Shuangge Steven Ma, PhD
Department Chair and Professor of BiostatisticsDownloadHi-Res Photo
Cards
Additional Titles
Affiliated Faculty, Yale Institute for Global Health
Director, Biostatistics and Bioinformatics Shared Resource
Contact Info
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Additional Titles
Affiliated Faculty, Yale Institute for Global Health
Director, Biostatistics and Bioinformatics Shared Resource
Contact Info
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
Additional Titles
Affiliated Faculty, Yale Institute for Global Health
Director, Biostatistics and Bioinformatics Shared Resource
Contact Info
Education
PhD
University of Wisconsin (2004)
University of Wisconsin (2004)
MS
University of California at Los Angeles (2000)
University of California at Los Angeles (2000)
About
Titles
Department Chair and Professor of Biostatistics
Affiliated Faculty, Yale Institute for Global Health; Director, Biostatistics and Bioinformatics Shared Resource
Biography
Dr. Ma received his Ph.D. degree in statistics at University of Wisconsin in 2004. Prior to arriving at Yale, Dr. Ma was a Senior Fellow in Collaborative Health Studies Coordinating Center (CHSCC) and Department of Biostatistics at University of Washington. He has been involved in developing novel statistical and bioinformatics methodologies for analysis of cancer (NHL, breast cancer, melanoma, lung cancer), mental disorders, and cardiovascular diseases. He has also been involved in health economics research, with special interest in health insurance in developing countries.
Appointments
Biostatistics
ChairDualBiostatistics
ProfessorPrimary
Other Departments & Organizations
- Biostatistics
- Cancer Prevention and Control
- Center for Biomedical Data Science
- Center for Infection and Immunity
- Computational Biology and Biomedical Informatics
- Ma Lab
- SPORE in Lung Cancer
- SPORE in Skin Cancer
- Yale Cancer Center
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale Institute for Global Health
- Yale School of Public Health
- Yale Ventures
- YSPH Global Health Concentration
Education & Training
- Postdoctoral Associate
- University of Washington (2006)
- PhD
- University of Wisconsin (2004)
- MS
- University of California at Los Angeles (2000)
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
ORCID
0000-0001-9001-4999
Research at a Glance
Yale Co-Authors
Frequent collaborators of Shuangge Steven Ma's published research.
Publications Timeline
A big-picture view of Shuangge Steven Ma's research output by year.
Research Interests
Research topics Shuangge Steven Ma is interested in exploring.
Tassos C. Kyriakides, PhD
Caroline Helen Johnson, PhD
Michael Wininger, PhD
Peter Peduzzi, PhD
Sajid A Khan, MD, FACS, FSSO
Xinyi Shen, MPH
41Publications
84Citations
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 ResearchConceptsA 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 ResearchMeSH Keywords and ConceptsConceptsGene 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 ResearchMeSH Keywords and ConceptsConceptsHierarchical 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 ResearchAltmetricIntegrative 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 ResearchCitationsIntegrative 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 ResearchConceptsVariable 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 ResearchCitationsConceptsAsymmetric 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 ResearchConceptsFixed- 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-basedIncorporating prior information in gene expression network-based cancer heterogeneity analysis
Li R, Xu S, Li Y, Tang Z, Feng D, Cai J, Ma S. Incorporating prior information in gene expression network-based cancer heterogeneity analysis. Biostatistics 2024, 26: kxae028. PMID: 39074174, DOI: 10.1093/biostatistics/kxae028.Peer-Reviewed Original Research
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
Activities
activity Health insurance in mainland China and Taiwan: utilization, impact, and policy interventions
01/01/2013 - PresentResearchDetailsChinaAbstract/SynopsisThe goal of this study is to provide an updated, comprehensive description of health insurance coverage and utilization and their impacts on health and economic outcomes. Special attention has been paid to the less-advantaged groups.
Honors
honor Fellow
05/01/2013International AwardASADetailsUnited Stateshonor Elected Member
06/01/2007International AwardISIDetailsUnited States
News
News
- May 06, 2025
Genetic Test Underused in Cancer Care
- 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
Get In Touch
Contacts
Locations
300 George Street
Academic Office
Ste 206
New Haven, CT 06511
Events
Jun 20252-3Multiple Days
Organization Only All Day