Bayesian Methods for Joint Longitudinal and Survival Data

Duration: 60 minutes


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Why watch

Summary

Many clinical trials and other medical and reliability studies generate both longitudinal (repeated measurement) and survival (time to event) data. Many well-established methods exist for analyzing such data separately, but these may be inappropriate when the longitudinal variable is correlated with patient health status, hence the survival endpoint (as well as the possibility of study dropout). In this webinar, we review recent approaches to joint modeling of longitudinal and survival endpoints, with an emphasis on Bayesian methods implemented via Markov chain Monte Carlo (MCMC) methods.  Despite the apparent complexity of these models, they are often routinely fit in standard software environments, such as SAS, BUGS, Stan, and related packages. 

Key Learnings

  •   Appreciation of the key statistical and regulatory issues in the joint modeling of longitudinal and survival data
  •   Guidance on how Bayes-MCMC methods can be understood and implemented in these settings
  •   Comparison of various recent approaches
speakers

A full hour of inspirational speaker(s)

Brad Carlin

Senior Advisor, Data Science, PharmaLex

Brad is a statistical researcher, methodologist, consultant, and instructor.  Prior to joining PharmaLex, he spent 27 years on the faculty of the Division of Biostatistics at the University of Minnesota School of Public Health, serving as division head for 7 of those years.  He has published more than 185 papers in refereed books and journals, and has co-authored three popular textbooks on Bayesian statistical methods and their applications in spatial statistics and adaptive clinical trials.  During his spare time, Brad is a health musician and bandleader, providing keyboards and vocals in a variety of venues.  He is excited and proud to be a part of the PharmaLex data science and statistics team. 

 

Maud Hennion

Senior Manager, Statistics - Manager, Pharmacometrics, PharmaLex

Maud Hennion holds a master´s degree in Mathematics and a master´s degree in Biostatistics (Université Catholique de Louvain). Maud has been working at PharmaLex as a bio-statistician for 5 years. He is primarily involved in pre-clinical and clinical studies (development of methodology of analysis, study simulation, study design, SAP and protocol writing and PK/PD analysis).