Featured Publications
Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons
Clark-Boucher D, Zhou X, Du J, Liu Y, Needham B, Smith J, Mukherjee B. Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons. PLOS Genetics 2023, 19: e1011022. PMID: 37934796, PMCID: PMC10655967, DOI: 10.1371/journal.pgen.1011022.Peer-Reviewed Original ResearchConceptsBayesian Sparse Linear Mixed ModelMediation analysisHigh-dimensional mediation analysisMulti-ethnic cohortEpigenetic researchHealth outcomesHigh-dimensional DNA methylation dataLinear mixed modelsDNA methylation dataContinuous outcomesEvaluate DNA methylationDNA methylationMethylation dataDNAm dataMixed modelsDiverse simulationsSeamless implementationModern statistical methodsMediation effectR packageUnited StatesOutcomes
2022
Methods for large‐scale single mediator hypothesis testing: Possible choices and comparisons
Du J, Zhou X, Clark‐Boucher D, Hao W, Liu Y, Smith J, Mukherjee B. Methods for large‐scale single mediator hypothesis testing: Possible choices and comparisons. Genetic Epidemiology 2022, 47: 167-184. PMID: 36465006, PMCID: PMC10329872, DOI: 10.1002/gepi.22510.Peer-Reviewed Original ResearchConceptsNull hypothesisTest statisticsMediation hypothesis testingComposite null hypothesisHypothesis testingClasses of methodsFalse positive rateAlternative hypothesisSimulation studyHypothesis testing methodContinuous mediatorReference distributionSobel test statisticsContinuous outcomesExposure-mediator interactionMulti-Ethnic Study of AtherosclerosisDNA methylation sitesClassCRANMethylation sites
2021
A comparison of parametric propensity score‐based methods for causal inference with multiple treatments and a binary outcome
Yu Y, Zhang M, Shi X, Caram M, Little R, Mukherjee B. A comparison of parametric propensity score‐based methods for causal inference with multiple treatments and a binary outcome. Statistics In Medicine 2021, 40: 1653-1677. PMID: 33462862, DOI: 10.1002/sim.8862.Peer-Reviewed Original ResearchConceptsComparative effectiveness researchEstimation of causal effectsPropensity score-based methodsBinary outcomesInsurance networksCausal effectsPropensity score methodsPropensity-based methodsConfounding biasContinuous outcomesPharmacy claimsEffectiveness researchObservational studySimulation studyAdverse outcomesPropensity scoreEmergency room
2020
Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions
Zhang M, Yu Y, Wang S, Salvatore M, Fritsche L, He Z, Mukherjee B. Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions. Statistics In Medicine 2020, 39: 1675-1694. PMID: 32101638, DOI: 10.1002/sim.8505.Peer-Reviewed Original ResearchConceptsType I error rateType I error inflationIndependence assumptionWald and score testsCorrect type I error ratesSandwich variance estimatorSandwich estimatorScore testVariance estimationSimulation studyMisspecificationMichigan Genomics InitiativeStatistical practiceBinary outcomesTested interactionsEmpirical factsFlexible modelData modelTest of interactionBiobank studyInflationAssumptionsContinuous outcomesEpidemiological literatureLinear regression models