2025
Moderate stability of risk and ambiguity attitudes across quantitative and qualitative decisions
Dan O, Xu C, Jia R, Wertheimer E, Chawla M, Fuhrmann Alpert G, Fried T, Levy I. Moderate stability of risk and ambiguity attitudes across quantitative and qualitative decisions. Scientific Reports 2025, 15: 3119. PMID: 39856239, PMCID: PMC11760528, DOI: 10.1038/s41598-025-87644-x.Peer-Reviewed Original ResearchConceptsHypothetical monetary gainsMonetary lotteriesUncertainty attitudesAmbiguity attitudesBinary choiceIndividual preferencesLow outcomesDecision-makingMonetary gainsOutcome magnitudeDecision-making taskSuccess chancesStabilization of riskDecisionUncertaintyChoiceLotteryModerate stabilityEveryday choicesAttitude consistencyQualitative decisionsQualitative outcomesLevel of improvementPreferences
2024
Net Monetary Benefit Lines Augmented with Value-of-Information Measures to Present the Results of Economic Evaluations under Uncertainty
Yaesoubi R, Kunst N. Net Monetary Benefit Lines Augmented with Value-of-Information Measures to Present the Results of Economic Evaluations under Uncertainty. Medical Decision Making 2024, 44: 770-786. PMID: 39056310, PMCID: PMC12181631, DOI: 10.1177/0272989x241262343.Peer-Reviewed Original ResearchResults of cost-effectiveness analysesNet monetary benefitCost-effectiveness planeCost-effectiveness analysisMonetary benefitsDecision uncertaintyDecision problemEffect estimatesHypothetical decision problemResults of economic evaluationsValue-of-informationDecision makersCost-effectiveness ratioEconomic evaluation studiesCorrelated costsHigh varianceLevel of uncertaintyParameter uncertaintiesEconomic evaluationMagnitude of parametersMakersDecisionUncertaintyCostMonetary
2023
Individualized Prediction of Outcomes of Hematopoietic Cell Transplantation for Sickle Cell Disease: A Machine Learning Approach
Subramaniam R, Kane M, Krishnamurti L. Individualized Prediction of Outcomes of Hematopoietic Cell Transplantation for Sickle Cell Disease: A Machine Learning Approach. Blood 2023, 142: 1058. DOI: 10.1182/blood-2023-178352.Peer-Reviewed Original ResearchIncorporation of uncertaintyModel selectionModel versatilityQuantifying uncertaintyML methodsHematopoietic cell transplantationSupervised random forest modelSuch modelsSickle cell diseaseThreshold δML modelsModel confidenceAverage probabilityMachine learning methodsIncorrect resultsPredictive model performancePositive predictive valueUnknown dataAcceptable AUCPredictive modelUncertaintyVariables of interestHigh accuracyArithmetic meanCell transplantationVisualization and assessment of model selection uncertainty
Qin Y, Wang L, Li Y, Li R. Visualization and assessment of model selection uncertainty. Computational Statistics & Data Analysis 2023, 178: 107598. DOI: 10.1016/j.csda.2022.107598.Peer-Reviewed Original Research
2022
A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth
Comess S, Chang HH, Warren JL. A Bayesian framework for incorporating exposure uncertainty into health analyses with application to air pollution and stillbirth. Biostatistics 2022, 25: 20-39. PMID: 35984351, PMCID: PMC10724312, DOI: 10.1093/biostatistics/kxac034.Peer-Reviewed Original ResearchConceptsFull conditional distributionsEfficient model fittingStatistical modeling approachDensity estimation approachBayesian settingKernel density estimation approachPosterior outputBayesian frameworkConditional distributionModel fittingEstimation approachAccurate inferenceKDE approachModeling approachComparison metricsExposure uncertaintyUncertaintySecond stageApproachFittingInferencePredictionSimulationsModel comparison metricsFirst stageMethods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison
Wolff H, Qendri V, Kunst N, Alarid-Escudero F, Coupé V. Methods for Communicating the Impact of Parameter Uncertainty in a Multiple-Strategies Cost-Effectiveness Comparison. Medical Decision Making 2022, 42: 956-968. PMID: 35587181, PMCID: PMC9452448, DOI: 10.1177/0272989x221100112.Peer-Reviewed Original ResearchConceptsCost-effectiveness acceptability curvesCost-effectiveness acceptability frontierCost-effectiveness analysisImpact of uncertaintyAcceptability curvesDecision analytic modelWrong decisionsCost-effectiveness comparisonBenefit curvePolicy makersHeat mapDecision makersPotential lossIntegration of methodsDecisionsIntegration of informationDecision problemMakersOverview of methodsUncertaintyHealth policyType of informationCourse of actionRelevant informationUse toolEmerging Therapies for COVID-19: The Value of Information From More Clinical Trials
Dijk SW, Krijkamp EM, Kunst N, Gross CP, Wong JB, Hunink MGM. Emerging Therapies for COVID-19: The Value of Information From More Clinical Trials. Value In Health 2022, 25: 1268-1280. PMID: 35490085, PMCID: PMC9045876, DOI: 10.1016/j.jval.2022.03.016.Peer-Reviewed Original ResearchConceptsCohort state-transition modelUS healthcare perspectiveValue of informationFace of uncertaintyQALY willingnessPolicy makersLifetime horizonHealthcare perspectiveState transition modelInformation analysisImmediate approvalModel outcomesImplementation decisionsOptimal momentWillingnessPolicyMakersMost valueCOVID-19DecisionsCostHorizonFurther researchAdditional evidenceUncertainty
2020
Methods to Account for Uncertainty in Latent Class Assignments When Using Latent Classes as Predictors in Regression Models, with Application to Acculturation Strategy Measures.
