2018
Associations between maternal phenol and paraben urinary biomarkers and maternal hormones during pregnancy: A repeated measures study
Aker A, Johns L, McElrath T, Cantonwine D, Mukherjee B, Meeker J. Associations between maternal phenol and paraben urinary biomarkers and maternal hormones during pregnancy: A repeated measures study. Environment International 2018, 113: 341-349. PMID: 29366524, PMCID: PMC5866216, DOI: 10.1016/j.envint.2018.01.006.Peer-Reviewed Original ResearchConceptsThyroid hormonesAssociated with altered thyroid hormone levelsFetal health outcomesThyroid hormone levelsIQR increaseMultivariate regression analysisGestational ageMultivariate linear regression modelFetal neurodevelopmentPregnant womenFree thyroxinePotential biological mechanismsTime of exposureUrinary biomarkersCohort studyHormone levelsParaben biomarkersTotal triiodothyronineCase-control samplePregnancyBlood samplesTotal thyroxineHormone concentrationsHealth outcomesHormone
2012
Likelihood‐based methods for regression analysis with binary exposure status assessed by pooling
Lyles R, Tang L, Lin J, Zhang Z, Mukherjee B. Likelihood‐based methods for regression analysis with binary exposure status assessed by pooling. Statistics In Medicine 2012, 31: 2485-2497. PMID: 22415630, PMCID: PMC3528351, DOI: 10.1002/sim.4426.Peer-Reviewed Original ResearchConceptsPopulation-based case-control study of colorectal cancerCase-control study of colorectal cancerPopulation-based case-control studyStudy of colorectal cancerExposure statusBinary outcomesRegression modelsCase-control sampleLogistic regression modelsGene-disease associationsObserved binary outcomeStudy designEpidemiological studiesColorectal cancerAssess exposureMaximum likelihood analysisRegression analysisLikelihood-based methodsExposure assessmentMaximum likelihood approachLikelihood approachCross-sectionSimulation studyOutcomesLikelihood analysisEfficient designs of gene–environment interaction studies: implications of Hardy–Weinberg equilibrium and gene–environment independence
Chen J, Kang G, VanderWeele T, Zhang C, Mukherjee B. Efficient designs of gene–environment interaction studies: implications of Hardy–Weinberg equilibrium and gene–environment independence. Statistics In Medicine 2012, 31: 2516-2530. PMID: 22362617, PMCID: PMC3448495, DOI: 10.1002/sim.4460.Peer-Reviewed Original ResearchConceptsPresence of G-E interactionsG-E interactionsSubsample of casesGene-environmentHardy-Weinberg equilibriumG-E independenceGene-environment interaction studiesGene-environment independenceRandom subsampleGenetic susceptibility variantsCase-control sampleEnvironmental risk factorsSusceptibility variantsExternal control dataRisk factorsGenetic effectsWald statisticInteraction studiesSubsampleVariable EControl dataEnvironmental effectsIndependenceDataWald