2018
Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE)
Ferguson K, Yu Y, Cantonwine D, McElrath T, Meeker J, Mukherjee B. Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE). Paediatric And Perinatal Epidemiology 2018, 32: 469-473. PMID: 30016545, PMCID: PMC6939297, DOI: 10.1111/ppe.12486.Peer-Reviewed Original ResearchConceptsLinear mixed modelsComplete-case analysisMultiple imputationEpidemiological studies of risk factorsImputed datasetsComplete-caseDemographic factorsStudy of risk factorsLIFECODES birth cohortUltrasound measurementsCalculate associationsBirth cohortCross-sectionEpidemiological studiesRisk factorsStudy visitsLongitudinal analysisParametric linear mixed modelImputationMissing dataMixed modelsLongitudinal measurementsSample sizeCovariate dataGrowth restriction
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 analysisWhere science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world
Markovitz A, Goldstick J, Levy K, Cevallos W, Mukherjee B, Trostle J, Eisenberg J. Where science meets policy: comparing longitudinal and cross-sectional designs to address diarrhoeal disease burden in the developing world. International Journal Of Epidemiology 2012, 41: 504-513. PMID: 22253314, PMCID: PMC3324455, DOI: 10.1093/ije/dyr194.Peer-Reviewed Original ResearchConceptsCross-sectional studyCross-sectional designEffect estimatesLongitudinal studyRisk factorsDisease risk factorsRisk factor distributionInforming public health policyPublic health policiesPublic health communityRisk factor effectsHousehold risk factorsDiarrhoeal disease burdenFactor effect estimatesHealth policyDiarrhoeal disease surveillanceEcuadorian villageNational policy decisionsHealth communityDisease burdenCross-sectionDisease surveillanceFactor distributionRiskGeographic regions