2025
Time-to-event analysis of preterm birth accounting for gestational age uncertainties
Zhang Y, Warren J, Hao H, Chang H. Time-to-event analysis of preterm birth accounting for gestational age uncertainties. The Annals Of Applied Statistics 2025, 19: 2155-2170. DOI: 10.1214/25-aoas2040.Peer-Reviewed Original ResearchTime-varying exposureTime-to-event analysisMCMC algorithmGestational agePreterm birthSimulation studyConvergence assessmentObstetric estimateThird trimesterHealth effect estimatesPreterm birth risk factorsLMP-based gestational ageOverall preterm birth rateBirth risk factorsRisk of preterm birthPopulation-based studyThird trimester of pregnancyJoint estimationAnalysis of preterm birthGestational age misclassificationPreterm birth rateDiscrete-time hazard modelsTrimester of pregnancyOutcome misclassificationSupplementary materialsA Bayesian Approach to the G‐Formula via Iterative Conditional Regression
Liu R, Hu L, Wilson F, Warren J, Li F. A Bayesian Approach to the G‐Formula via Iterative Conditional Regression. Statistics In Medicine 2025, 44: e70123. PMID: 40476299, PMCID: PMC12184534, DOI: 10.1002/sim.70123.Peer-Reviewed Original ResearchConceptsCausal effect estimationTime-varying covariatesModel misspecification biasBayesian approachReal world data examplesG-formulaAverage causal effect estimationTime-varying treatmentsBayesian additive regression treesAverage causal effectAdditive regression treesConditional expectationOutcome regressionConditional distributionJoint distributionData examplesPosterior distributionMisspecification biasParametric regressionSimulation studyEffect estimatesSampling algorithmAlgorithm formulaCausal effectsFlexible machine learning techniques
2024
A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design
Warren J, Wang Q, Ciarleglio M. A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design. Statistics In Medicine 2024, 43: 1615-1626. PMID: 38345148, PMCID: PMC11483151, DOI: 10.1002/sim.10032.Peer-Reviewed Original ResearchEstimation of parametersApproximate probability density functionKernel density estimationPosterior samplesSimulation studyDynamic borrowingDistribution elicitationDensity estimationParameter estimationProbability density functionPriorsBayesian analysisDensity functionFlexible alternativeEstimationDetect associationsDistribution
2022
A 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 Research
2020
A 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 Research
2019
Phylogeny-based tumor subclone identification using a Bayesian feature allocation model
Zeng L, Warren J, Zhao H. Phylogeny-based tumor subclone identification using a Bayesian feature allocation model. The Annals Of Applied Statistics 2019, 13: 1212-1241. DOI: 10.1214/18-aoas1223.Peer-Reviewed Original ResearchCopy number variationsCourse of evolutionSubgroup of cellsWhole-genome sequencing samplesTumor subclonesBayesian feature allocation modelPhylogenetic structureDifferent genetic alterationsPhylogeny structureSequencing depthFeature allocation modelNumber variationsSequencing samplesTree sizeDistinct genotypesGenetic alterationsSubclonesResult of competitionTumor progressionBayesian modelEstimation accuracySifADrug resistanceCellsSimulation study
2017
A 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
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