Erich J Greene, PhD
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Research Scientist in Biostatistics
Biography
Erich Greene is a biostatistician and statistical programmer at the Yale Center for Analytical Sciences (YCAS), working primarily with clinical trial monitoring and analysis. Prior to joining YCAS, he spent more than a decade as the primary fMRI data analyst and programmer at the Yale Memory and Cognition Laboratory. He holds a master's in physics from Cornell, a doctorate in psychology from Princeton, and a fondness for the serial comma.
Appointments
Biostatistics
Research ScientistPrimary
Other Departments & Organizations
Education & Training
- PhD
- Princeton University, Psychology (2003)
- MA
- Princeton University, Psychology (1997)
- MS
- Cornell University, Physics (1995)
- AB
- Princeton University, Physics (1991)
Research
Overview
Medical Subject Headings (MeSH)
Biostatistics; Mathematical Computing; Models, Statistical; Statistics
ORCID
0000-0002-9473-830X
Research at a Glance
Yale Co-Authors
Frequent collaborators of Erich J Greene's published research.
Publications Timeline
A big-picture view of Erich J Greene's research output by year.
Research Interests
Research topics Erich J Greene is interested in exploring.
James Dziura, MPH, PhD
Can Meng, MS, MPH
David Ganz
Denise Esserman, PhD
Thomas M. Gill, MD
Harlan Krumholz, MD, SM
44Publications
1,973Citations
Models, Statistical
Publications
Featured Publications
Estimation of ascertainment bias and its effect on power in clinical trials with time‐to‐event outcomes
Greene EJ, Peduzzi P, Dziura J, Meng C, Miller ME, Travison TG, Esserman D. Estimation of ascertainment bias and its effect on power in clinical trials with time‐to‐event outcomes. Statistics In Medicine 2020, 40: 1306-1320. PMID: 33316841, PMCID: PMC9007163, DOI: 10.1002/sim.8842.Peer-Reviewed Original ResearchCitationsA SAS Macro for Covariate-Constrained Randomization of General Cluster-Randomized and Unstratified Designs.
Greene EJ. A SAS Macro for Covariate-Constrained Randomization of General Cluster-Randomized and Unstratified Designs. Journal Of Statistical Software 2017, 77 PMID: 28649186, PMCID: PMC5479642, DOI: 10.18637/jss.v077.c01.Peer-Reviewed Original ResearchCitationsSimulating 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsA Randomized Trial of a Multifactorial Strategy to Prevent Serious Fall Injuries
Bhasin S, Gill TM, Reuben DB, Latham NK, Ganz DA, Greene EJ, Dziura J, Basaria S, Gurwitz JH, Dykes PC, McMahon S, Storer TW, Gazarian P, Miller ME, Travison TG, Esserman D, Carnie MB, Goehring L, Fagan M, Greenspan SL, Alexander N, Wiggins J, Ko F, Siu AL, Volpi E, Wu AW, Rich J, Waring SC, Wallace RB, Casteel C, Resnick NM, Magaziner J, Charpentier P, Lu C, Araujo K, Rajeevan H, Meng C, Allore H, Brawley BF, Eder R, McGloin JM, Skokos EA, Duncan PW, Baker D, Boult C, Correa-de-Araujo R, Peduzzi P. A Randomized Trial of a Multifactorial Strategy to Prevent Serious Fall Injuries. New England Journal Of Medicine 2020, 383: 129-140. PMID: 32640131, PMCID: PMC7421468, DOI: 10.1056/nejmoa2002183.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSerious fall injuriesFall injuriesIntervention groupControl groupUsual careMultifactorial interventionRate of hospitalizationPrimary care practicesCluster-randomized trialCommunity-dwelling adultsFirst-event analysisYears of ageHealth care systemRate of fallElectronic health recordsBaseline characteristicsPrimary outcomeRandomized trialsMean ageEfficacy trialsIndividualized planCare practicesInjuryMultifactorial strategyEvent ratesEffect of a Multifactorial Fall Injury Prevention Intervention on Patient Well‐Being: The STRIDE Study
Gill TM, Bhasin S, Reuben DB, Latham NK, Araujo K, Ganz DA, Boult C, Wu AW, Magaziner J, Alexander N, Wallace RB, Miller ME, Travison TG, Greenspan SL, Gurwitz JH, Rich J, Volpi E, Waring SC, Manini TM, Min LC, Teresi J, Dykes PC, McMahon S, McGloin JM, Skokos EA, Charpentier P, Basaria S, Duncan PW, Storer TW, Gazarian P, Allore HG, Dziura J, Esserman D, Carnie MB, Hanson C, Ko F, Resnick NM, Wiggins J, Lu C, Meng C, Goehring L, Fagan M, Correa‐de‐Araujo R, Casteel C, Peduzzi P, Greene EJ. Effect of a Multifactorial Fall Injury Prevention Intervention on Patient Well‐Being: The STRIDE Study. Journal Of The American Geriatrics Society 2020, 69: 173-179. PMID: 33037632, PMCID: PMC8178516, DOI: 10.1111/jgs.16854.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSerious fall injuriesFall injuriesMultifactorial interventionPhysical functionOutcomes Measurement Information System (PROMIS) AnxietyLeast square mean changePragmatic cluster-randomized trialMean scoreCommunity-living personsIntervention group's mean scoreInjury prevention interventionsPrimary care practicesFalls Efficacy ScaleCluster-randomized trialUsual careElders StudyDisability InstrumentSTRIDE studyDepression ScaleMean changePrevention interventionsHigh riskCare practicesControl groupMeaningful improvements
