2025
Addressing selection bias in cluster randomized experiments via weighting
Papadogeorgou G, Liu B, Li F, Li F. Addressing selection bias in cluster randomized experiments via weighting. Biometrics 2025, 81: ujaf013. PMID: 40052595, DOI: 10.1093/biomtc/ujaf013.Peer-Reviewed Original ResearchConceptsCluster-randomized experimentCluster randomized trialAverage treatment effectSelection biasInverse probability weightingOverall populationTreatment effectsCo-paymentControl armRecruited populationProbability weightingRandomized experimentRandomized trialsPopulationEstimation strategyTreatment assignmentIndividualsRecruitment assumptionR packageOverallAnalysis approachInterventionRecruitmentWhat Should Health Professions Students Learn About Data Bias?
Shenson D, Sheares B, Fearce C. What Should Health Professions Students Learn About Data Bias? The AMA Journal Of Ethic 2025, 27: e14-20. PMID: 39745910, DOI: 10.1001/amajethics.2025.14.Peer-Reviewed Original ResearchMeSH KeywordsBiasEpidemiologyHealth OccupationsHumansPrejudiceSelection BiasStudents, Health Occupations
2024
Core Concepts in Pharmacoepidemiology: Quantitative Bias Analysis
Brown J, Hunnicutt J, Ali M, Bhaskaran K, Cole A, Langan S, Nitsch D, Rentsch C, Galwey N, Wing K, Douglas I. Core Concepts in Pharmacoepidemiology: Quantitative Bias Analysis. Pharmacoepidemiology And Drug Safety 2024, 33: e70026. PMID: 39375940, DOI: 10.1002/pds.70026.Peer-Reviewed Original ResearchConceptsQuantitative bias analysisBias analysisValidity of study findingsPharmacoepidemiological studiesRobustness of studiesEffects of medicationStudy designEffect estimatesResidual biasStudy findingsSelection biasConfoundingEstimated effectsPotential biasPharmacoepidemiologyBiasMedicationCore conceptsStudyMeasurement error
2022
Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification
Beesley L, Mukherjee B. Case studies in bias reduction and inference for electronic health record data with selection bias and phenotype misclassification. Statistics In Medicine 2022, 41: 5501-5516. PMID: 36131394, PMCID: PMC9826451, DOI: 10.1002/sim.9579.Peer-Reviewed Original ResearchConceptsElectronic health recordsElectronic health record data analysisElectronic health record settingsLeverages external data sourcesElectronic health record dataPopulation-based data sourcesEHR-based researchLongitudinal health informationUniversity of Michigan Health SystemHealth record dataSelection biasPopulation-based researchMichigan Health SystemMultiple sources of biasFactors related to selectionPatient-level dataHealth recordsHealth systemHealth informationPhenotype misclassificationSummary estimatesPhenotyping errorsCancer diagnosisSources of biasRecord dataToward a Clearer Definition of Selection Bias When Estimating Causal Effects
Lu H, Cole S, Howe C, Westreich D. Toward a Clearer Definition of Selection Bias When Estimating Causal Effects. Epidemiology 2022, 33: 699-706. PMID: 35700187, PMCID: PMC9378569, DOI: 10.1097/ede.0000000000001516.Peer-Reviewed Original ResearchImplications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies
