2019
Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension
Liu M, Sun J, Herazo-Maya JD, Kaminski N, Zhao H. Joint Models for Time-to-Event Data and Longitudinal Biomarkers of High Dimension. Statistics In Biosciences 2019, 11: 614-629. PMID: 33281995, PMCID: PMC7717673, DOI: 10.1007/s12561-019-09256-0.Peer-Reviewed Original Research
2018
Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome
Sun J, Herazo-Maya J, Molyneaux PL, Maher TM, Kaminski N, Zhao H. Regularized Latent Class Model for Joint Analysis of High-Dimensional Longitudinal Biomarkers and a Time-to-Event Outcome. Biometrics 2018, 75: 69-77. PMID: 30178494, DOI: 10.1111/biom.12964.Peer-Reviewed Original ResearchConceptsJoint latent class modelLongitudinal biomarkersExtensive simulation studyLatent class modelLongitudinal submodelJoint modeling methodSurvival submodelLikelihood approachSimulation studyClass modelEvent outcomesLatent classesModeling methodMembership modelRandom effectsModeling approachClassSubmodelsJoint analysisModelBootstrapUnique trajectoriesNovel biological insightsInference
2017
A Dirichlet process mixture model for clustering longitudinal gene expression data
Sun J, Herazo‐Maya J, Kaminski N, Zhao H, Warren JL. A Dirichlet process mixture model for clustering longitudinal gene expression data. Statistics In Medicine 2017, 36: 3495-3506. PMID: 28620908, PMCID: PMC5583037, DOI: 10.1002/sim.7374.Peer-Reviewed Original ResearchConceptsLongitudinal gene expression profilesDirichlet process prior distributionRegression coefficientsExtensive simulation studyLongitudinal gene expression dataBayesian settingPrior distributionClustering methodFactor analysis modelDimensionality challengeStatistical methodsSimulation studyNovel clustering methodHigh dimensionality challengeSubgroup identificationImportant problemGene expression dataInteresting subgroupsClusteringCoefficientAnalysis modelModelExpression data