Denise Esserman, PhD
Professor of BiostatisticsCards
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About
Titles
Professor of Biostatistics
Biography
Denise Esserman joined the Yale School of Public Health faculty in 2014 as an associate professor of biostatistics. She is interested in methods related to clustered randomized trials and the impact of the ICC and other factors on calculating sample size. As a member of the Yale Center for Analytical Sciences, she collaborates with a number of departments at the Yale School of Medicine, including the Clinical and Translational Science Award Program, Patient-Centered Outcomes Research Institute, and the Cancer Center. She has reviewed articles for the American Journal of Epidemiology, Arteriosclerosis, Thrombosis and Vascular Biology; Statistics in Biopharmaceutical Research; Clinical Trials; and Obesity. Esserman earned her doctorate at Columbia University and has since taught at the University of North Carolina, Chapel Hill.
Appointments
Biostatistics
ProfessorPrimary
Other Departments & Organizations
Education & Training
- PhD
- Columbia University (2006)
- MS
- University of Georgia (2001)
Research
Overview
Medical Subject Headings (MeSH)
ORCID
0000-0003-1326-9618
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Michael Kane, PhD, MA, MS
Erich J Greene, PhD
Fan Li, PhD
Leah Ferrucci, PhD, MPH
Melinda Irwin, PhD, MPH
Ondrej Blaha, PhD
Longitudinal Studies
Publications
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 qualityAscertainmentA Bayesian platform trial design with hybrid control based on multisource exchangeability modelling
Wei W, Blaha O, Esserman D, Zelterman D, Kane M, Liu R, Lin J. A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling. Statistics In Medicine 2024, 43: 2439-2451. PMID: 38594809, PMCID: PMC11325877, DOI: 10.1002/sim.10077.Peer-Reviewed Original ResearchValidation 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 sizeAssociation between diet quality and ovarian cancer risk and survival
Cao A, Esserman D, Cartmel B, Irwin M, Ferrucci L. Association between diet quality and ovarian cancer risk and survival. Journal Of The National Cancer Institute 2024, 116: 1095-1104. PMID: 38400738, PMCID: PMC11223874, DOI: 10.1093/jnci/djae040.Peer-Reviewed Original ResearchCitationsAltmetricConceptsOvarian cancer riskAll-cause mortalityAssociated with lower all-cause mortalityDiet qualityCancer riskOvarian cancer diagnosisAssociated with ovarian cancer riskPre-diagnosisProspective NIH-AARP DietHealthy Eating Index-2015Cancer diagnosisNIH-AARP DietStop Hypertension scoreCancer registry dataEpithelial ovarian cancerMediterranean diet scoreFood frequency questionnaireCox proportional hazards regressionOvarian cancerProportional hazards regressionHEI-2015Health StudyHypertension scoreDiet scoreFrequency questionnaireAssociation Between Rental Assistance Programs and Undiagnosed Diabetes Among U.S
Gonzalez-Lopez C, Fenelon A, Lipska K, Denary W, Schlesinger P, Esserman D, Keene D. Association Between Rental Assistance Programs and Undiagnosed Diabetes Among U.S. Journal Of General Internal Medicine 2024, 39: 1625-1631. PMID: 38351417, PMCID: PMC11255160, DOI: 10.1007/s11606-024-08673-2.Peer-Reviewed Original ResearchAltmetricConceptsOdds of undiagnosed diabetesRental assistance programsRental assistanceUndiagnosed diabetesAssistance programsHousing programsNational Health and Nutrition Examination SurveyHealth and Nutrition Examination SurveyRates of undiagnosed diabetesAssociated with lower oddsNutrition Examination SurveyAssociated with decreased oddsAssisted housingLogistic regression modelsHousing qualityHealthcare accessPoverty ratioExamination SurveyDiabetes screeningQuasi-experimental approachLower oddsHealth dataAssisted groupAssisted participantsEducation levelBayesian 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. PMID: 39072299, PMCID: PMC11271983, 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 preventionAlgorithmHierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials
Tong G, Tong J, Jiang Y, Esserman D, Harhay M, Warren J. Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials. Clinical Trials 2024, 21: 451-460. PMID: 38197388, PMCID: PMC11233424, DOI: 10.1177/17407745231222018.Peer-Reviewed Original Research
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 ResearchCitationsThe design and baseline characteristics for the HOPE Consortium Trial to reduce pain and opioid use in hemodialysis
Dember L, Hsu J, Bernardo L, Cavanaugh K, Charytan D, Crowley S, Cukor D, Doorenbos A, Edwards D, Esserman D, Fischer M, Jhamb M, Joffe S, Johansen K, Kalim S, Keefe F, Kimmel P, Krebs E, Kuzla N, Mehrotra R, Mishra P, Pellegrino B, Steel J, Unruh M, White D, Yabes J, Becker W, Consortium F. The design and baseline characteristics for the HOPE Consortium Trial to reduce pain and opioid use in hemodialysis. Contemporary Clinical Trials 2023, 136: 107409. PMID: 38086444, PMCID: PMC10922728, DOI: 10.1016/j.cct.2023.107409.Peer-Reviewed Original ResearchCitationsGroup sequential two‐stage preference designs
Liu R, Li F, Esserman D, Ryan M. Group sequential two‐stage preference designs. Statistics In Medicine 2023, 43: 315-341. PMID: 38010193, DOI: 10.1002/sim.9962.Peer-Reviewed Original ResearchAltmetric
Clinical Trials
Current Trials
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