Brown S, Lavery J, Shen R, Martin A, Kehl K, Sweeney S, Lepisto E, Rizvi H, McCarthy C, Schultz N, Warner J, Park B, Bedard P, Riely G, Schrag D, Panageas K, Sweeney S, Foti M, Khotskaya Y, Fiandalo M, Gross B, Schultz N, Mastrogiacomo B, Sarmardy M, Li M, Resnick A, Waanders A, Lilly J, Carvajal R, Rabadan R, Ingham M, Hsaio S, Abraham J, Brenton J, Rueda O, Caldas C, Valgañón M, Silva D, Boursnell C, Garcia R, Rodriguez E, Nimmervoll B, Cerami E, Ducar M, Kumari P, Lindeman N, MacConnaill L, Orechia J, Schrag D, Shivdasani P, Van Allen E, Johnson J, Jänne P, Lepisto E, Hassett M, Pimentel S, Sripakdeevong P, Janeway K, Johnson J, Meyerson M, Quinn D, Cushing O, Haigis K, Miller D, Kehl K, Gustav A, Tramontano A, Baquero S, Bell J, Green M, McCall S, Datto M, Calvo F, Andre F, Guillaume M, Dogan S, Ludovic L, Scoazec J, Ardenos M, Vassal G, Michels S, Velculescu V, Baras A, Gocke C, Brahmer J, Sawyers C, Solit D, Gardos S, Berger M, Ladanyi M, Riely G, Sirintrapun J, Panageas K, Caroline A, Thomas S, Zarski A, Zehir A, Iasonosa A, Philip J, Brown S, Kung A, Kundra R, Rudolph J, Lavery J, Rivzi H, Schwartz J, McCarthy C, Bhuiya M, Martin A, Chu C, DuBois R, van de Velde T, Meijer G, Horlings H, van Tinteren H, Lolkema M, Nijman L, Bierkens M, Hoeve J, Voest E, Hiemstra A, Sonke G, Craenmehr J, Hudecek J, Monkhorst K, Urba W, Bernard B, Piening B, Bifulco C, Tittel P, Cramer J, Guinney J, Yu T, Guo X, Acebedo A, Gold P, Bailey N, Kadri S, Segal J, Pankhuri W, Wang P, George S, Christine M, Van't Veer L, Talevich E, Wren A, Sweet-Cordero A, Turski M, Bedard P, KamelReid S, Lu Z, Pugh T, Siu L, Watt S, Leighl N, Yu C, Ahmed L, Krishna G, Virtaenen C, Chow H, Plagianakos D, Del Rossi S, Singaravelan N, Hakgor S, Qazi N, Nguyen A, Stickle N, Stricker T, Micheel C, Anderson I, Jones L, Wang L, Lovly C, LeNoue Newton M, Park B, Warner J, Fabbri D, Coco J, Ye C, Chaugai S, Mishra S, Yang Y, Wen L, Dienstmann R, Aguilar Izquierdo S, Viaplana Donato C, Mancuso F, Topaloglu U, Liu L, Guan M, Zhang W, Jin G, Knight J, D'Eletto M, Ormay E, Mane S, Bilguvar K, Zenta W, Dykas D. Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies. JAMA Oncology 2022, 8: 287-291. PMID: 34734967, PMCID: PMC9190030, DOI: 10.1001/jamaoncol.2021.5153.Peer-Reviewed Original ResearchMeSH KeywordsBiasCarcinoma, Non-Small-Cell LungGenomicsHumansLung NeoplasmsSelection BiasSurvival AnalysisConceptsOverall survivalStage IV non-small cell lung cancerNon-small cell lung cancerStage IV colorectal cancerUnadjusted median survivalCell lung cancerMedian survivalStudy entryCancer outcomesColorectal cancerLung cancerMolecular testingSurvival analysisGeneralizable research findingsClinical genomic studiesSurvivalCancerSelection biasAppropriate statistical methodsDiagnosisAmerican Association
2021
Clarifying selection bias in cluster randomized trials
Li F, Tian Z, Bobb J, Papadogeorgou G, Li F. Clarifying selection bias in cluster randomized trials. Clinical Trials 2021, 19: 33-41. PMID: 34894795, DOI: 10.1177/17407745211056875.Peer-Reviewed Original ResearchConceptsAverage treatment effectCluster randomized trialPost-randomization selection biasPrincipal strataAnalysis of cluster randomized trialsSelection biasCausal effectsCovariate adjustment methodsData generating processRecruited populationPrincipal stratification frameworkPresence of selection biasHeterogeneous treatment effectsRegression adjustment methodEstimate causal effectsRandomized trialsElectronic health recordsOverall populationEffect heterogeneityIntention-to-treat analysisSimulation studyTreatment effectsEmpirical performanceEstimandsEstimation strategy
2020
Outcomes after Thrombectomy for Minor Stroke: A Meta-Analysis
