Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy.

ABSTRACT: An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individual

SUBMITTER: Maier C 

PROVIDER: S-EPMC7080550 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature