Survival Analysis (Advanced Biostatistics) 2023
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.
Participants are requested to use their own workspace / laptop for following this course. SPSS or R software is required, for the practical assignments. For external participants, not based at LUMC, a room will be made available.
This course is for beginners and does not require any pre-knowledge with survival data. Advanced survival models will be discussed the last day of the course.
The course puts a lot on emphasis on the interpretation of the analysis, on the well-known mistakes often occurring while working with survival data and provide inputs on how to report results in scientific publications.
It is not the aim of the course to discuss about data preparation, data cleaning for the statistical analysis.
SPSS and R codes to solve the afternoon exercise will be provided at the end of each day.
Basic knowledge of statistics (e.g. the Boerhaave course 'Basic methods and reasoning in Biostatistics') and of regression models (e.g. the Boerhaave course 'Regression Analysis')
Morning lectures, video’s, self-study assignments and daily plenary discussions with the teacher.
Proof of participation / exam / ECTS
In order to obtain a proof of participation, all lectures should be attended. A practical assignment/exam must be submitted at the end of the course. ECTS:1,5.
All study materials are supplied electronically only, and will be made available about 1 week prior to the course.
Master-, PhD students, postdocs and researchers in the bio-medical sciences.
- Prof. dr. Marta Fiocco, Mathematical Institute Leiden University and Biomedical Data Science Medical Statistical Section (email@example.com)
- Route description to the LUMC and parking