Elliott M, Zhao Z, Mukherjee B, Kanaya A, Needham B. Methods to Account for Uncertainty in Latent Class Assignments When Using Latent Classes as Predictors in Regression Models, with Application to Acculturation Strategy Measures. Epidemiology 2020, 31: 194-204. PMID: 31809338, PMCID: PMC7480960, DOI: 10.1097/ede.0000000000001139.Peer-Reviewed Original ResearchConceptsMeasurement error modelJoint modelRegression parametersLatent classesLikelihood-basedLatent class modelSimulation studyClass modelTwo-stage modelClassError modelPrimary interestAcculturation behaviorsMeasurement errorSouth Asian immigrantsLatent class analysisAsian immigrantsTrue classUncertaintyClass analysisEstimationStrategy measures
2019
Information Inundation on Platforms and Implications
Allon G, Drakopoulos K, Manshadi V. Information Inundation on Platforms and Implications. 2019, 555-556. DOI: 10.1145/3328526.3329611.Peer-Reviewed Original Research
2018
Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models
Zimmer C, Leuba SI, Cohen T, Yaesoubi R. Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models. Statistical Methods In Medical Research 2018, 28: 3591-3608. PMID: 30428780, PMCID: PMC6517086, DOI: 10.1177/0962280218805780.Peer-Reviewed Original ResearchConceptsFilter degeneracyParameter estimatesPosterior distributionStochastic transmission-dynamic modelParameter posterior distributionsEpidemic compartmental modelKey epidemic parametersStochastic compartmental modelStochastic systemsPrediction intervalsCompartmental modelMultiple shootingArt calibration methodsEpidemic parametersDegeneracyDynamic modelInfluenza modelMSS approachLong-term predictionTransmission dynamic modelSimulation experimentsCalibration methodUncertaintyEstimatesCompetitive performanceUncertainty in forecasts of long-run economic growth
Christensen P, Gillingham K, Nordhaus W. Uncertainty in forecasts of long-run economic growth. Proceedings Of The National Academy Of Sciences Of The United States Of America 2018, 115: 5409-5414. PMID: 29760089, PMCID: PMC6003472, DOI: 10.1073/pnas.1713628115.Peer-Reviewed Original ResearchEconomic growthPer capita economic growth rateForecasts of economic growthLong-run economic growthPer capita gross domestic productLong-run forecastsEconomic growth rateGross domestic productLong-running projectSocial insurance programsGlobal growth rateEconometric approachLong-runDomestic productGrowth rateInsurance programClimate change outcomesPolicy decisionsForecastingCompare estimatesExpert surveyUncertaintyEconomyUnited StatesPolicy
2013
Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy
Gilbert JA, Meyers LA, Galvani AP, Townsend JP. Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy. Epidemics 2013, 6: 37-45. PMID: 24593920, PMCID: PMC4316830, DOI: 10.1016/j.epidem.2013.11.002.Peer-Reviewed Original ResearchConceptsProbabilistic uncertainty analysisParameter uncertaintiesTypical sensitivity analysisUncertainty analysisDynamic modelDynamic infectious disease modelsSensitivity analysisMathematical modelingEpidemiological modelingInfectious disease modelIntervention policiesPublic health intervention policiesGlobal uncertaintyPolicy makersModel predictionsEpidemiological outcomesQuantitative predictionsProbability of successPoint estimatesPolicyUncertaintyLevel of vaccinationRelative importanceModelingHealth policyOptimal targeting of seasonal influenza vaccination toward younger ages is robust to parameter uncertainty
