Discovering Interactions Using Covariate Informed Random Partition Models

TL;DR

This paper introduces a flexible, covariate-informed random partition model to discover complex interactions between physiological variables and treatment responses, specifically applied to osteonecrosis severity in leukemia patients.

Contribution

It proposes a novel exploratory method that uses dependent random partition priors and machine learning to identify covariate-response associations without prior guidance.

Findings

Successfully identified physiological predictors influencing osteonecrosis severity.

Demonstrated utility through simulation studies.

Applied method to real clinical data with promising results.

Abstract

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.