Survival analysis is the study of the distribution of life times, i.e. the times from an initiating event (birth, diagnosis, start of treatment) to some terminal event (relapse, death). Survival analysis is most prominently (but not only) used in the biomedical sciences. This course is offered at Erasmus University Rotterdam.
A special feature of survival data is that it takes time to observe the event of interest. A result of this seemingly innocent observation is that for a number of subjects the event is not observed, but instead it is known that it has not taken place yet. This phenomenon is called censoring and it requires special statistical methods.
During the course different types of censored and truncated data will be introduced and techniques for estimating the survival function by employing both parametric and non-parametric methods will be illustrated. Also techniques for testing equality of survival functions (the log-rank test and alternatives) are discussed. Finally regression models for survival analysis, based on the hazard function (most notably the Cox proportional hazards model), will be studied in great detail.
Special aspects such as time-dependent covariates and stratification will be introduced. Techniques to be used to assess the validity of the proportional hazards regression model will be discussed. The last part of the course touches on models for multivariate survival analysis, including competing risks and multi-state models and frailty models. Finally, aspects of the planning of clinical trials with lifetime data will be discussed.
You can apply until:
Always verify the dates on the programme website.
Monday to Friday (5 full days)
From 8:45 till 16:00
After competing this course the student should:
Clinicians who are involved in clinical research with time-to-event data or want to learn the concepts and techniques for the analysis of such data.
Attendance, Written exam
Prof. Hein Putter, PhD
This programme requires students to demonstrate proficiency in English.Take IELTS test
Knowledge of statistics. Knowledge of regression models is advised.
Each year, the Erasmus Summer Programme provides international students and professionals with an invaluable opportunity to spend three weeks in Holland, bringing themselves right up to date in the health sciences.
In just three weeks, the Erasmus Summer Programme provides 30 courses on state-of-the-art biomedical topics. Each course is designed to fully satisfy the needs of international students with a specific interest such as clinical medicine, general practice, public health, epidemiology, genetics or biostatistics.
A key part in the Erasmus Summer Programme is played by the Fellowships: three suites of courses that provide top-level instruction in clinical research training, public health research, and genetic epidemiology. Each Fellowship is a stimulating challenge for talented young scientists who seek leading careers in scientific research.
Between them, the Erasmus Summer Programme and the Fellowships make a vital contribution to life-long learning and the professional development of students and teachers. By attracting ever greater interest each year, they reveal the enormous international demand for a springboard to an MSc or PhD in quantitative medicine or public health research.
StudyPortals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.
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