  In this work, we examine recently developed methods for Bayesian inference of
optimal dynamic treatment regimes (DTRs). DTRs are a set of treatment decision
rules aimed at tailoring patient care to patient-specific characteristics,
thereby falling within the realm of precision medicine. In this field,
researchers seek to tailor therapy with the intention of improving health
outcomes; therefore, they are most interested in identifying optimal DTRs.
Recent work has developed Bayesian methods for identifying optimal DTRs in a
family indexed by $\psi$ via Bayesian dynamic marginal structural models (MSMs)
(Rodriguez Duque et al., 2022a); we review the proposed estimation procedure
and illustrate its use via the new BayesDTR R package. Although methods in
(Rodriguez Duque et al., 2022a) can estimate optimal DTRs well, they may lead
to biased estimators when the model for the expected outcome if everyone in a
population were to follow a given treatment strategy, known as a value
function, is misspecified or when a grid search for the optimum is employed. We
describe recent work that uses a Gaussian process ($GP$) prior on the value
function as a means to robustly identify optimal DTRs (Rodriguez Duque et al.,
2022b). We demonstrate how a $GP$ approach may be implemented with the BayesDTR
package and contrast it with other value-search approaches to identifying
optimal DTRs. We use data from an HIV therapeutic trial in order to illustrate
a standard analysis with these methods, using both the original observed trial
data and an additional simulated component to showcase a longitudinal
(two-stage DTR) analysis.
