Association testing for binary trees-A Markov branching process approach

dc.contributor.authorWu, Xiaoweien
dc.contributor.authorZhu, Hongxiaoen
dc.date.accessioned2022-07-14T13:09:09Zen
dc.date.available2022-07-14T13:09:09Zen
dc.date.issued2022-03-09en
dc.description.abstractWe propose a new approach to test associations between binary trees and covariates. In this approach, binary-tree structured data are treated as sample paths of binary fission Markov branching processes (bMBP). We propose a generalized linear regression model and developed inference procedures for association testing, including variable selection and estimation of covariate effects. Simulation studies show that these procedures are able to accurately identify covariates that are associated with the binary tree structure by impacting the rate parameter of the bMBP. The problem of association testing on binary trees is motivated by modeling hierarchical clustering dendrograms of pixel intensities in biomedical images. By using semi-synthetic data generated from a real brain-tumor image, our simulation studies show that the bMBP model is able to capture the characteristics of dendrogram trees in brain-tumor images. Our final analysis of the glioblastoma multiforme brain-tumor data from The Cancer Imaging Archive identified multiple clinical and genetic variables that are potentially associated with brain-tumor heterogeneity.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1002/sim.9370en
dc.identifier.eissn1097-0258en
dc.identifier.issn0277-6715en
dc.identifier.pmid35262202en
dc.identifier.urihttp://hdl.handle.net/10919/111240en
dc.language.isoenen
dc.publisherWileyen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectassociation testingen
dc.subjectbinary treeen
dc.subjectglioblastoma multiformeen
dc.subjectMarkov branching processen
dc.titleAssociation testing for binary trees-A Markov branching process approachen
dc.title.serialStatistics in Medicineen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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