2022
Simulating time-to-event data subject to competing risks and clustering: A review and synthesis
Meng C, Esserman D, Li F, Zhao Y, Blaha O, Lu W, Wang Y, Peduzzi P, Greene E. Simulating time-to-event data subject to competing risks and clustering: A review and synthesis. Statistical Methods In Medical Research 2022, 32: 305-333. PMID: 36412111, DOI: 10.1177/09622802221136067.Peer-Reviewed Original ResearchA comparison of analytical strategies for cluster randomized trials with survival outcomes in the presence of competing risks
Li F, Lu W, Wang Y, Pan Z, Greene EJ, Meng G, Meng C, Blaha O, Zhao Y, Peduzzi P, Esserman D. A comparison of analytical strategies for cluster randomized trials with survival outcomes in the presence of competing risks. Statistical Methods In Medical Research 2022, 31: 1224-1241. PMID: 35290139, PMCID: PMC10518064, DOI: 10.1177/09622802221085080.Peer-Reviewed Original Research
2021
Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
Zhao Y, Chang C, Hannum M, Lee J, Shen R. Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data. Scientific Reports 2021, 11: 5146. PMID: 33664338, PMCID: PMC7933297, DOI: 10.1038/s41598-021-84514-0.Peer-Reviewed Original ResearchMeSH KeywordsBayes TheoremCarcinogenesisCluster AnalysisComputational BiologyEpigenomicsGenomicsHumansModels, StatisticalNeoplasmsTranscriptomeConceptsMolecular dataJoint posterior distributionHigh-dimensional settingsVariational Bayes approachSingle-cell dataArt clustering methodsPosterior distributionMolecular profiling dataComputational efficiencyCanonical oncogenicTranscriptomic alterationsBiological discoveryModel inferenceBayes approachCell decompositionStemness phenotypeProfiling dataSingle cellsComputational methodsBulk tumorPathway alterationsNebulaClustering methodAnalysis settingsCell data