Statistical analysis of survival data for biostatistical/statistical PhD students

Aim

The course will describe classical methods for survival data. The first 3 days will give an introduction to central concepts like censoring, truncation, competing risks and multistate models, the Kaplan-Meier estimator, the log-rank test, stratified analysis, Poisson regression, and parametric models. The last 3 days will focus on regression models for survival data, including Cox's regression model and alternative models like the additive intensity model. Goodness-of-fit for these models will be discussed. Finally we will describe regression methods for multivariate survival data. The course consists of lectures and computer sessions (using R) illustrating how the various models can be applied with focus on the practical implementation and interpretation of the methods. The course will be passed via satisfactorily responding to a takehome exam.

Location & Organization

Organizer

DGSB - Danish Graduate School in Biostatistics

Course Director

Thomas Scheike

Location / venue

University of Copenhagen (Faculty of Health Sciences)

Timing & Workload

Duration 6 days
ECTS points 9
Frequency Every third year
 

Examination yes

Criteria

Is the course taught in English? yes
Is documentation available? (book, syllabus)? yes
Is the course open for external researchers? yes

More Information

http://pubhealth.ku.dk/bs/undervisning/phdkurser/efteraar2007/