2025
Subgroup Testing in the Change‐Plane Cox Model
Zhang X, Ren P, Shi X, Ma S, Liu X. Subgroup Testing in the Change‐Plane Cox Model. Statistics In Medicine 2025, 44: e70179. PMID: 40662752, DOI: 10.1002/sim.70179.Peer-Reviewed Original ResearchConceptsFinite sample performanceAnalysis of survival dataLikelihood ratio testAsymptotic distributionSample performanceLung cancer dataScore testSimulation studyRatio testSurvival dataCancer dataCox modelImmune checkpoint blockade therapyCheckpoint blockade therapySolid tumor patientsTumor mutational burdenSubgroup testsTreatment effectsCovariatesBlockade therapyMutational burdenSubgroupsJoint modeling of mixed outcomes using a rank-based sparse neural network
Xue J, Xu Y, Li J, Ma S, Fang K. Joint modeling of mixed outcomes using a rank-based sparse neural network. Journal Of Biomedical Informatics 2025, 169: 104870. PMID: 40623577, PMCID: PMC12306493, DOI: 10.1016/j.jbi.2025.104870.Peer-Reviewed Original ResearchSparse neural networksNeural networkCompetitive performanceImbalance issueLoss functionSparse layerLeverage informationPrediction accuracyTraditional methodsNetworkParametric frameworkPenalization methodFaces challengesJoint modelPrediction modelInformationSkin cutaneous melanomaHigh-throughput profilingHigh-dimensional covariatesDimensionalityGenomic researchFeaturesMethodSimulation studyBiomedical studiesSubgroup Analysis of Differential Networks with Latent Variables
Li L, Ma S, Zhang Q. Subgroup Analysis of Differential Networks with Latent Variables. Statistics And Computing 2025, 35: 140. DOI: 10.1007/s11222-025-10681-z.Peer-Reviewed Original ResearchLow-rank structureSubgroup networksBaseline networkCompetitive performanceDifferential networksReal-world observational dataLatent variablesEfficient computational algorithmNetworkSparsityHeterogeneity analysis methodComputational algorithmInfluence of latent variablesDense networkSubgroup structureStatistical propertiesAlgorithmNetwork analysisSimulation studyMethodAnalysis methodDifferential network analysis
2024
The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies
Liu Y, Ren J, Ma S, Wu C. The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies. Statistics In Medicine 2024, 43: 4928-4983. PMID: 39260448, PMCID: PMC11585335, DOI: 10.1002/sim.10196.Peer-Reviewed Original ResearchAsymmetric Laplace distributionSpike-and-slab LASSORobust variable selection methodHeavy-tailed errorsRobust variable selectionHeavy-tailed distributionsAnalysis of high-dimensional genomic dataHigh-dimensional genomic dataExpectation-maximizationComprehensive simulation studyVariable selection methodsLaplace distributionCoordinate descent frameworkPosterior modeCancer genomics studiesRobust likelihoodVariable selectionSparsity patternSimulation studyComputational advantagesQuantile regressionNonrobust oneSelf-adaptationLoss functionGenomic studies
2022
Heterogeneous Graphical Model for Non-Negative and Non-Gaussian PM2.5 data
Zhang J, Fan X, Li Y, Ma S. Heterogeneous Graphical Model for Non-Negative and Non-Gaussian PM2.5 data. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2022, 71: 1303-1329. DOI: 10.1111/rssc.12575.Peer-Reviewed Original Research
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