![]() |
Brad CarlinSenior Advisor, Data Science, PharmaLex |
![]() |
Maud HennionSenior Manager, Statistics - Manager, Pharmacometrics, PharmaLex |
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.