Wu X, Khunte M, Payabvash S, Zhu C, Brackett A, Matouk CC, Gandhi D, Sanelli P, Malhotra A. Outcomes after Thrombectomy for Minor Stroke: A Meta-Analysis. World Neurosurgery 2020, 149: e1140-e1154. PMID: 33359881, DOI: 10.1016/j.wneu.2020.12.047.Peer-Reviewed Original ResearchConceptsSymptomatic intracranial hemorrhageBest medical managementMedical managementMechanical thrombectomyExcellent outcomesBetter outcomesImmediate thrombectomyMinor strokeIncidence of sICHLarge vessel occlusionSignificant selection biasStroke symptomatologyIntravenous thrombolysisBaseline characteristicsPooled proportionIntracranial hemorrhageOdds ratioThrombectomyMT outcomesMeta-AnalysisPatientsMortalityOutcomesStrokeSelection biasStatistical Inference for Association Studies Using Electronic Health Records: Handling Both Selection Bias and Outcome Misclassification
Beesley L, Mukherjee B. Statistical Inference for Association Studies Using Electronic Health Records: Handling Both Selection Bias and Outcome Misclassification. Biometrics 2020, 78: 214-226. PMID: 33179768, DOI: 10.1111/biom.13400.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsElectronic health record data analysisElectronic health record settingsSelection biasMichigan Genomics InitiativeAssociation studiesEHR-linkedHealth researchInverse probability weighting methodStudy sampleEffect estimatesProbability weighting methodLack of representativenessType I errorSurvey sampling literatureStandard error estimatesGold standard labelsDisease statusError estimatesStatistical inferenceMisclassificationInference strategySampling literatureStandard labelsQuantifying treatment selection bias effect on survival in comparative effectiveness research: findings from low-risk prostate cancer patients
Miccio JA, Talcott WJ, Jairam V, Park HS, Yu JB, Leapman MS, Johnson SB, King MT, Nguyen PL, Kann BH. Quantifying treatment selection bias effect on survival in comparative effectiveness research: findings from low-risk prostate cancer patients. Prostate Cancer And Prostatic Diseases 2020, 24: 414-422. PMID: 32989262, DOI: 10.1038/s41391-020-00291-3.Peer-Reviewed Original ResearchConceptsProstate cancer-specific survivalLow-risk prostate cancerExternal beam radiotherapyTreatment selection biasOverall survivalRadical prostatectomyProstate cancerOS differenceLow-risk prostate cancer patientsCancer-specific survivalEnd Results (SEER) databaseProstate cancer patientsClinical trial designEffectiveness researchComparative effectiveness researchPropensity-score matchingMethodsThe SurveillanceTreatment guidelinesResults databaseEntire cohortResultsA totalCancer patientsTreatment modalitiesNational registryPatient managementSampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable
Mavian C, Pond SK, Marini S, Magalis BR, Vandamme AM, Dellicour S, Scarpino SV, Houldcroft C, Villabona-Arenas J, Paisie TK, Trovão NS, Boucher C, Zhang Y, Scheuermann RH, Gascuel O, Lam TT, Suchard MA, Abecasis A, Wilkinson E, de Oliveira T, Bento AI, Schmidt HA, Martin D, Hadfield J, Faria N, Grubaugh ND, Neher RA, Baele G, Lemey P, Stadler T, Albert J, Crandall KA, Leitner T, Stamatakis A, Prosperi M, Salemi M. Sampling bias and incorrect rooting make phylogenetic network tracing of SARS-COV-2 infections unreliable. Proceedings Of The National Academy Of Sciences Of The United States Of America 2020, 117: 12522-12523. PMID: 32381734, PMCID: PMC7293693, DOI: 10.1073/pnas.2007295117.Peer-Reviewed Original Research
2019
Divergent estimates of HIV incidence among people who inject drugs in Ukraine
