Joshua Warren, PhD
Associate Professor of BiostatisticsCards
Contact Info
About
Titles
Associate Professor of Biostatistics
Biography
Joshua Warren is an associate professor in the Department of Biostatistics at the Yale School of Public Health. He received his Ph.D. in statistics from North Carolina State University in 2011. Dr. Warren’s research focuses on statistical methods in public health with an emphasis on environmental health problems. Much of his work involves introducing spatial and spatiotemporal models in the Bayesian setting to learn more about associations between environmental exposures, such as air pollution, and various health outcomes including preterm birth, low birth weight, and congenital anomalies. He also has interest in developing and applying spatiotemporal models in collaborative settings such as epidemiology, geography, nutrition, and glaucoma research. His theoretical and methodological interests include multiple topics in spatial/spatiotemporal modeling and Bayesian nonparameterics.
Appointments
Biostatistics
Associate Professor TenurePrimary
Other Departments & Organizations
Education & Training
- Postdoctoral Research Associate
- University of North Carolina at Chapel Hill (2014)
- PhD
- North Carolina State University (2011)
- MS
- North Carolina State University (2009)
Research
Overview
Medical Subject Headings (MeSH)
ORCID
0000-0002-6274-6970- View Lab Website
Warren Research Website
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Nicole Deziel, PhD, MHS
Ted Cohen, DPH, MD, MPH
Xiaomei Ma, PhD
Daniel Weinberger, PhD
Michelle L. Bell, PhD
Virginia Pitzer, ScD
Algorithms
Biostatistics
Publications
Featured Publications
Spatial Modeling of Mycobacterium Tuberculosis Transmission with Dyadic Genetic Relatedness Data
Warren J, Chitwood M, Sobkowiak B, Colijn C, Cohen T. Spatial Modeling of Mycobacterium Tuberculosis Transmission with Dyadic Genetic Relatedness Data. Biometrics 2023, 79: 3650-3663. PMID: 36745619, PMCID: PMC10404301, DOI: 10.1111/biom.13836.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsA Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth
Comess S, Chang HH, Warren JL. A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth. Biostatistics 2022, 25: 20-39. PMID: 35984351, PMCID: PMC10724312, DOI: 10.1093/biostatistics/kxac034.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsFull conditional distributionsEfficient model fittingStatistical modeling approachDensity estimation approachBayesian settingKernel density estimation approachPosterior outputBayesian frameworkConditional distributionModel fittingEstimation approachAccurate inferenceKDE approachModeling approachComparison metricsExposure uncertaintyUncertaintySecond stageApproachFittingInferencePredictionSimulationsModel comparison metricsFirst stageA Discrete Kernel Stick-Breaking Model for Detecting Spatial Boundaries in Hydraulic Fracturing Wastewater Disposal Well Placement Across Ohio
Warren J, Cai J, Johnson N, Deziel N. A Discrete Kernel Stick-Breaking Model for Detecting Spatial Boundaries in Hydraulic Fracturing Wastewater Disposal Well Placement Across Ohio. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2022, 71: 175-193. DOI: 10.1111/rssc.12527.Peer-Reviewed Original ResearchCitationsSPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION.
