2020
A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank
Bi W, Fritsche L, Mukherjee B, Kim S, Lee S. A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank. American Journal Of Human Genetics 2020, 107: 222-233. PMID: 32589924, PMCID: PMC7413891, DOI: 10.1016/j.ajhg.2020.06.003.Peer-Reviewed Original ResearchConceptsControlled type I error ratesTime-to-event data analysisType I error rateGenetic studies of human diseasesGenome-wide significance levelTime-to-event phenotypesSaddlepoint approximationGenome-wide analysisEuropean ancestry samplesMinor allele frequencyStudy of human diseaseElectronic health recordsCox PH regression modelRegression modelsStandard Wald testProportional hazardsBinary phenotypesData analysisAncestry samplesGenetic studiesHealth recordsUK BiobankAllele frequenciesInpatient dataCox proportional hazards
2016
Increasing efficiency for estimating treatment–biomarker interactions with historical data
Boonstra P, Taylor J, Mukherjee B. Increasing efficiency for estimating treatment–biomarker interactions with historical data. Statistical Methods In Medical Research 2016, 25: 2959-2971. PMID: 24855118, PMCID: PMC5450810, DOI: 10.1177/0962280214535370.Peer-Reviewed Original Research
2013
Environmental 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
2009
Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis
Mukherjee B, Ahn J, Gruber S, Ghosh M, Chatterjee N. Case–Control Studies of Gene–Environment Interaction: Bayesian Design and Analysis. Biometrics 2009, 66: 934-948. PMID: 19930190, PMCID: PMC3103064, DOI: 10.1111/j.1541-0420.2009.01357.x.Peer-Reviewed Original ResearchConceptsGene-environment interactionsCase-control study of colorectal cancerStudy of gene-environment interactionsStudy of colorectal cancerGene-environment independenceRed meat consumptionBayesian designCase-control studyBayesian approachSample size determination criteriaCase-controlEpidemiological studiesColorectal cancerFrequentist counterpartsNatural wayMeat consumptionAnalyze current dataHypothesis testingDetermination criteriaSmokingEpidemiological exposureAnalysis strategyStudy
2006
A Score Test for Determining Sample Size in Matched Case‐Control Studies with Categorical Exposure
Sinha S, Mukherjee B. A Score Test for Determining Sample Size in Matched Case‐Control Studies with Categorical Exposure. Biometrical Journal 2006, 48: 35-53. PMID: 16544811, DOI: 10.1002/bimj.200510200.Peer-Reviewed Original ResearchConceptsCase-control studyCategorical exposureMatched case-control studyScore testDichotomous exposureNull hypothesisExposure variablesOdds ratioNatural orderDisease-gene associationsMatched setsDisease riskColorectal cancerPower functionSample sizeAssociationOddsGeneralizationDiseaseSetsScoresEstimationExposureStudyRisk