Name: Survival Analysis (Advanced Biostatistics) 2024
Start: 2024-09-23
End: 2024-09-27
Location: Leiden University Medical Center (LUMC)

OVERVIEW

Survival Analysis (Advanced Biostatistics) 2024 is organized by LUMC Boerhaave Continuing Medical Education (CME) and will be held from Sep 23 - 27, 2024 at Leiden University Medical Center (LUMC), Leiden, South Holland, Netherlands.

Description:
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). This type of data analysis is most prominently (but not only) used in the biomedical sciences. A special feature of survival data is that it takes time to observe the event of interest. As a result 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 data will be introduced and techniques for estimating the survival function by employing non-parametric methods will be illustrated. Multiplicative hazards regression models, testing and inference techniques will be studied in great details. Special aspects as time-dependent covariates effects, stratification, time and prediction will be introduced. Techniques to be used to assess the validity of the hazard regression model will be discussed. Alternative to Cox model will be illustrated and predictive models will be introduced. The last part of the course focus on more advanced models like competing risks and multi-states.

A competing risks model is concerned with failure time data where each subject may experience one of the K different type of terminal events. Multi-states are employed when some intermediate events may occur before the final event of interest and one is interested in the effects of the occurrence of those intermediate events on the final events. Also, for these more complex models, estimation and prediction techniques will be discussed. The course ends with a discussion about sample size calculations.