  Combination chemotherapy treatment regimens created for patients diagnosed
with childhood acute lymphoblastic leukemia have had great success in improving
cure rates. Unfortunately, patients prescribed these types of treatment
regimens have displayed susceptibility to the onset of osteonecrosis. Some have
suggested that this is due to pharmacokinetic interaction between two agents in
the treatment regimen (asparaginase and dexamethasone) and other physiological
variables. Determining which physiological variables to consider when searching
for interactions in scenarios like these, minus a priori guidance, has proved
to be a challenging problem, particularly if interactions influence the
response distribution in ways beyond shifts in expectation or dispersion only.
In this paper we propose an exploratory technique that is able to discover
associations between covariates and responses in a very general way. The
procedure connects covariates to responses very flexibly through dependent
random partition prior distributions, and then employs machine learning
techniques to highlight potential associations found in each cluster. We
provide a simulation study to show utility and apply the method to data
produced from a study dedicated to learning which physiological predictors
influence severity of osteonecrosis multiplicatively.
