Shuangge Steven Ma, PhD
Department Chair and Professor of BiostatisticsCards
About
Research
Overview
Develop novel statistical methodologies for complex data;
Study epidemiology and pathogenesis of multiple cancers, including breast cancer, NHL, melanoma and lung cancer;
Conduct survey studies in mainland China and Taiwan, investigating health insurance utilization and impact;
Provide statistical support to multiple biomedical studies.
Medical Research Interests
Biostatistics; Economics; Neoplasms
Public Health Interests
Mental Health; Health Policy; Cardiovascular Diseases; Genetics, Genomics, Epigenetics; Bioinformatics; Biomarkers; Cancer
Publications
2025
Local Clustering for Functional Data
Chen Y, Zhang Q, Ma S. Local Clustering for Functional Data. Journal Of Computational And Graphical Statistics 2025, ahead-of-print: 1-16. DOI: 10.1080/10618600.2024.2431057.Peer-Reviewed Original ResearchA Selective Review of Network Analysis Methods for Gene Expression Data
Li R, Yi H, Ma S. A Selective Review of Network Analysis Methods for Gene Expression Data. Methods In Molecular Biology 2025, 2880: 293-307. PMID: 39900765, DOI: 10.1007/978-1-0716-4276-4_14.Peer-Reviewed Original ResearchConceptsGene Expression DataGene expression networksExpression DataDownstream analysisExpression networksGene expressionBiological processesGenesMolecular mechanismsBiological implicationsHigh-throughput profiling techniquesBiological findingsGlobal viewComplex interactionsProfiling techniquesRegulationHierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling
Li J, Zhang Q, Ma S, Fang K, Xu Y. Hierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling. Statistics In Medicine 2025, 44: e10330. PMID: 39865593, DOI: 10.1002/sim.10330.Peer-Reviewed Original ResearchConceptsHierarchical multi-label classificationMulti-label classificationGene-environment interaction analysisGene-environmentEfficient expectation-maximizationGene-environment interactionsSemi-supervised scenariosCancer Genome AtlasUnlabeled dataInteraction analysisExpectation-maximizationGenome AtlasSuperior performanceHierarchical responseDisease outcomeClassificationPenalized estimatorsPractice settingsDisease modelsBiomedical studiesAnalysis literatureE effectsBayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data
Im Y, Li R, Ma S. Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data. Statistics In Medicine 2025, 44: e10350. PMID: 39840672, PMCID: PMC11774474, DOI: 10.1002/sim.10350.Peer-Reviewed Original ResearchIntegrative rank-based regression for multi-source high-dimensional data with multi-type responses
Xu F, Ma S, Zhang Q. Integrative rank-based regression for multi-source high-dimensional data with multi-type responses. Journal Of Applied Statistics 2025, ahead-of-print: 1-20. DOI: 10.1080/02664763.2025.2452964.Peer-Reviewed Original Research
2024
Statistical Methods for Accommodating Immortal Time: A Selective Review and Comparison
Wang J, Peduzzi P, Wininger M, Ma S. Statistical Methods for Accommodating Immortal Time: A Selective Review and Comparison. 2024, 53-92. DOI: 10.1007/978-3-031-65937-9_3.Peer-Reviewed Original ResearchIntegrative factor-adjusted sparse generalized linear models
Xu F, Ma S, Zhang Q. Integrative factor-adjusted sparse generalized linear models. Journal Of Statistical Computation And Simulation 2024, 95: 764-780. DOI: 10.1080/00949655.2024.2439450.Peer-Reviewed Original ResearchVariable selection consistencyHigh-dimensional dataIncreased accessibility of dataSelection consistencyConsistency propertiesCorrelated covariatesGeneralized linear modelVariable selectionAnalysis of genetic dataAccessibility of dataIdiosyncratic componentsCompetitive performanceCovariatesGenetic dataLinear modelSample sizeImprove model performanceEstimationIntegrated analysisModel estimatesLatent factorsModel performancePractical useConsistencyThe 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 studiesHigh-Dimensional Gene–Environment Interaction Analysis
Wu M, Li Y, Ma S. High-Dimensional Gene–Environment Interaction Analysis. Annual Review Of Statistics And Its Application 2024 DOI: 10.1146/annurev-statistics-112723-034315.Peer-Reviewed Original ResearchFixed- and random-effects analysisG-E interaction analysisG-E interactionsVariable selectionFrequentist analysisGene-environmentRandom effects analysisGeneral frameworkStatistical propertiesProgression of complex diseasesDimension reductionHypothesis testingG-EComplex diseasesGenetic factorsInteraction analysisNonlinear effect analysisStatistical perspectiveDisease outcomeEnvironmental factorsPrediction-basedEstimation-basedCTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma
Li E, Cheung H, Ma S. CTHRC1+ fibroblasts and SPP1+ macrophages synergistically contribute to pro-tumorigenic tumor microenvironment in pancreatic ductal adenocarcinoma. Scientific Reports 2024, 14: 17412. PMID: 39075108, PMCID: PMC11286765, DOI: 10.1038/s41598-024-68109-z.Peer-Reviewed Original ResearchConceptsPancreatic ductal adenocarcinomaTumor-associated macrophagesTumor microenvironmentEpithelial mesenchymal transitionDuctal adenocarcinomaImmune-suppressive tumor microenvironmentPro-tumorigenic tumor microenvironmentPancreatic cancer casesHeterogeneous tumor microenvironmentCombination of single-cellCancer-associated myofibroblastsSurgical resectionMyeloid cellsCurrent therapiesCancer casesLethal cancersSurvival rateExtracellular matrixTreat cancerMesenchymal transitionTherapeutic targetAdenocarcinomaCellular populationsCancerIntercellular interactions
Clinical Trials
Current Trials
Molecular Markers of UV Exposure and Cancer Risk in Skin
HIC ID2000024848RoleSub InvestigatorPrimary Completion Date03/31/2024Recruiting Participants
Academic Achievements & Community Involvement
News
News
- January 07, 2025
Leadership Appointments Underscore Yale Biostatistics’ Global Strength in Research and Innovation
- October 24, 2024
New Analytics Center for Cardiovascular Medicine
- September 19, 2024
YSPH alumna applies biostatistician skills to improve drug outcomes
- June 25, 2024
Terika McCall recognized for inclusive digital health tools
Get In Touch
Contacts
Locations
300 George Street
Academic Office
Ste 206
New Haven, CT 06511
Events
Jun 20252-3Multiple Days
- Organization OnlyAll Day