Warren JL, Miranda ML, Tootoo JL, Osgood CE, Bell ML. SPATIAL DISTRIBUTED LAG DATA FUSION FOR ESTIMATING AMBIENT AIR POLLUTION. The Annals Of Applied Statistics 2021, 15: 323-342. PMID: 34113416, PMCID: PMC8189329, DOI: 10.1214/20-aoas1399.Peer-Reviewed Original ResearchCitationsA Nonstationary Spatial Covariance Model for Processes Driven by Point Sources
Warren J. A Nonstationary Spatial Covariance Model for Processes Driven by Point Sources. Journal Of Agricultural, Biological And Environmental Statistics 2020, 25: 415-430. DOI: 10.1007/s13253-020-00404-4.Peer-Reviewed Original ResearchCitationsA Spatially Varying Distributed Lag Model with Application to an Air Pollution and Term Low Birth Weight Study
Warren JL, Luben TJ, Chang HH. A Spatially Varying Distributed Lag Model with Application to an Air Pollution and Term Low Birth Weight Study. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2020, 69: 681-696. PMID: 32595237, PMCID: PMC7319179, DOI: 10.1111/rssc.12407.Peer-Reviewed Original ResearchCitationsConceptsHealth effect parametersLow birth weight riskTerm low birth weightLow birth weightPregnancy outcomesWeight riskBirth weightResidual confoundingPregnancy periodNull associationBirth recordsAverage weekly concentrationsCritical windowDiminished abilityPregnancyExposure characteristicsRisk estimationAssociationExposureGeographic variabilityCritical window variable selection: estimating the impact of air pollution on very preterm birth
Warren JL, Kong W, Luben TJ, Chang HH. Critical window variable selection: estimating the impact of air pollution on very preterm birth. Biostatistics 2019, 21: 790-806. PMID: 30958877, PMCID: PMC7422642, DOI: 10.1093/biostatistics/kxz006.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsHierarchical Bayesian frameworkBayesian frameworkStatistical modelVariable selectionImproved estimationCritical windowPreterm birthRisk parametersVery preterm birthAdverse birth outcomesControl analysisExposure-disease relationshipsDifferent reproductive outcomesBirth outcomesPregnant womenReproductive outcomesCase/control analysisDiagnosing Glaucoma Progression With Visual Field Data Using a Spatiotemporal Boundary Detection Method
Berchuck SI, Mwanza JC, Warren JL. Diagnosing Glaucoma Progression With Visual Field Data Using a Spatiotemporal Boundary Detection Method. Journal Of The American Statistical Association 2019, 114: 1063-1074. PMID: 31662589, PMCID: PMC6818507, DOI: 10.1080/01621459.2018.1537911.Peer-Reviewed Original ResearchCitationsA spatially varying change points model for monitoring glaucoma progression using visual field data
Berchuck SI, Mwanza JC, Warren JL. A spatially varying change points model for monitoring glaucoma progression using visual field data. Spatial Statistics 2019, 30: 1-26. PMID: 30931247, PMCID: PMC6438211, DOI: 10.1016/j.spasta.2019.02.001.Peer-Reviewed Original ResearchCitationsSpatial-Temporal Modeling of the Association Between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure
Warren J, Fuentes M, Herring A, Langlois P. Spatial-Temporal Modeling of the Association Between Air Pollution Exposure and Preterm Birth: Identifying Critical Windows of Exposure. Biometrics 2012, 68: 1157-1167. PMID: 22568640, PMCID: PMC3422613, DOI: 10.1111/j.1541-0420.2012.01774.x.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and Concepts
Academic Achievements and Community Involvement
activity Statistics in Biosciences
Journal ServiceAssociate EditorDetails2015 - Presentactivity Journal of the American Statistical Association - Theory and Methods
Journal ServiceAssociate EditorDetails2018 - 2020activity Health Effects Institute
Peer Review Groups and Grant Study SectionsReviewerDetails2019 - 2019honor Coauthor, Kenneth Rothman Epidemiology Prize Paper
International AwardInternational Society for PharmacoepidemiologyDetails06/01/2018United Statesactivity Swiss National Science Foundation
Peer Review Groups and Grant Study SectionsReviewerDetails2017 - 2017
Links & Media
News
- July 03, 2024
Ambient heat during pregnancy linked to increased risk of childhood cancer
- January 10, 2024
Josh Warren: From clear, organized messaging comes a diversity of statistical usage
- March 06, 2023
Researchers Connect Air Pollution to Hospital Readmissions for Stroke
- January 10, 2023
Biostatistics Modelers Pioneering a New Direction for Public Health Investigations
Get In Touch
Contacts
Locations
350 George Street
Academic Office
Fl 3, Rm c302
New Haven, CT 06511
Appointments
203.785.4188