Morozova O, Booth RE, Dvoriak S, Dumchev K, Sazonova Y, Saliuk T, Crawford FW. Divergent estimates of HIV incidence among people who inject drugs in Ukraine. International Journal Of Drug Policy 2019, 73: 156-162. PMID: 31405731, PMCID: PMC6899203, DOI: 10.1016/j.drugpo.2019.07.023.Peer-Reviewed Original ResearchConceptsHIV incidenceStudy subjectsBio-behavioral surveillance surveysRecent syringe sharingPopulation of PWIDLongitudinal cohort studyDrug of choiceLow-risk individualsCross-sectional surveyBaseline characteristicsCohort studyAverage monthly numberRisk stratificationSyringe sharingIntervention trialsSurveillance SurveyHigh incidenceRisk individualsRCTsPWIDStudy designIncidenceDrugsFuture surveillanceSignificant differencesEmpirical evidence of recruitment bias in a network study of people who inject drugs
Zeng L, Li J, Crawford FW. Empirical evidence of recruitment bias in a network study of people who inject drugs. The American Journal Of Drug And Alcohol Abuse 2019, 45: 460-469. PMID: 30896982, PMCID: PMC7680667, DOI: 10.1080/00952990.2019.1584203.Peer-Reviewed Original Research
2018
Temporal Mitogenomics of the Galapagos Giant Tortoise from Pinzón Reveals Potential Biases in Population Genetic Inference
Jensen E, Miller J, Edwards D, Garrick R, Tapia W, Caccone A, Russello M. Temporal Mitogenomics of the Galapagos Giant Tortoise from Pinzón Reveals Potential Biases in Population Genetic Inference. Journal Of Heredity 2018, 109: 631-640. PMID: 29659893, DOI: 10.1093/jhered/esy016.Peer-Reviewed Original ResearchHow to investigate and adjust for selection bias in cohort studies
Nohr EA, Liew Z. How to investigate and adjust for selection bias in cohort studies. Acta Obstetricia Et Gynecologica Scandinavica 2018, 97: 407-416. PMID: 29415329, DOI: 10.1111/aogs.13319.Peer-Reviewed Original ResearchMeSH KeywordsCohort StudiesData Interpretation, StatisticalGynecologyHumansObstetricsResearch DesignSelection BiasConceptsCohort studyDanish National Birth CohortSelection biasLong-term followNational Birth CohortLongitudinal cohort studyCase-control studySubsequent follow upsExposure-outcome associationsTraditional epidemiological approachesPreventable causeFollow-upInverse probability weightingBirth cohortInitial participation rateStudy designEpidemiological approachLimited dataCohortDiseaseBias analysisProbability weightingParticipation ratesParticipantsLess disease
2017
Sex disparities in substance abuse research: Evaluating 23 years of structural neuroimaging studies
Lind KE, Gutierrez EJ, Yamamoto DJ, Regner MF, McKee SA, Tanabe J. Sex disparities in substance abuse research: Evaluating 23 years of structural neuroimaging studies. Drug And Alcohol Dependence 2017, 173: 92-98. PMID: 28212516, PMCID: PMC5581940, DOI: 10.1016/j.drugalcdep.2016.12.019.Peer-Reviewed Original ResearchConceptsSubstance use statusSex differencesBrain imaging researchSex disparitiesBrain imaging studiesStructural brain imaging studiesSubstance use studiesSubstance effectsSex effectsBrain morphometric changesStructural neuroimaging studiesSubstance use disordersUse statusNeuroimaging studiesNumber of participantsSubstance abuse researchBrain morphometryStructural brainGreater biasClinical courseSubstance abuseAnalytic approachBrain structuresProportion of studiesImaging research
2015
Variation and Trends in the Documentation of National Institutes of Health Stroke Scale in GWTG-Stroke Hospitals