2024
Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials – A case study
Esserman D, Greene E, Latham N, Kane M, Lu C, Peduzzi P, Gill T, Ganz D. Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials – A case study. Contemporary Clinical Trials 2024, 142: 107572. PMID: 38740298, DOI: 10.1016/j.cct.2024.107572.Peer-Reviewed Original ResearchConceptsElectronic health record dataElectronic health recordsOutcome ascertainmentDevelop Confidence in EldersElectronic health record platformsClinical sitesPrimary care practicesHealth record dataMulti-site trialMulti-site clinical trialCare practicesHealth recordsAssess readinessAcute clinical outcomesHealthcare systemRecord dataClinical trialsReduce injuriesData qualityData comprehensionChecklistStudy dataClinical trial sitesVariable data qualityAscertainmentValidation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data
Ganz D, Esserman D, Latham N, Kane M, Min L, Gill T, Reuben D, Peduzzi P, Greene E. Validation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data. The Journals Of Gerontology Series A 2024, 79: glae096. PMID: 38566617, PMCID: PMC11167485, DOI: 10.1093/gerona/glae096.Peer-Reviewed Original ResearchConceptsFee-for-serviceFall injuriesMedicare AdvantageMedicare dataTrial armsHealthcare systemDevelop Confidence in EldersArea under the receiver operating characteristic curveMedicare fee-for-serviceStratified resultsMedicareReduce injuriesMedical attentionObservational studyStrideReceiver operating characteristic curveCalendar monthMA dataInjuryData sourcesHealthcareArmReference standardTrialsWindow sizeThe dementia care study (D‐CARE): Recruitment strategies and demographic characteristics of participants in a pragmatic randomized trial of dementia care
Yang M, Samper‐Ternent R, Volpi E, Green A, Lichtenstein M, Araujo K, Borek P, Charpentier P, Dziura J, Gill T, Galloway R, Greene E, Lenoir K, Peduzzi P, Meng C, Reese J, Shelton A, Skokos E, Summapund J, Unger E, Reuben D, Williamson J, Stevens A. The dementia care study (D‐CARE): Recruitment strategies and demographic characteristics of participants in a pragmatic randomized trial of dementia care. Alzheimer's & Dementia 2024, 20: 2575-2588. PMID: 38358084, PMCID: PMC11032530, DOI: 10.1002/alz.13698.Peer-Reviewed Original ResearchCitationsAltmetricConceptsElectronic health recordsPragmatic clinical trialsRecruitment strategiesDementia careApproaches to dementia careDementia care studiesCommunity-dwelling dyadsDyads of personsDiagnosis of dementiaHealth system patientsDemographic characteristics of participantsPragmatic randomized trialCharacteristics of participantsPragmatic research studiesFamily caregiversHealth recordsHealth systemCare studiesPLWDSystem patientsDemographic characteristicsD-CAREDementiaCaregiversClinical trial sitesBayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event
Tian X, Ciarleglio M, Cai J, Greene E, Esserman D, Li F, Zhao Y. Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2024, 73: 598-620. DOI: 10.1093/jrsssc/qlae003.Peer-Reviewed Original ResearchConceptsSemi-parametric inferenceRecurrent eventsAccelerated failure time modelFailure time modelEfficient sampling algorithmFrailty distributionDirichlet processPosterior inferenceSampling algorithmTime modelTerminal eventSurvival processesComplex data structuresDirichletInferenceData structureFall injury preventionAlgorithm
2023
A compressed large language model embedding dataset of ICD 10 CM descriptions
Kane M, King C, Esserman D, Latham N, Greene E, Ganz D. A compressed large language model embedding dataset of ICD 10 CM descriptions. BMC Bioinformatics 2023, 24: 482. PMID: 38105180, PMCID: PMC10726612, DOI: 10.1186/s12859-023-05597-2.Peer-Reviewed Original ResearchCitations
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Public ServiceNew Haven Theater CompanyDetails2011 - 2022DescriptionCo-founder, board member, and company member of Theater New Haven Inc. (d.b.a. New Haven Theater Company), a nonprofit whose mission is, “To celebrate the power of storytelling by providing New Haven with awesome theater experiences that are relevant and accessible to all members of our community.”
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