2025
[18F]MK-6240 Radioligand Delivery Indices as Surrogates of Cerebral Perfusion: Bias and Correlation Against [15O]Water.
Fu J, Juttukonda M, Garimella A, Salvatore A, Lois C, Ranasinghe A, Efthimiou N, Sari H, Aye W, Guehl N, El Fakhri G, Johnson K, Dickerson B, Izquierdo-Garcia D, Catana C, Price J. [18F]MK-6240 Radioligand Delivery Indices as Surrogates of Cerebral Perfusion: Bias and Correlation Against [15O]Water. Journal Of Nuclear Medicine 2025, 66: 410-417. PMID: 39947916, PMCID: PMC11876731, DOI: 10.2967/jnumed.124.268701.Peer-Reviewed Original ResearchEvaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies
Jing N, Lu Y, Tong J, Weaver J, Ryan P, Xu H, Chen Y. Evaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies. Journal Of Biomedical Informatics 2025, 163: 104787. PMID: 39904407, DOI: 10.1016/j.jbi.2025.104787.Peer-Reviewed Original ResearchConceptsType I errorIntegrated likelihood estimatorsElectronic health recordsUse-case analysisLikelihood estimationLow prevalence outcomesUse-casesBias reductionNaive methodEffect sizeSynthetic dataPhenotyping algorithmsEstimation biasReal-world scenariosStatistical inferenceSimulation studyAssociation effect sizesAccurate prior informationBinary outcomesPoint estimatesAssociation estimatesStatistical powerHealth recordsKnowledge-guidedOutcome prevalenceBalancing Data Quality and Bias: Investigating Functional Connectivity Exclusions in the Adolescent Brain Cognitive Development℠ (ABCD Study) Across Quality Control Pathways
Peverill M, Russell J, Keding T, Rich H, Halvorson M, King K, Birn R, Herringa R. Balancing Data Quality and Bias: Investigating Functional Connectivity Exclusions in the Adolescent Brain Cognitive Development℠ (ABCD Study) Across Quality Control Pathways. Human Brain Mapping 2025, 46: e70094. PMID: 39788921, PMCID: PMC11717557, DOI: 10.1002/hbm.70094.Peer-Reviewed Original ResearchUsing Quantitative Bias Analysis to Adjust for Misclassification of COVID‐19 Outcomes: An Applied Example of Inhaled Corticosteroids and COVID‐19 Outcomes
Bokern M, Rentsch C, Quint J, Hunnicutt J, Douglas I, Schultze A. Using Quantitative Bias Analysis to Adjust for Misclassification of COVID‐19 Outcomes: An Applied Example of Inhaled Corticosteroids and COVID‐19 Outcomes. Pharmacoepidemiology And Drug Safety 2025, 34: e70086. PMID: 39776023, PMCID: PMC11706700, DOI: 10.1002/pds.70086.Peer-Reviewed Original ResearchConceptsProbabilistic bias analysisRisk of COVID-19 hospitalisationCOVID-19 hospitalisationChronic obstructive pulmonary diseaseOutcome misclassificationInhaled corticosteroid usersCOVID-19 outcomesIncreased risk of COVID-19 hospitalisationClinical Practice Research Datalink AurumInhaled corticosteroidsLogistic regressionQuantitative bias analysisBias analysisTriple therapySummary-levelObstructive pulmonary diseaseImpact treatment effect estimatesWhat 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 Research
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 errorGroup Response‐Adaptive Randomization With Delayed and Missing Responses
Zhai G, Li Y, Zhang L, Hu F. Group Response‐Adaptive Randomization With Delayed and Missing Responses. Statistics In Medicine 2024, 43: 5047-5059. PMID: 39285137, DOI: 10.1002/sim.10220.Peer-Reviewed Original ResearchRestrictive versus liberal red blood cell transfusion strategies for people with haematological malignancies treated with intensive chemotherapy or radiotherapy, or both, with or without haematopoietic stem cell support
