Survival Analysis (online)

This online medical course will give an introduction to survival analysis and cover many of the types of survival data and analysis techniques regularly encountered in epidemiologic research. The necessary statistical theory will be presented, but the course will focus on practical examples, with an emphasis on matching data analysis to the research question at hand. Lab sessions will give students the opportunity to apply the theory to real datasets using the free statistical software R.

By the end of the course, you should be able to:

• recognize or describe the type of problem addressed by a survival analysis
• define and recognize censored data
• define and interpret a survivor function and a hazard function, and describe their relation
• recognize the computer printout from a Cox proportional hazards model, a stratified Cox model, and a Cox model extended for time-dependent covariates
• state the meaning of the proportional hazards assumption and know how to check this assumption
• recognize which survival analysis technique is appropriate for a given research question and dataset
• interpret the computer printout for survival models, including hazard ratios, hypothesis testing, and confidence intervals

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

5 weeks, 9 hours per week (online)

To successfully complete this course, you need to actively participate in the discussion forums and complete the learning unit assignments, including:

• Individual and group assignments
• A final assignment: this involves the completion of a daily quiz. The course is closed by the presentation of a case study by the student. The submission deadline and the resit deadline will be announced when they become available. You are allowed to redo the final assignment once.

Even though you can manage your own time our courses are not intended as individual education. We offer personalized online learning with lots of interaction with peer students, the E-moderator and lecturers. Flexibility from students, a positive attitude towards teachers and peers and the willingness to learn together and help each other is invaluable to our courses. To experience maximum interaction, we advise you to log on several times per week.

Note that the starting dates of courses, interim deadlines, and dates of exams are fixed, but you can choose when and where you want to watch web lectures and work on assignments. The e-moderator of the course will inform you about the beginning of the course and about deadlines during the course.

The average required study workload for the courses of MSc Epidemiology Postgraduate Online is 14 hours per week. You will need this time to study, to keep up with the assignments and course material.

To enroll in this course, you need:

• A BSc degree
• At least one course in basic statistical methods, up to and including simple and multiple linear regression, such as: Classical Methods in Data Analysis, Introduction to Biostatistics for Researchers, or their equivalent
• Basic programming experience in R, e.g. the ability to read in data and run a simple linear model

Please note that this course is part of an existing program within the Graduate School of Life Sciences. Tuitition fees may alter during the year.