{"status":"OK","data":{"id":55607,"identifier":"DVN/IHUVAS","persistentUrl":"https://doi.org/10.7910/DVN/IHUVAS","protocol":"doi","authority":"10.7910","separator":"/","publisher":"Harvard Dataverse","publicationDate":"2010-10-12","storageIdentifier":"s3://1902.1/15146","effectiveDatasetFileCountLimit":1000,"datasetFileUploadsAvailable":999,"datasetType":"dataset","locks":[],"latestVersion":{"id":54348,"datasetId":55607,"datasetPersistentId":"doi:10.7910/DVN/IHUVAS","datasetType":"dataset","storageIdentifier":"s3://1902.1/15146","versionNumber":2,"internalVersionNumber":1,"versionMinorNumber":0,"versionState":"RELEASED","latestVersionPublishingState":"RELEASED","deaccessionNote":"","deaccessionLink":"","productionDate":"2008","lastUpdateTime":"2010-10-13T16:23:07Z","releaseTime":"2010-10-13T00:00:00Z","createTime":"2010-10-13T16:23:07Z","alternativePersistentId":"hdl:1902.1/15146","publicationDate":"2010-10-12","citationDate":"2010-10-12","effectiveDatasetFileCountLimit":1000,"datasetFileUploadsAvailable":999,"license":{"name":"CC0 1.0","uri":"http://creativecommons.org/publicdomain/zero/1.0","iconUri":"https://licensebuttons.net/p/zero/1.0/88x31.png","rightsIdentifier":"CC0-1.0","rightsIdentifierScheme":"SPDX","schemeUri":"https://spdx.org/licenses/","languageCode":"en"},"fileAccessRequest":false,"metadataBlocks":{"citation":{"displayName":"Citation Metadata","name":"citation","fields":[{"typeName":"title","multiple":false,"typeClass":"primitive","value":"Estimation and Testing for the Effect of a Genetic Pathway on a Disease Outcome Using Logistic Kernel Machine Regression via Logistic Mixed Models"},{"typeName":"author","multiple":true,"typeClass":"compound","value":[{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Dawei Liu"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Brown University"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Debashis Ghosh"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Pennsylvania State University"}},{"authorName":{"typeName":"authorName","multiple":false,"typeClass":"primitive","value":"Xihong Lin"},"authorAffiliation":{"typeName":"authorAffiliation","multiple":false,"typeClass":"primitive","value":"Harvard School of Public Health"}}]},{"typeName":"datasetContact","multiple":true,"typeClass":"compound","value":[{"datasetContactEmail":{"typeName":"datasetContactEmail","multiple":false,"typeClass":"primitive","value":"N/A"}}]},{"typeName":"dsDescription","multiple":true,"typeClass":"compound","value":[{"dsDescriptionValue":{"typeName":"dsDescriptionValue","multiple":false,"typeClass":"primitive","value":"BACKGROUND:  Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome.  The fact that genese within a pathway tend to interact with each other and relate to the outcome in a complicated way makes nonparametric methods more desirable.  The kernel machine method provides a convenient, powerful and unified method for multi-dimensional parametric and nonparametric modeling of the pathway effect.    RESULTS:  In this paper we propose a logistic kernel machine regression model for binary outcomes.  This model relates the disease risk to covariates parametrically and to genes within a genetic pathway parametrically or nonparametrically using kernel machines.  The nonparametric genetic pathway effect allows for possible interactions among the genes within the same pathway and a complicated relationship of the genetic pathway and the outcome.  We show that kernel machine estimation of the model components can be formulated using a logistic mixed model.  Estimation hence can proceed within a mixed model framework using standard statistical software.  A score test based on a Gaussian process approximation is developed to test for the genetic pathway effect.  The methods are illustrated using a prostate cancer data set and evaluated using simulations.  An extension to continuous and discrete outcomes using generalized kernel machine models and its connection with generalized linear mixed models is discussed."}}]},{"typeName":"topicClassification","multiple":true,"typeClass":"compound","value":[{"topicClassValue":{"typeName":"topicClassValue","multiple":false,"typeClass":"primitive","value":"Statistical Genetics, Genomics, and Omics"}}]},{"typeName":"productionDate","multiple":false,"typeClass":"primitive","value":"2008"},{"typeName":"dateOfDeposit","multiple":false,"typeClass":"primitive","value":"2010-10-12"}]}},"files":[{"description":"","label":"logistic_km.pdf","restricted":false,"version":1,"datasetVersionId":54348,"dataFile":{"id":1581516,"persistentId":"doi:10.7910/DVN/IHUVAS/T1CKXS","pidURL":"https://doi.org/10.7910/DVN/IHUVAS/T1CKXS","filename":"logistic_km.pdf","contentType":"application/pdf","friendlyType":"Adobe PDF","filesize":224421,"description":"","storageIdentifier":"s3://dvn-cloud:61673","rootDataFileId":-1,"md5":"a143adfb4bf1b59aebb0d4203f8ecd80","checksum":{"type":"MD5","value":"a143adfb4bf1b59aebb0d4203f8ecd80"},"tabularData":false,"creationDate":"2010-10-12","publicationDate":"2010-10-11","lastUpdateTime":"2010-10-13T16:23:07Z","fileAccessRequest":false}}]}}}