41: Personalized Dosing Decisions Using a Bayesian Exposure-Hazard Multistate Model

Music City Center 

Multistate models provide a general framework for analyzing time-to-event data when there are multiple events of interest. We built a Bayesian competing-risk, multistate hazard model, focusing on an anticoagulant drug application where drug exposure decreases the risk of ischemic events while increasing the risk of bleeding events, both of which can ultimately lead to fatality. We present a computationally efficient strategy to estimate the steady-state exposure given only a single pair of pre- and post-dose concentration measurements using a pharmacokinetic (PK) submodel. This exposure estimate is then used in the hazard submodel as a predictor. Both submodels are estimated jointly using full Bayesian inference with Stan.
Using simulated data we evaluate the usefulness of the multistate model compared with simpler hazard models and the benefit of estimating the drug exposure using the PK model compared with using the assigned dose as a proxy for drug exposure. Finally, we demonstrate the use of patient preferences as utility scores for principled individual dose recommendations. The approach establishes a foundation for dynamic, personalized risk prediction.

Bayesian competing-risk

bleeding

multistate hazard model

oral anticoagulant

stroke 

Biopharmaceutical Section