Cheng-Han Yang
Associate Research Scientist in BiostatisticsDownloadHi-Res Photo
About
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Titles
Associate Research Scientist in Biostatistics
Appointments
Biostatistics
Associate Research ScientistPrimary
Other Departments & Organizations
- All Institutions
- Biostatistics
- Yale School of Public Health
Education & Training
- PhD
- The University of Texas Health Science Center at Houston, Biostatistics (2025)
- MS
- National Tsing Hua University, Statistics (2017)
- BS
- National Tsing Hua University, Industrial Engineering and Engineering Management, (2015)
Research
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Overview
My research focuses on developing innovative statistical methodologies for both early-phase clinical trials and real-world data analysis, with applications in oncology, rare diseases, and precision medicine. In addition to advancing the design and analysis of early-phase trials, I develop principled methods for analyzing electronic health record (EHR) data, addressing challenges such as informative visit processes, informative missingness, and complex longitudinal structures. Ultimately, my goal is to improve patient health by enabling more accurate, ethical, and personalized clinical research.
Public Health Interests
Bayesian Statistics; Clinical Trials; Survival Analysis
ORCID
0000-0002-4161-3140- View Lab Website
Mukherjee Lab
Publications
2025
DEMO: Dose exploration, monitoring, and optimization using biological and clinical outcomes
Yang C, Thall P, Lin R. DEMO: Dose exploration, monitoring, and optimization using biological and clinical outcomes. The Annals Of Applied Statistics 2025, 19: 2599-2617. DOI: 10.1214/25-aoas2099.Peer-Reviewed Original ResearchConceptsClinical outcomesOptimal doseSurvival timeLong-term success rateLong-term therapeutic successMean survival timeTumor responseClinical responseInactive doseDose explorationCandidate dosesDose-findingTreatment-relatedImmunological effectsPharmacodynamic activityTherapeutic successPhase 1Restricted mean survival timeDoseSuccess rateEarly responseOutcomesBiological outcomesTrialsToxicityA Virtual Wellness Program to Enhance Well-being for Pediatric Oncology Staff during the COVID-19 Pandemic
Moody K, Swartz M, Gresham Z, Askins M, Cahalan L, Francis P, Geistkemper C, Heaton A, Lin R, Melo M, Rajan A, Smith M, Tewari P, Williams K, Yang C, Robert R. A Virtual Wellness Program to Enhance Well-being for Pediatric Oncology Staff during the COVID-19 Pandemic. Advances In Cancer Education And Quality Improvement 2025, 1: 10.52519/aceqi.25.1.1.a18. PMID: 41050054, PMCID: PMC12490267, DOI: 10.52519/aceqi.25.1.1.a18.Peer-Reviewed Original ResearchConceptsWellness programsWell-beingNon-clinical staff membersPediatric oncology staffCulture of wellnessDivision of PediatricImprove personal well-beingIncrease social connectionImprove well-beingChildren's Cancer HospitalCOVID-19-related distressSocial connectionsHealthcare staffHealthy behaviorsOncology staffSelf-CareDecrease distressPediatric leadershipPediatric staffProgram sessionsProfessional fulfillmentProgram benefitsCOVID-19 related distressRelated distressStaff members
2024
REDOMA: Bayesian random‐effects dose‐optimization meta‐analysis using spike‐and‐slab priors
Yang C, Kwiatkowski E, Lee J, Lin R. REDOMA: Bayesian random‐effects dose‐optimization meta‐analysis using spike‐and‐slab priors. Statistics In Medicine 2024, 43: 3484-3502. PMID: 38857904, PMCID: PMC11789924, DOI: 10.1002/sim.10107.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsOptimal biological doseDose-efficacy curveDose efficacyPhase I/II trialMeta-analysisOncology trialsSpike-and-slabMultiple phase IDose-toxicity relationshipPrecision cancer treatmentEarly-phase oncology trialsOncological therapyBiological doseEfficacy dataSpike-and-slab priorsTreatment efficacyCancer treatmentExtensive simulation studyTrialsEfficacyBayesian model selection frameworkTreatmentPhase IDoseGamma processA model-free variable screening method for optimal treatment regimes with high-dimensional survival data
Yang C, Cheng Y. A model-free variable screening method for optimal treatment regimes with high-dimensional survival data. Biometrika 2024, 111: 1369-1386. DOI: 10.1093/biomet/asae022.Peer-Reviewed Original ResearchCitationsConceptsHigh-dimensional survival dataOptimal treatment regimeVariable screening methodClassification problemOutcome-dependent samplingLevel of robustnessSurvival dataNonparametric learning methodKolmogorov-Smirnov approachCensoring distributionTheoretical propertiesModel misspecificationMisclassification error rateLogit lossHinge lossSimulation studyOptimal classifierBinary classificationSelection probabilityLearning methodsError rateRandom forestLung cancer datasetModel assumptionsCancer datasetsOn the relative conservativeness of Bayesian logistic regression method in oncology dose‐finding studies
Yang C, Cheng G, Lin R. On the relative conservativeness of Bayesian logistic regression method in oncology dose‐finding studies. Pharmaceutical Statistics 2024, 23: 585-594. PMID: 38317370, PMCID: PMC11789473, DOI: 10.1002/pst.2364.Peer-Reviewed Original ResearchCitationsMeSH Keywords and Concepts
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