Event / Webinar

Bayesian Methods for Joint Longitudinal and Survival Data

Many clinical trials and other medical and reliability studies generate both longitudinal (repeated measurement) and survival (time to event) data.

On-Demand

1 hour

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Speakers

Brad Carlin

Senior Advisor, Data Science, PharmaLex

Maud Hennion

Senior 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.

Key learning objectives:

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