2022
High pre-diagnosis inflammation-related risk score associated with decreased ovarian cancer survival
Brieger KK, Phung MT, Mukherjee B, Bakulski KM, Anton-Culver H, Bandera EV, Bowtell DDL, Cramer DW, DeFazio A, Doherty JA, Fereday S, Fortner RT, Gentry-Maharaj A, Goode EL, Goodman MT, Harris HR, Matsuo K, Menon U, Modugno F, Moysich KB, Qin B, Ramus SJ, Risch HA, Rossing MA, Schildkraut JM, Trabert B, Vierkant RA, Winham SJ, Wentzensen N, Wu AH, Ziogas A, Khoja L, Cho KR, McLean K, Richardson J, Grout B, Chase A, Deurloo CM, Odunsi K, Nelson BH, Brenton JD, Terry KL, Pharaoh P, Berchuck A, Hanley GE, Webb PM, Pike MC, Pearce CL. High pre-diagnosis inflammation-related risk score associated with decreased ovarian cancer survival. Cancer Epidemiology Biomarkers & Prevention 2022, 31: cebp.epi-21-0977-a.2021. PMID: 34789471, PMCID: PMC9281656, DOI: 10.1158/1055-9965.epi-21-0977.Peer-Reviewed Original ResearchMeSH KeywordsAgedCarcinoma, Ovarian EpithelialFemaleHealth BehaviorHumansInflammationMiddle AgedOvarian NeoplasmsProportional Hazards ModelsRisk AssessmentConceptsOvarian cancer survivalCox proportional hazards modelProportional hazards modelCancer survivalOvarian cancerRisk scoreHazards modelNonsteroidal anti-inflammatory drug useAnti-inflammatory drug useMenopausal hormone therapy useEnvironmental tobacco smoke exposureInvasive epithelial ovarian cancerHormone therapy usePelvic inflammatory diseaseInflammation-related factorsPolycystic ovarian syndromeTobacco smoke exposureBody mass indexRisk of deathEpithelial ovarian cancerOvarian Cancer Association ConsortiumOvarian cancer diagnosisHigh death rateAspirin useOvarian syndrome
2020
Menopausal hormone therapy prior to the diagnosis of ovarian cancer is associated with improved survival
Brieger KK, Peterson S, Lee AW, Mukherjee B, Bakulski KM, Alimujiang A, Anton-Culver H, Anglesio MS, Bandera EV, Berchuck A, Bowtell DDL, Chenevix-Trench G, Cho KR, Cramer DW, DeFazio A, Doherty JA, Fortner RT, Garsed DW, Gayther SA, Gentry-Maharaj A, Goode EL, Goodman MT, Harris HR, Høgdall E, Huntsman DG, Shen H, Jensen A, Johnatty SE, Jordan SJ, Kjaer SK, Kupryjanczyk J, Lambrechts D, McLean K, Menon U, Modugno F, Moysich K, Ness R, Ramus SJ, Richardson J, Risch H, Rossing MA, Trabert B, Wentzensen N, Ziogas A, Terry KL, Wu AH, Hanley GE, Pharoah P, Webb PM, Pike MC, Pearce CL, Consortium F. Menopausal hormone therapy prior to the diagnosis of ovarian cancer is associated with improved survival. Gynecologic Oncology 2020, 158: 702-709. PMID: 32641237, PMCID: PMC7487048, DOI: 10.1016/j.ygyno.2020.06.481.Peer-Reviewed Original ResearchConceptsMenopausal hormone therapyOvarian cancer survivalMHT useResidual diseaseHormone therapyCancer survivalOvarian cancerHigh-grade serous carcinomaMacroscopic residual diseaseHormone therapy useFavorable prognostic factorPost-menopausal womenOvarian Cancer Association ConsortiumProportional hazards modelRecency of useImproved survivalPrognostic factorsTherapy useSerous carcinomaOvarian carcinomaHazards modelSmall studyAdvanced stageLarger studyLogistic regressionA 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
2019
Association Between Life Purpose and Mortality Among US Adults Older Than 50 Years
Alimujiang A, Wiensch A, Boss J, Fleischer N, Mondul A, McLean K, Mukherjee B, Pearce C. Association Between Life Purpose and Mortality Among US Adults Older Than 50 Years. JAMA Network Open 2019, 2: e194270. PMID: 31125099, PMCID: PMC6632139, DOI: 10.1001/jamanetworkopen.2019.4270.Peer-Reviewed Original ResearchMeSH KeywordsAttitude to DeathCause of DeathCohort StudiesFemaleHumansLife Change EventsMaleMiddle AgedProportional Hazards ModelsProspective StudiesQuality of LifeRisk FactorsConceptsHealth and Retirement StudyCause-specific mortalityAll-causeHealth outcomesUS adultsHealth and Retirement Study participantsCohort study of US adultsCause-specific mortality analysesInterview periodStudy of US adultsInfluence health outcomesAssociated with all-cause mortalityLife purposeCohort study sampleWeighted Cox proportional hazards modelsNational cohort studyPurpose in lifeQuality of lifeAll-cause mortalityCox proportional hazards modelsEnhance overall qualityAssociated with decreased mortalityProportional hazards modelRetirement StudyMortality associations
2017
Complete hazard ranking to analyze right-censored data: An ALS survival study
Huang Z, Zhang H, Boss J, Goutman S, Mukherjee B, Dinov I, Guan Y, . Complete hazard ranking to analyze right-censored data: An ALS survival study. PLOS Computational Biology 2017, 13: e1005887. PMID: 29253881, PMCID: PMC5749893, DOI: 10.1371/journal.pcbi.1005887.Peer-Reviewed Original Research
2016
Repeated measures of inflammation and oxidative stress biomarkers in preeclamptic and normotensive pregnancies
Ferguson K, Meeker J, McElrath T, Mukherjee B, Cantonwine D. Repeated measures of inflammation and oxidative stress biomarkers in preeclamptic and normotensive pregnancies. American Journal Of Obstetrics And Gynecology 2016, 216: 527.e1-527.e9. PMID: 28043842, PMCID: PMC5420472, DOI: 10.1016/j.ajog.2016.12.174.Peer-Reviewed Original ResearchConceptsTumor necrosis factor-aNormotensive pregnanciesC-reactive proteinMeasures of inflammationHazard ratioOxidative stress biomarkersInterleukin-1bTime pointsInflammation biomarkersStudy visitsTime of preeclampsia diagnosisPrediction of preeclampsiaOxidative stressHigher body mass indexOxidative stress biomarker 8-isoprostaneStress biomarkersCox proportional hazards modelsCompared to other time pointsBody mass indexProspective birth cohortCalculate hazard ratiosSmall study populationPanel of inflammationFactor AProportional hazards modelMicrosatellite Alterations With Allelic Loss at 9p24.2 Signify Less-Aggressive Colorectal Cancer Metastasis
Koi M, Garcia M, Choi C, Kim H, Koike J, Hemmi H, Nagasaka T, Okugawa Y, Toiyama Y, Kitajima T, Imaoka H, Kusunoki M, Chen Y, Mukherjee B, Boland C, Carethers J. Microsatellite Alterations With Allelic Loss at 9p24.2 Signify Less-Aggressive Colorectal Cancer Metastasis. Gastroenterology 2016, 150: 944-955. PMID: 26752111, PMCID: PMC4808397, DOI: 10.1053/j.gastro.2015.12.032.Peer-Reviewed Original ResearchMeSH KeywordsBiomarkers, TumorChi-Square DistributionChromosome AberrationsChromosomes, Human, Pair 9Colorectal NeoplasmsDisease ProgressionDisease-Free SurvivalFemaleGenetic Predisposition to DiseaseHumansJapanKaplan-Meier EstimateLiver NeoplasmsLogistic ModelsLoss of HeterozygosityMaleMicrosatellite RepeatsMiddle AgedNeoplasm Recurrence, LocalNeoplasm StagingOdds RatioPhenotypeProportional Hazards ModelsProto-Oncogene Proteins B-rafProto-Oncogene Proteins p21(ras)Republic of KoreaRisk FactorsTime FactorsTreatment OutcomeConceptsPrimary colorectal tumorsLoss of heterozygosityLiver metastasesColorectal cancerColorectal tumorsElevated microsatellite alterationsMicrosatellite alterationsStage IICurative treatment of patientsStage III colorectal cancerOverall survival of patientsSurvival of patientsIII colorectal cancerTumor to liverColorectal cancer recurrenceTreatment of patientsMatched liver metastasesCancer cell nucleiMatched metastasesDisease recurrenceOverall survivalPrognostic factorsAllelic lossNo significant differenceCurative treatment
