Featured Publications
A 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 ResearchConceptsFull 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 Dirichlet process mixture model for clustering longitudinal gene expression data
Sun J, Herazo‐Maya J, Kaminski N, Zhao H, Warren JL. A Dirichlet process mixture model for clustering longitudinal gene expression data. Statistics In Medicine 2017, 36: 3495-3506. PMID: 28620908, PMCID: PMC5583037, DOI: 10.1002/sim.7374.Peer-Reviewed Original ResearchConceptsLongitudinal gene expression profilesDirichlet process prior distributionRegression coefficientsExtensive simulation studyLongitudinal gene expression dataBayesian settingPrior distributionClustering methodFactor analysis modelDimensionality challengeStatistical methodsSimulation studyNovel clustering methodHigh dimensionality challengeSubgroup identificationImportant problemGene expression dataInteresting subgroupsClusteringCoefficientAnalysis modelModelExpression data
2016
A Statistical Model to Analyze Clinician Expert Consensus on Glaucoma Progression using Spatially Correlated Visual Field Data
Warren JL, Mwanza JC, Tanna AP, Budenz DL. A Statistical Model to Analyze Clinician Expert Consensus on Glaucoma Progression using Spatially Correlated Visual Field Data. Translational Vision Science & Technology 2016, 5: 14-14. PMID: 27622079, PMCID: PMC5017314, DOI: 10.1167/tvst.5.4.14.Peer-Reviewed Original ResearchStatistical modelSpatial probit regression modelsDeviance information criterionModel selection metricsBayesian settingSimulation study resultsModel parametersInformation criterionSpatial modelingCorrelated sensitivityNew methodologySingle frameworkSelection metricsField dataModelProbit regression modelInferenceEstimationVF locationsRegression modelsModelingPredictive abilityNumber of areas