@article{05a2992395fc4b8d8b4d6a104bec2398,
title = "Discovering interactions using covariate informed random partition models",
abstract = "Combination chemotherapy treatment regimens created for patients diag-nosed 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 pri-ori 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 gen-eral way. The procedure connects covariates to responses flexibly through dependent random partition distributions and then employs machine learning techniques to highlight potential associations found in each cluster. We pro-vide 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.",
keywords = "Dependent random partition models, Exploratory data analysis, Multiplicative associations, Nonparametric Bayes",
author = "Page, {Garritt L.} and Quintana, {Fernando A.} and Rosner, {Gary L.}",
note = "Funding Information: Acknowledgments. We thank all the reviewers for comments and suggestions that helped us to greatly improve this manuscript. We thank Drs. Yuhui Chen and Timothy Han-son for kindly sharing code that carries out the P{\'o}lya Tree permutation test. Garritt L. Page was also affiliated with Basque Center of Applied Mathematics and was partially supported by the Basque Government through the BERC 2018-2021 program, by the Spanish Ministry of Science, Innovation and Universities through BCAM Severo Ochoa accreditation SEV-2017-0718. Fernando A. Quintana is also affiliated to ANID—Millennium Science Initiative Program—Millennium Nucleus Center for the Discovery of Structurs in Complex Data. This work was supported by grant FONDECYT 1180034 and by ANID—Millennium Science Initiative Program—NCN17_059. Gary L. Rosner was partially funded by grants GM092666 and P30CA006973. Funding Information: We thank all the reviewers for comments and suggestions that helped us to greatly improve this manuscript. We thank Drs. Yuhui Chen and Timothy Hanson for kindly sharing code that carries out the P{\'o}lya Tree permutation test. Garritt L. Page was also affiliated with Basque Center of Applied Mathematics and was partially supported by the Basque Government through the BERC 2018-2021 program, by the Spanish Ministry of Science, Innovation and Universities through BCAM Severo Ochoa accreditation SEV-2017-0718. Fernando A. Quintana is also affiliated to ANID—Millennium Science Initiative Program—Millennium Nucleus Center for the Discovery of Structurs in Complex Data. This work was supported by grant FONDECYT 1180034 and by ANID—Millennium Science Initiative Program—NCN17_059. Gary L. Rosner was partially funded by grants GM092666 and P30CA006973. Publisher Copyright: {\textcopyright} Institute of Mathematical Statistics, 2021.",
year = "2021",
doi = "10.1214/20-AOAS1372",
language = "English (US)",
volume = "15",
pages = "1--21",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "1",
}