Abstract
While Joint models become very common in different fields, such as medical research, literature on extending joint models outside a “classical” framework of a longitudinal biomarker and a survival time has exploded over the last decade, followed over the last years by an increased availability of softwares allowing to analyze more complex joint models.
We therefore offer in this course a broad overview over various available “innovative joint survival models” and in particular: considering joint models for recurrent events, joint models to validate surrogate endpoints, joint models for an excess of zero in the longitudinal part, joint models for a mixture of cured and uncured patients and joint models for an ordinal biomarker (with at least one survival endpoint). The objective of this course is to address with a practical perspective all these models with unified notations and level of explanation. The course will be illustrated with the analysis of several real-life datasets from medical research, considering existing R packages.
The level of the course is adapted to academic statisticians and applied statisticians in medical research (but will also be accessible to statisticians in other field of application); as well as graduate students in (bio)-statistics having already followed a course on classical survival analysis.
The two authors of this intensive course are specialist of the domain, with several international publications, books and a large experience of teaching.
Prerequisites
The course will provide a broad overview of various innovative joint survival models, with enough details to understand how to apply them and interpret the results, and also aim at good understanding of the estimation methods principle without entering too much in technical details.
Therefore, the level of the course is adapted to academic statisticians and applied statisticians in medical research (but will also be accessible to statisticians in other field of application); as well as graduate students in (bio)-statistics having already followed a course on classical survival analysis.
Participants should have (at least) some basic knowledge in the analysis of “classical” survival analysis, including in particular the concepts of (right-)censoring, the Kaplan-Meier estimator of the survival function, the logrank test and the proportional hazards model.
Of course, the participants are expected to have some basic knowledge of statistical data analysis and inference, and in particular, already a good background on standard maximum likelihood theory and standard regression models for a continuous. This comes of course with minimum prerequisites in mathematics (matrix algebra, concept of limit, derivatives and integrals, ...). We also assume basic familiarity with the use of R in particular with regards to data import, manipulation, and standard analysis techniques for continuous and time-to-event endpoint.
Learning Objectives
The objective of this course is to master modern statistical methods and to master how and when applying them on
real-world clinical data from different settings.
This course will provide to the audience a broad overview of different up-to-date innovative extensions of the classical
joint model and the context in which they are relevant. In particular, what to do when we have to face a non-gaussian
longitudinal biomarker, several biomarkers or when a part of the population will never experience the event of interest
(cure) or when we want to use a joint model to validate a surrogate markers.
At the end of the course, the participants should be able to
(i) recognize these situations and acknowledge the limitations of the classical approach in these situations,
(ii) understand the important features, the estimation principle and how to apply various innovative joint models and
make an informed choice about the different models/methods available,
(iii) identify and use an appropriate R package to perform the analyses,
(iv) correctly interpret the results of his/her analysis.
For a participant whose objective is to pursue with more methodological research on one of the topic covered by the
course, we think that this course will provide him/her with a good introduction to the topic and a good overview of the
available estimation/fitting techniques. References for more detailed methodological descriptions of the models
discussed will be provided.
Textbook
Books from the authors recommended, but not mandatory:
1. D. Commenges, H. Jacqmin-Gadda, A. Amadou, P. Joly, B. Liquet, C. Proust-Lima, V. Rondeau, and R. Thiébaut.
Dynamical Biostatistical Models, volume 86. CRC Press, 2015.
2. T. Emura, S. Matsui, and V. Rondeau. Survival Analysis with Correlated Endpoints : Joint Frailty-Copula Models.
JSS Research Series in Statistics, 2019.
3. C. Legrand. Advanced Survival Models. Published March 23, 2021 by Chapman and Hall/CRC. ISBN
9780367149673. 1st Edition - Catherine Legrand - Routledge (routledge.com)
The material of this course is largely based on the content of these books.
Laptop
Recommended but not necessary.
About the Instructors
Virginie Rondeau is the director of research in Biostatistics at the INSERM institute in Bordeaux (France) since 2015. Joint Models for recurrent events and competing risks (e.g. death) was her main research topic recently and the development of joint models for longitudinal markers and/or multiple times-to-event.
Catherine Legrand is Professor at the Institute of Statistics, Biostatistics and Actuarial Sciences (LIDAM/ISBA) of the Université catholique de Louvain (UCLouvain, Belgium). Her area of research includes survival data analysis with a particular focus on frailty models and cure models.