Radford M, Estcourt L, Sirotich E, Pitre T, Britto J, Watson M, Brunskill S, Fergusson D, Dorée C, Arnold D. Restrictive versus liberal red blood cell transfusion strategies for people with haematological malignancies treated with intensive chemotherapy or radiotherapy, or both, with or without haematopoietic stem cell support. Cochrane Database Of Systematic Reviews 2024, 2024: cd011305. PMID: 38780066, PMCID: PMC11112982, DOI: 10.1002/14651858.cd011305.pub3.Peer-Reviewed Original ResearchConceptsRed blood cell transfusion strategyHaematopoietic stem cell transplantationLiberal transfusion strategyClinically significant bleedingRestrictive transfusion strategyNon-randomised studiesTransfusion strategyIntensive chemotherapyRandomised controlled trialsRed blood cellsSignificant bleedingAll-cause mortalityLength of hospital admissionAcute leukemiaHaematological malignanciesRisk of clinically significant bleedingHaematopoietic stem cell supportProspective non-randomised studyRisk ratioControlled trialsQuality of lifeStem cell supportStem cell transplantationMalignant haematological disordersCochrane Central Register of Controlled TrialsAssessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials
Blette B, Halpern S, Li F, Harhay M. Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials. Statistical Methods In Medical Research 2024, 33: 909-927. PMID: 38567439, PMCID: PMC11041086, DOI: 10.1177/09622802241242323.Peer-Reviewed Original ResearchConceptsMultilevel multiple imputationHeterogeneous treatment effectsCluster randomized trialPotential effect modifiersMultiple imputationAssess treatment effect heterogeneityEffect modifiersTreatment effect heterogeneityComplete-case analysisMissingness mechanismIntracluster correlationSimulation studyUnder-coverageRandomized trialsEffect heterogeneityHealth StudyTreatment effectsContinuous outcomesClinical practiceImputationModel specificationMissingnessData methodsModified dataTrialsQuantifying possible bias in clinical and epidemiological studies with quantitative bias analysis: common approaches and limitations
Brown J, Hunnicutt J, Ali M, Bhaskaran K, Cole A, Langan S, Nitsch D, Rentsch C, Galwey N, Wing K, Douglas I. Quantifying possible bias in clinical and epidemiological studies with quantitative bias analysis: common approaches and limitations. The BMJ 2024, 385: e076365. PMID: 38565248, DOI: 10.1136/bmj-2023-076365.Peer-Reviewed Original Research
2023
Automation Bias and Assistive AI
Khera R, Simon M, Ross J. Automation Bias and Assistive AI. JAMA 2023, 330: 2255-2257. PMID: 38112824, DOI: 10.1001/jama.2023.22557.Commentaries, Editorials and LettersDesigning individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations
Wang X, Turner E, Li F. Designing individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations. Statistics In Medicine 2023, 43: 358-378. PMID: 38009329, PMCID: PMC10939061, DOI: 10.1002/sim.9966.Peer-Reviewed Original ResearchConceptsSample size proceduresConstant treatment effectCorrelation structureSize proceduresMarginal mean modelClosed-form sample size formulaCorrelation parametersSandwich variance estimatorGroup treatment trialsEquation approachExchangeable correlation structureSample size formulaBinary outcomesVariance estimatorEmpirical powerLinear timeMean modelCorrelation matrixDifferent correlation parametersEstimating EquationsSize formulaEquationsSample size calculationDifferent assumptionsProper sample size calculationSelf-Schemas and Information Processing Biases as Mechanisms Underlying Sexual Orientation Disparities in Depressive Symptoms: Results From a Longitudinal, Population-Based Study
Bränström R, Pachankis J, Jin J, Klein D, Hatzenbuehler M. Self-Schemas and Information Processing Biases as Mechanisms Underlying Sexual Orientation Disparities in Depressive Symptoms: Results From a Longitudinal, Population-Based Study. Journal Of Psychopathology And Clinical Science 2023, 132: 681-693. PMID: 37326561, PMCID: PMC10524885, DOI: 10.1037/abn0000823.Peer-Reviewed Original ResearchConceptsInformation processing biasesProcessing biasesNegative wordsSelf-schemaDepressive symptomsCognitive risk factorsSelf-report measuresSexual minoritiesSexual minority individualsSexual orientation disparitiesOrientation disparitiesPotential intervention targetsCognitive mechanismsBehavioral tasksCross-sectional designIntervention targetsMinority individualsProspective associationsWordsYoung adultsHeterosexual individualsReaction timeWave 2BiasesNonprobability samplePotential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.
