Mixed effect estimation in deep compartment models: Variational methods outperform first-order approximations.

ABSTRACT: This work focusses on extending the deep compartment model (DCM) framework to the estimation of mixed-effects. By introducing random effects, model predictions can be personalized based on drug measurements, enabling the testing of different treatment schedules on an individual basis. The performance of classical first-order (FO and FOCE) and machine learning based variational inference (VI) algorithms were compared in a simulation study. In VI, posterior distributions of the random variables are approximated using variational distributions whose parameters can be directly optimized. We found that variational approximations estimated using the path derivative gradient estimator version of VI were highly accurate. Models fit on the simulated data set using the FO and VI objective functions

SUBMITTER: Janssen A 

PROVIDER: S-EPMC11579087 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature