2021
SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation
Zimmermann L, Bhattacharya S, Purkayastha S, Kundu R, Bhaduri R, Ghosh P, Mukherjee B. SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation. Studies In Microeconomics 2021, 9: 137-179. DOI: 10.1177/23210222211054324.Peer-Reviewed Original ResearchStudy end dateExcess deathsMeta-analysisCase fatality rateState-specific estimatesGlobal Index MedicusEnd dateFatality rateSARS-CoV-2-related deathInfection fatality rateSARS-CoV-2 infection fatality rateRandom-effects modelDeath dataDeath reportingExcess death estimatesImprove death reportingNationwide estimatesSystematic reviewDatabases PubMedPRISMA guidelinesWave 2DerSimonian-Laird estimatorSystematic searchWave 1Model-based estimates
2011
Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome
Boonstra P, Mukherjee B, Taylor J, Nilbert M, Moreno V, Gruber S. Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome. Biometrics 2011, 67: 1627-1637. PMID: 21627626, PMCID: PMC3176998, DOI: 10.1111/j.1541-0420.2011.01607.x.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAgedAnticipation, GeneticBayes TheoremChildChild, PreschoolColorectal Neoplasms, Hereditary NonpolyposisComputer SimulationDenmarkFemaleHumansInfantInfant, NewbornMaleMiddle AgedModels, GeneticModels, StatisticalMutationPolymorphism, Single NucleotidePrevalenceRisk AssessmentRisk FactorsYoung AdultConceptsLynch syndromeBirth cohortGenetic anticipationHereditary nonpolyposis colorectal cancerCancer registry dataNonpolyposis colorectal cancerDanish Cancer RegisterGenetic counseling clinicAge-specific incidenceHigh-risk familiesRandom-effects modelCancer RegisterRegistry dataCounseling clinicMismatch repairRandom effectsSecular trendsMedical practiceColorectal cancerSurvival analysis methodsEffects modelConfounding effectsLynchFlexible random effects modelModel fit diagnostics
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
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