Comparison of some suboptimal control policies in medical drug therapy

In drug therapy, efficient dosage policies are needed to maintain drug concentrations at target. The relationship between the concentration of a drug and the dosages is often described by compartment models in which the parameters are unknown, although prior knowledge may be available and can be updated after blood samples are taken during the therapy. In this paper we define some tractable policies for adaptive control of drug concentrations in compartment models and compare their performances using computer simulation in a one-compartment model. We also discuss the effects of assuming normal priors, discrete approximation of a continuous prior, using nonquadratic costs, and information probing. From the simulation we derive intuition as to what types of policies perform well and address the topic of actively versus passively learning.