Mbah M, Medlock J, Meyers LA, Galvani AP, Townsend JP. Optimal targeting of seasonal influenza vaccination toward younger ages is robust to parameter uncertainty. Vaccine 2013, 31: 3079-3089. PMID: 23684837, PMCID: PMC3764605, DOI: 10.1016/j.vaccine.2013.04.052.Peer-Reviewed Original ResearchConceptsOptimal vaccine allocationParameter uncertaintiesMathematical modelProbability distributionConsideration of uncertaintiesOutcome measuresVaccine allocationHuman population immunitySeasonal influenza vaccinationControl of influenzaUncertainty analysisFace of uncertaintyInfluenza vaccinationVaccine efficacySeasonal influenzaPopulation immunityUncertaintyEpidemiological dataHigh riskYounger ageYoung adultsEpidemiological parametersOptimal agePrevious recommendationsVaccine
2012
Violence Risk Assessment in Clinical Settings: Being Sure about Being Sure
Buchanan A. Violence Risk Assessment in Clinical Settings: Being Sure about Being Sure. Behavioral Sciences & The Law 2012, 31: 74-80. PMID: 23281104, DOI: 10.1002/bsl.2045.Peer-Reviewed Original ResearchGlobal and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010
Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Abraham J, Adair T, Aggarwal R, Ahn SY, AlMazroa M, Alvarado M, Anderson H, Anderson L, Andrews K, Atkinson C, Baddour L, Barker-Collo S, Bartels D, Bell M, Benjamin E, Bennett D, Bhalla K, Bikbov B, Bin Abdulhak A, Birbeck G, Blyth F, Bolliger I, Boufous S, Bucello C, Burch M, Burney P, Carapetis J, Chen H, Chou D, Chugh S, Coffeng L, Colan S, Colquhoun S, Colson K, Condon J, Connor M, Cooper L, Corriere M, Cortinovis M, de Vaccaro K, Couser W, Cowie B, Criqui M, Cross M, Dabhadkar K, Dahodwala N, De Leo D, Degenhardt L, Delossantos A, Denenberg J, Jarlais D, Dharmaratne S, Dorsey E, Driscoll T, Duber H, Ebel B, Erwin P, Espindola P, Ezzati M, Feigin V, Flaxman A, Forouzanfar M, Fowkes F, Franklin R, Fransen M, Freeman M, Gabriel S, Gakidou E, Gaspari F, Gillum R, Gonzalez-Medina D, Halasa Y, Haring D, Harrison J, Havmoeller R, Hay R, Hoen B, Hotez P, Hoy D, Jacobsen K, James S, Jasrasaria R, Jayaraman S, Johns N, Karthikeyan G, Kassebaum N, Keren A, Khoo J, Knowlton L, Kobusingye O, Koranteng A, Krishnamurthi R, Lipnick M, Lipshultz S, Ohno S, Mabweijano J, MacIntyre M, Mallinger L, March L, Marks G, Marks R, Matsumori A, Matzopoulos R, Mayosi B, McAnulty J, McDermott M, McGrath J, Memish Z, Mensah G, Merriman T, Michaud C, Miller M, Miller T, Mock C, Mocumbi A, Mokdad A, Moran A, Mulholland K, Nair M, Naldi L, Narayan K, Nasseri K, Norman P, O'Donnell M, Omer S, Ortblad K, Osborne R, Ozgediz D, Pahari B, Pandian J, Rivero A, Padilla R, Perez-Ruiz F, Perico N, Phillips D, Pierce K, Pope C, Porrini E, Pourmalek F, Raju M, Ranganathan D, Rehm J, Rein D, Remuzzi G, Rivara F, Roberts T, De León F, Rosenfeld L, Rushton L, Sacco R, Salomon J, Sampson U, Sanman E, Schwebel D, Segui-Gomez M, Shepard D, Singh D, Singleton J, Sliwa K, Smith E, Steer A, Taylor J, Thomas B, Tleyjeh I, Towbin J, Truelsen T, Undurraga E, Venketasubramanian N, Vijayakumar L, Vos T, Wagner G, Wang M, Wang W, Watt K, Weinstock M, Weintraub R, Wilkinson J, Woolf A, Wulf S, Yeh P, Yip P, Zabetian A, Zheng Z, Lopez A, Murray C. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet 2012, 380: 2095-2128. PMID: 23245604, PMCID: PMC10790329, DOI: 10.1016/s0140-6736(12)61728-0.Peer-Reviewed Original ResearchConceptsUncertainty intervalsParameter estimation uncertaintyStatistical modelUncertainty distributionEstimation uncertaintyStochastic variationDifferent modelling strategiesNegative binomial modelBinomial modelPrediction errorModelling strategyModel approachDifferent modelsEnsemble model approachModel specificationModel ensembleCrucial inputUncertaintyComponent modelLarge setModel performanceModelRisk Factors Study 2010Plausible covariatesHigher-level causesModel Parameter Estimation and Uncertainty Analysis
Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD. Model Parameter Estimation and Uncertainty Analysis. Medical Decision Making 2012, 32: 722-732. PMID: 22990087, DOI: 10.1177/0272989x12458348.Peer-Reviewed Original ResearchConceptsDecision uncertaintyCost-effectiveness acceptability curvesHealth care resource allocationEconomic evidence basePoint estimatesReporting of uncertaintyAcceptability curvesLevel of uncertaintyPolicy perspectivePerfect informationPrecise point estimatesPresentational techniquesDecision makersDecision modelingResource allocationParameter uncertaintiesBetter decisionsStructural uncertaintyDecisionsModel purposeUncertaintyInformation analysisModel-based analysisExtensive recommendationsStochastic uncertaintyModel Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6
Briggs AH, Weinstein MC, Fenwick EA, Karnon J, Sculpher MJ, Paltiel AD, Force I. Model Parameter Estimation and Uncertainty: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6. Value In Health 2012, 15: 835-842. PMID: 22999133, DOI: 10.1016/j.jval.2012.04.014.Peer-Reviewed Original ResearchConceptsDecision uncertaintyCost-effectiveness acceptability curvesHealth care resource allocationEconomic evidence basePoint estimatesReporting of uncertaintyAcceptability curvesLevel of uncertaintyPolicy perspectivePerfect informationPrecise point estimatesPresentational techniquesDecision makersDecision modelingResource allocationParameter uncertaintiesBetter decisionsStructural uncertaintyDecisionsModel purposeUncertaintyInformation analysisModel-based analysisExtensive recommendationsStochastic uncertainty
2009
Nature plays with dice – terrorists do not: Allocating resources to counter strategic versus probabilistic risks
Golany B, Kaplan E, Marmur A, Rothblum U. Nature plays with dice – terrorists do not: Allocating resources to counter strategic versus probabilistic risks. European Journal Of Operational Research 2009, 192: 198-208. DOI: 10.1016/j.ejor.2007.09.001.Peer-Reviewed Original ResearchOptimal policyStrategic riskStrategic uncertaintyPotential damage levelsBest policyImpact functionProbabilistic riskInvestment of resourcesPolicyOptimization problemSolution coincidesProbabilistic uncertaintyInterested partiesAllocation schemeInvestmentUncertaintyResourcesHigh impactRiskPartiesImpactProblem
2008
Ice as a water-equivalent solid medium for brachytherapy dosimetric measurements
Song H, Chen Z, Yue N, Wu Q, Yin FF. Ice as a water-equivalent solid medium for brachytherapy dosimetric measurements. Radiation And Environmental Biophysics 2008, 48: 145-151. PMID: 19066926, DOI: 10.1007/s00411-008-0205-9.Peer-Reviewed Original ResearchConceptsSolid phantomWater conversion factorNon-negligible uncertaintiesMono-energetic photonsPrecise positioningLow-energy photon-emitting brachytherapy sourcesChemical compositionBrachytherapy sourcesPositioning advantageDosimetric measurementsExperimental determinationNecessary precisionDosimetric characteristicsPhantomIceBrachytherapy dosimetryPositioningConversion factorPhotonsUncertaintyKeVA Bayesian hierarchical model for the estimation of two incomplete surveillance data sets
Buenconsejo J, Fish D, Childs JE, Holford TR. A Bayesian hierarchical model for the estimation of two incomplete surveillance data sets. Statistics In Medicine 2008, 27: 3269-3285. PMID: 18314934, DOI: 10.1002/sim.3190.Peer-Reviewed Original ResearchConceptsBayesian hierarchical modelMarkov chain Monte Carlo simulation techniquesMonte Carlo simulation techniqueHierarchical modelModel uncertaintyUse of covariatesBayesian frameworkSimulation techniquesModel-based approachData setsSurveillance datasetModelSuch dataPublic health impactSpatial distributionPublic health officialsInferenceEstimationSpatial variationTreatable diseaseChronic diseasesUncertaintyHigh riskDisease riskDisease control
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