Boyd A, Gonzalez-Guarda R, Lawrence K, Patil C, Ezenwa M, O'Brien E, Paek H, Braciszewski J, Adeyemi O, Cuthel A, Darby J, Zigler C, Ho P, Faurot K, Staman K, Leigh J, Dailey D, Cheville A, Del Fiol G, Knisely M, Grudzen C, Marsolo K, Richesson R, Schlaeger J. Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory. Journal Of The American Medical Informatics Association 2023, 30: 1561-1566. PMID: 37364017, PMCID: PMC10436149, DOI: 10.1093/jamia/ocad115.Peer-Reviewed Original ResearchConceptsElectronic health record dataPragmatic clinical trialsHealth record dataPopulation health problemElectronic health record systemsClinical trialsEHR-derived dataHealth record systemsHealth problemsSocial determinantsHealth equityRecord dataVulnerable populationsEHR dataHealthcare systemNational InstituteRecord systemLack of generalizabilityHealthDifferent subsetsGeneralizable researchEPCTsPopulationPotential biasTrialsRelationships between cognitive biases, decision-making, and delusions
Sheffield J, Smith R, Suthaharan P, Leptourgos P, Corlett P. Relationships between cognitive biases, decision-making, and delusions. Scientific Reports 2023, 13: 9485. PMID: 37301915, PMCID: PMC10257713, DOI: 10.1038/s41598-023-36526-1.Peer-Reviewed Original ResearchConceptsDelusional ideationDelusional thinkingCognitive biasesDistinct cognitive processesSelf-reported dataCognitive processesProbabilistic reversalUnique varianceComputational mechanismsEvidence integrationPsychosis spectrumMultiple measuresProportion of varianceParanoiaRandom explorationIdeationThinkingTaskBiasesMeasuresVarianceDelusionsJTCIndependent studiesRelationshipCorrecting for Bias Due to Mismeasured Exposure History in Longitudinal Studies with Continuous Outcomes
Cai J, Zhang N, Zhou X, Spiegelman D, Wang M. Correcting for Bias Due to Mismeasured Exposure History in Longitudinal Studies with Continuous Outcomes. Biometrics 2023, 79: 3739-3751. PMID: 37222518, PMCID: PMC11214728, DOI: 10.1111/biom.13877.Peer-Reviewed Original ResearchEquity and bias in electronic health records data
Boyd A, Gonzalez-Guarda R, Lawrence K, Patil C, Ezenwa M, O'Brien E, Paek H, Braciszewski J, Adeyemi O, Cuthel A, Darby J, Zigler C, Ho P, Faurot K, Staman K, Leigh J, Dailey D, Cheville A, Del Fiol G, Knisely M, Marsolo K, Richesson R, Schlaeger J. Equity and bias in electronic health records data. Contemporary Clinical Trials 2023, 130: 107238. PMID: 37225122, PMCID: PMC10330606, DOI: 10.1016/j.cct.2023.107238.Peer-Reviewed Original ResearchORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals
Meng C, Ryan M, Rathouz P, Turner E, Preisser J, Li F. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals. Computer Methods And Programs In Biomedicine 2023, 237: 107567. PMID: 37207384, DOI: 10.1016/j.cmpb.2023.107567.Peer-Reviewed Original ResearchConceptsOrdinal outcomesSandwich estimatorR packageSimulation studyCorrelated ordinal dataFinite sample biasesNumber of clustersCovariance estimationMarginal modelsEquationsParameter estimatesOrdinal responsesAssociation parametersCluster associationsBias correctionOrdinal dataEstimatorEstimating EquationsNominal levelMarginal meansResidualsEstimationPairwise odds ratiosAssociation modelGEE modelNonsystematic Reporting Biases of the SARS-CoV-2 Variant Mu Could Impact Our Understanding of the Epidemiological Dynamics of Emerging Variants
Petrone M, Lucas C, Menasche B, Breban M, Yildirim I, Campbell M, Omer S, Holmes E, Ko A, Grubaugh N, Iwasaki A, Wilen C, Vogels C, Fauver J. Nonsystematic Reporting Biases of the SARS-CoV-2 Variant Mu Could Impact Our Understanding of the Epidemiological Dynamics of Emerging Variants. Genome Biology And Evolution 2023, 15: evad052. PMID: 36974986, PMCID: PMC10113931, DOI: 10.1093/gbe/evad052.Peer-Reviewed Original ResearchCognitive bias in pathology, as exemplified in dermatopathology
Ko C, Glusac E. Cognitive bias in pathology, as exemplified in dermatopathology. Human Pathology 2023, 140: 267-275. PMID: 36906184, DOI: 10.1016/j.humpath.2023.03.003.Peer-Reviewed Original Research
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