have no knowledge of survival analysis, or they only know the most basic methods. The aim of the course is to give PhD-students and other researchers in biostatistics, bioinformatics, epidemiology, and related fields an up-to-date overview of statistical methodology for analysing time-to-event data.
The course starts with a broad introduction of the basic concepts and methods in survival and event history analysis, including methods for handling multiple states/outcome such as competing risks. Further topics of special relevance when analysing biobank data and data with high-dimensional covariates are discussed, and alternatives to Cox regression that are particularly useful for non-proportional hazards and time-dependent effects are considered. The effect of unobserved heterogeneity (frailty) in survival analysis is discussed, and methods for analysing recurrent events and clustered data are presented. The course concludes with a discussion of causality and methods for causal inference for survival data.
The course is given over five days and computer exercises will be an integrated part of the course. The plan of the five days is as follows:
Day1: Introduction to survival analysis; statistical methods for one and more samples (Kaplan-Meier and Nelson-Aalen estimators, log-rank type tests).
Day 2: Cox regression; competing risks and multistate models.
Day 3: Cox regression for nested case-control and case-cohort data; Cox regression for data with high-dimensional covariates (lasso, ridge, elastic net).
Day 4: Additive hazards regression; unobserved heterogeneity (frailty); frailty models for recurrent and clustered data.
Day 5: Mixed effects models for discrete time survival data; causality and causal inference for survival data.
Learning outcome
After having completed the course the students should:
- have an overview over the different study designs that are used for survival and time-to-event data and understand their benefits and limitations,
- have knowledge about the various data structures that occur in studies with survival and time-to-event data and their implications for statistical models and methods,
- know the difference between an individual hazard rate and the population hazard rate and understand the implications this has for interpreting empirical finding,
- be able to identify the appropriate method for a given problem with survival and time-to-event data and perform an analysis of the data using the R software,
be able to understand and critically assess analyses of survival and time-to-event data as they are typically reported in publications.
Admission
The course is restricted to students at the Medical Student Research Programme at the Faculty of Medicine and the Faculty of Dentistry, UiO.
Students apply in StudentWeb
The courses MEDFL5335 and IMB9335 have common admission
Prerequisites
Formal prerequisite knowledge
Passed exam in an introductory course in statistics (e.g. MF9130) and in a more advanced course in statistics, which includes multiple linear or logistic regression.
Recommended previous knowledge
The students should have a good understanding of the common statistical models, concepts and methods and experience with using statistics in medicine, biology or similar fields. No background in survival and event history analysis is needed, but familiarity with the basic concepts will be useful. Experience with the R software is recommended, but not required.
Overlapping courses
4 credits overlap with IMB9335 – Modern methods for analyzing survival and time to event data
Teaching
The course will be given as an intensive one-week long course (Monday to Friday) and consist of a mixture of lectures (about 60 %) and computer exercises (about 40 %). In the computer exercises the students will analyse given survival and time-to-event data using R, and the students should bring their own laptops with the last version of R and RStudio installed. Some R packages will also be needed, and the participants will be informed about this before the start of the course. The students will receive a reading list before the start of the course and are expected to prepare well. The students will do a project after the course and deliver a written report within a month (home exam).
You have to participate in at least 80 % of the teaching to be allowed to take the exam. Attendance at lectures will be registered.
Course textbook: Aalen, O.O, Borgan, ?. and Gjessing, H.K. (2008). Survival and Event History Analysis. A Process Point of View. Springer New York. A list of required and recommended reading, mostly from the course textbook, will be provided some weeks before the course.
Examination
The exam will be a home exam in the form of project work. The students should deliver their written project report within a month after the end of the course.
The exam paper will be provided in English and should be answered in English.
Use of sources and citation
You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.
Grading scale
Grades are awarded on a pass/fail scale. Read more about the grading system.
Explanations and appeals
Resit an examination
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
Evaluation
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.
This course is organized as part of the National Research School in Bioinformatics, Biostatistics and Systems Biology (NORBIS).