Reeves M, Smith E, Fonarow G, Zhao X, Thompson M, Peterson E, Schwamm L, Olson D. Variation and Trends in the Documentation of National Institutes of Health Stroke Scale in GWTG-Stroke Hospitals. Circulation Cardiovascular Quality And Outcomes 2015, 8: s90-8. PMID: 26515215, DOI: 10.1161/circoutcomes.115.001775.Peer-Reviewed Original ResearchConceptsGWTG-Stroke hospitalsHealth Stroke ScaleNIHSS scoreDocumentation ratesNIHSS dataStroke ScaleMultivariable logistic regression modelAcute ischemic strokeHospital-level factorsPrimary stroke centerImportant prognostic variablesPatient-level predictorsNational InstituteLogistic regression modelsMedian NIHSSIschemic strokeStroke centersThrombolysis candidatesClinical registryPrognostic variablesNIHSSHospitalPatientsStrokeLow documentationToward a Clarification of the Taxonomy of “Bias” in Epidemiology Textbooks
Schwartz S, Campbell UB, Gatto NM, Gordon K. Toward a Clarification of the Taxonomy of “Bias” in Epidemiology Textbooks. Epidemiology 2015, 26: 216-222. PMID: 25536455, DOI: 10.1097/ede.0000000000000224.Peer-Reviewed Original Research
2014
Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error
Shipitsin M, Small C, Choudhury S, Giladi E, Friedlander S, Nardone J, Hussain S, Hurley AD, Ernst C, Huang YE, Chang H, Nifong TP, Rimm DL, Dunyak J, Loda M, Berman DM, Blume-Jensen P. Identification of proteomic biomarkers predicting prostate cancer aggressiveness and lethality despite biopsy-sampling error. British Journal Of Cancer 2014, 111: 1201-1212. PMID: 25032733, PMCID: PMC4453845, DOI: 10.1038/bjc.2014.396.Peer-Reviewed Original ResearchMeSH KeywordsActininAgedAlkyl and Aryl TransferasesArea Under CurveBiomarkers, TumorBiopsy, Fine-NeedleCullin ProteinsDNA-Binding ProteinsFollow-Up StudiesHSP70 Heat-Shock ProteinsHumansImage Processing, Computer-AssistedMaleMembrane ProteinsMiddle AgedMitochondrial ProteinsNeoplasm GradingNeoplasm StagingPhosphorylationProstateProstatic NeoplasmsProteomicsRibosomal Protein S6RNA-Binding Protein FUSROC CurveSelection BiasSmad2 ProteinSmad4 ProteinTissue Array AnalysisVoltage-Dependent Anion Channel 1Y-Box-Binding Protein 1ConceptsProstate cancer aggressivenessCancer aggressivenessLarge patient cohortLow Gleason gradePatient cohortTumor microarrayLethal outcomeProstatectomy samplesGleason gradeSignificant overtreatmentBiopsy interpretationProstatectomy tissuePatient samplesBiopsy testsProteomic biomarkersCancer biomarker discoveryExpert pathologistsMarker signaturesTumor heterogeneityBiomarkersAggressivenessProtein biomarkersBiomarker discoveryQuantitative proteomics approachThe impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification
Stenzel S, Ahn J, Boonstra P, Gruber S, Mukherjee B. The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification. European Journal Of Epidemiology 2014, 30: 413-423. PMID: 24894824, PMCID: PMC4256150, DOI: 10.1007/s10654-014-9908-1.Peer-Reviewed Original ResearchConceptsG-E interactionsExposure informationDetection of gene-environment interactionsPrevalence of exposureGene-environment interactionsSampling designCase-control studyRandom selection of subjectsPerformance of sampling designsCase-onlyExposure prevalenceJoint testExposure misclassificationCase-controlRare exposuresMarginal associationSelection of subjectsType I errorEmpirical simulation studyIdeal sampling schemesJoint effectsPrevalenceRandom selectionG-EMisclassification
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