2013
Transcriptome Profiling Identifies HMGA2 as a Biomarker of Melanoma Progression and Prognosis
Raskin L, Fullen D, Giordano T, Thomas D, Frohm M, B. K, Ahn J, Mukherjee B, Johnson T, Gruber S. Transcriptome Profiling Identifies HMGA2 as a Biomarker of Melanoma Progression and Prognosis. Journal Of Investigative Dermatology 2013, 133: 2585-2592. PMID: 23633021, PMCID: PMC4267221, DOI: 10.1038/jid.2013.197.Peer-Reviewed Original ResearchConceptsAmerican Joint Committee on CancerOverall survivalTissue microarrayPrimary melanomaMelanoma pathogenesisMelanoma progressionAssociated with disease-free survivalAnalysis of tissue microarraysMetastases-free survivalDisease-free survivalHMGA2 overexpressionCox proportional hazards regression modelsLog-rank testPredictors of survivalProportional hazards regression modelsHazards regression modelsBRAF/NRAS mutationsPrimary tumorPrognostic featuresMelanoma metastasesClinicopathological characteristicsReal-time PCRGenetic alterationsAQUA analysisMelanoma development
2011
High Risk of Colorectal and Endometrial Cancer in Ashkenazi Families With the MSH2 A636P Founder Mutation
Mukherjee B, Rennert G, Ahn J, Dishon S, Lejbkowicz F, Rennert H, Shiovitz S, Moreno V, Gruber S. High Risk of Colorectal and Endometrial Cancer in Ashkenazi Families With the MSH2 A636P Founder Mutation. Gastroenterology 2011, 140: 1919-1926. PMID: 21419771, PMCID: PMC4835182, DOI: 10.1053/j.gastro.2011.02.071.Peer-Reviewed Original ResearchMeSH KeywordsAdultAge FactorsAgedAged, 80 and overCase-Control StudiesColorectal Neoplasms, Hereditary NonpolyposisEndometrial NeoplasmsFemaleFounder EffectGene FrequencyGenetic Predisposition to DiseaseGenetic TestingHeredityHumansIsraelJewsLikelihood FunctionsMaleMass ScreeningMiddle AgedMutationMutS Homolog 2 ProteinPedigreePenetrancePhenotypeProportional Hazards ModelsRegistriesRisk AssessmentRisk FactorsSex FactorsYoung AdultConceptsRisk of colorectal cancerHazard ratioColorectal cancerCumulative riskPopulation-basedLifetime risk of colorectal cancerCumulative risk of colorectal cancerEstimates of colorectal cancerAge-specific cumulative riskHigh risk of colorectalCases of colorectal cancerModified segregation analysisRisk of colorectalClinical genetics servicesClinic-based sampleEndometrial cancerRisk of ECCase-control studyGenetic servicesLynch syndromeCancer screeningEC riskLifetime riskAshkenazi familiesEstimated penetrance
2010
A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome
Boonstra P, Gruber S, Raymond V, Huang S, Timshel S, Nilbert M, Mukherjee B. A review of statistical methods for testing genetic anticipation: looking for an answer in Lynch syndrome. Genetic Epidemiology 2010, 34: 756-768. PMID: 20878717, PMCID: PMC3894615, DOI: 10.1002/gepi.20534.Peer-Reviewed Original ResearchConceptsAffected parent-child pairsDanish HNPCC registerParent-child pairsLynch syndromePaired t-testGenetic anticipationLynch syndrome cohortCancer genetics clinicsT-testEvidence of genetic anticipationFamily membersClinic-based populationRandom-effects modelGenetics clinicAffected pairsMismatch repairUnaffected family membersFamilial correlationsAffected parentType I errorSyndrome cohortRegression modelsPedigree dataDecreasing ageAscertainment
