2023
An inverse probability weighted regression method that accounts for right‐censoring for causal inference with multiple treatments and a binary outcome
Yu Y, Zhang M, Mukherjee B. An inverse probability weighted regression method that accounts for right‐censoring for causal inference with multiple treatments and a binary outcome. Statistics In Medicine 2023, 42: 3699-3715. PMID: 37392070, DOI: 10.1002/sim.9826.Peer-Reviewed Original ResearchConceptsRight censoringWeighted score functionCausal treatment effectsAverage treatment effectAsymptotic propertiesCensored componentPre-specified time windowEstimation consistencyRobustness propertiesSimulation studyBinary outcomesPresence of confoundersCensoringScoring functionInverse probabilityTreatment effectsEstimationSources of biasInferenceLetter CComparative effectiveness researchTreatment switchRegression methodLogistic regression modelsInsurance claims database
2021
Revisiting the genome-wide significance threshold for common variant GWAS
Chen Z, Boehnke M, Wen X, Mukherjee B. Revisiting the genome-wide significance threshold for common variant GWAS. G3: Genes, Genomes, Genetics 2021, 11: jkaa056. PMID: 33585870, PMCID: PMC8022962, DOI: 10.1093/g3journal/jkaa056.Peer-Reviewed Original ResearchConceptsGenome-wide significance thresholdP-value thresholdGWAS meta-analysesMeta-analysis consortiumExcessive false positive ratesSignificance thresholdGene set enrichmentBenjamini-Yekutieli procedureModest-sized studiesFDR-controlling proceduresGlobal lipidsMeta-analysesPathway analysisGWASReplication studyP-valueIncreased discoveryMultiple testing strategiesSample sizePositive discoveriesBenjamini-HochbergLipid levelsTesting strategiesDownstream workFDR
2013
Modeling and analysis of personal exposures to VOC mixtures using copulas
Su F, Mukherjee B, Batterman S. Modeling and analysis of personal exposures to VOC mixtures using copulas. Environment International 2013, 63: 236-245. PMID: 24333991, PMCID: PMC4233140, DOI: 10.1016/j.envint.2013.11.004.Peer-Reviewed Original ResearchConceptsVolatile organic compound mixtureVolatile organic compoundsPositive matrix factorizationCumulative cancer riskLikelihood of adverse health effectsLifetime cumulative cancer riskMixtures of pollutantsMeasurements of volatile organic compoundsToxicological mode of actionVolatile organic compound compositionMultivariate lognormal modelsPersonal exposure measurementsEvaluate cumulative risksAdverse health effectsAir exchange rateRIOPA participantsEnvironmental mixturesVehicle exhaustFit lognormal distributionsChlorinated solventsPersonal airRIOPADependence of multiple variablesToxicological modeIndoor-outdoorEnvironmental Confounding in Gene-Environment Interaction Studies
Vanderweele T, Ko Y, Mukherjee B. Environmental Confounding in Gene-Environment Interaction Studies. American Journal Of Epidemiology 2013, 178: 144-152. PMID: 23821317, PMCID: PMC3698991, DOI: 10.1093/aje/kws439.Peer-Reviewed Original ResearchConceptsGene-environment independenceGene-environment interaction studiesGene-environment interactionsEnvironmental confoundersGenetic factorsJoint testGene-environmentGenetic effectsEnvironmental factorsConfounding variablesConfoundingInteraction studiesSimulation studyJoint nullSample sizeBias estimatesFactorsIndependenceStudyTest