  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 individualised chemotherapy. Sequential
Bayesian DA proved to be most computational efficient for handling
interoccasion variability and integrating TDM data. For new digital monitoring
devices enabling more frequent data collection, these features will be of
critical importance to improve patient care decisions in various therapeutic
areas.

Quantitative Methods;Medical Physics