2009
Risk of Pancreatic Cancer in Families With Lynch Syndrome
Kastrinos F, Mukherjee B, Tayob N, Wang F, Sparr J, Raymond V, Bandipalliam P, Stoffel E, Gruber S, Syngal S. Risk of Pancreatic Cancer in Families With Lynch Syndrome. JAMA 2009, 302: 1790-1795. PMID: 19861671, PMCID: PMC4091624, DOI: 10.1001/jama.2009.1529.Peer-Reviewed Original ResearchMeSH KeywordsAdaptor Proteins, Signal TransducingAdultAgedAged, 80 and overColorectal Neoplasms, Hereditary NonpolyposisDNA Mismatch RepairDNA Mutational AnalysisDNA-Binding ProteinsFemaleGenotypeGerm-Line MutationHumansMaleMiddle AgedMutL Protein Homolog 1MutS Homolog 2 ProteinNuclear ProteinsPancreatic NeoplasmsPedigreePhenotypeProportional Hazards ModelsRegistriesRiskSEER ProgramYoung AdultConceptsRisk of pancreatic cancerMutations of DNA mismatch repairPancreatic cancer riskGermline MMR gene mutationsMMR gene mutationsCancer riskHazard ratio estimatesLynch syndromeInherited cause of colorectal cancerAge-specific cumulative riskCumulative riskCumulative risk of pancreatic cancerFamily history of pancreatic cancerHistory of pancreatic cancerFamilial cancer registryGeneral populationModified segregation analysisCause of colorectal cancerUniversity of Michigan Comprehensive Cancer CenterComprehensive cancer centerGene mutation carriersCases of pancreatic cancerStudy start dateDana-Farber Cancer InstituteExtracolonic tumorsGraphical diagnostics to check model misspecification for the proportional odds regression model
Liu I, Mukherjee B, Suesse T, Sparrow D, Park S. Graphical diagnostics to check model misspecification for the proportional odds regression model. Statistics In Medicine 2009, 28: 412-429. PMID: 18693299, DOI: 10.1002/sim.3386.Peer-Reviewed Original ResearchConceptsCovariate effectsOrdinal responsesModel misspecificationProportional odds regression modelStudy covariate effectsGoodness-of-fit statisticsClass of modelsNumerical methodFunctional misspecificationBinary responsesGraphical diagnosticsSimulation studyCumulative logitsMisspecificationCumulative sumRegression modelsGraphical methodSumArbogastVA Normative Aging StudyCovariatesProportional odds regressionClass
2008
Fitting stratified proportional odds models by amalgamating conditional likelihoods
Mukherjee B, Ahn J, Liu I, Rathouz P, Sánchez B. Fitting stratified proportional odds models by amalgamating conditional likelihoods. Statistics In Medicine 2008, 27: 4950-4971. PMID: 18618428, PMCID: PMC3085191, DOI: 10.1002/sim.3325.Peer-Reviewed Original ResearchConceptsNuisance parametersConditional likelihoodProportional odds modelStratum-specific nuisance parametersCumulative logit modelStratum-specific interceptsGeneral regression frameworkMultiple ordered categoriesOdds modelContinuous covariatesSandwich estimatorData examplesBinary exposureRobust sandwich estimatorLikelihood principleProportional oddsStandard softwareRegression frameworkNatural choiceOutcome modelEstimationClassical methodsStratified dataLogistic regression modelsRandom-effects model
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