Topics covered
The course material will be presented in a lecture format, changing between theory and illustrations. Ample attention will be devoted to the practical implementation of the methods covered in the course, by use of R. The lectures will be complemented by computer practicals in R.
Topics covered include:
- Introduction: Survival analysis, pitfalls and solutions
- Competing risks
- Time-dependent covariates in Cox regression models and landmarking
- Non-proportional hazards
- Pseudo-observations
- Relative survival
- Multi-state models
- Dynamic prediction
- Frailty models
- Missing data
- Informative censoring
- Joint models
Learning strategy
The material will be presented using slides and through class discussion. All slides, which will contain clear descriptions of the methodology, of applications, and of how to implement analyses in R, and exercise material will be made available. Papers for further study will be shared with the participants.
Pre‐requisites
This course is directed at statisticians, epidemiologists and clinicians with a good background in statistics and basic knowledge of mathematics. Participants are expected to have a fair knowledge of the standard techniques from survival analysis.
About the instructors
Liesbeth de Wreede is assistant professor at Leiden University Medical Center (Department of Biomedical Data Sciences). She mainly works in survival analysis, with a special interest in competing risks and multi-state models, relative survival and methods for missing data. She collaborates with several organizations in the field of haematology.
Hein Putter is full professor at Leiden University Medical Center (Department of Biomedical Data Sciences). His research interests include competing risks and multi-state models, frailty models and dynamic prediction. He is co-author of the book “Dynamic Prediction in Clinical Survival Analysis”, with Hans van Houwelingen.
Maja Pohar Perme is professor of biostatistics at the University of Ljubljana, where she received all her degrees. She is the head of the Department of Biostatistics and Medical Informatics at the Medical faculty. She is highly involved in the development of statistics in Slovenia, she is the head of the Applied Statistics programme at the University of Ljubljana. Her research focuses on survival analysis with particular interest in relative survival methodology and the use of pseudo-observations. She is the author of the relsurv package in R.
Pedro Miranda-Afonso is a biostatistician at Erasmus University Medical Center (Department of Epidemiology and Biostatistics). His research interests orbit modelling longitudinal biomarkers and their relationship with clinical events. He enjoys tackling methodological puzzles from real-world problems and implementing the resulting solutions in statistical software. He is a co-author of the R package JMbayes2, which implements extended joint models for longitudinal and time-to-event data, including dynamic prediction. He also provides statistical support to several clinical research groups and values multidisciplinary collaboration to translate statistical advances into better clinical understanding.
Nan van Geloven is assistant professor at Leiden University Medical Center (Department of Biomedical Data Sciences). Her research focuses on causal analysis of survival data. She works both on trials and on observational data. She has a special interest in defining estimands and in causal prediction, i.e., predicting outcomes under hypothetical interventions conditional on individual characteristics.
Edouard Bonneville is a biostatistician with research interests in competing risks, prediction models, and methods for dealing with missing data. He obtained his PhD at Leiden University Medical Center (Department of Biomedical Data Sciences), and works at LMU Munich (Institute for Medical Information Processing, Biometry, and Epidemiology) on metascientific aspects of methodological research.
Photo impression 2024
