  Optimal treatment regimes (OTR) are individualised treatment assignment
strategies that identify a medical treatment as optimal given all background
information available on the individual. We discuss Bayes optimal treatment
regimes estimated using a loss function defined on the bivariate distribution
of dichotomous potential outcomes. The proposed approach allows considering
more general objectives for the OTR than maximization of an expected outcome
(e.g., survival probability) by taking into account, for example, unnecessary
treatment burden. As a motivating example we consider the case of oropharynx
cancer treatment where unnecessary burden due to chemotherapy is to be avoided
while maximizing survival chances. Assuming ignorable treatment assignment we
describe Bayesian inference about the OTR including a sensitivity analysis on
the unobserved partial association of the potential outcomes. We evaluate the
methodology by simulations that apply Bayesian parametric and more flexible
non-parametric outcome models. The proposed OTR for oropharynx cancer reduces
the frequency of the more burdensome chemotherapy assignment by approximately
75% without reducing the average survival probability. This regime thus offers
a strong increase in expected quality of life of patients.
