2024
Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials – A case study
Esserman D, Greene E, Latham N, Kane M, Lu C, Peduzzi P, Gill T, Ganz D. Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials – A case study. Contemporary Clinical Trials 2024, 142: 107572. PMID: 38740298, DOI: 10.1016/j.cct.2024.107572.Peer-Reviewed Original ResearchElectronic health record dataElectronic health recordsOutcome ascertainmentDevelop Confidence in EldersElectronic health record platformsClinical sitesPrimary care practicesHealth record dataMulti-site trialMulti-site clinical trialCare practicesHealth recordsAssess readinessAcute clinical outcomesHealthcare systemRecord dataClinical trialsReduce injuriesData qualityData comprehensionChecklistStudy dataClinical trial sitesVariable data qualityAscertainmentA Bayesian platform trial design with hybrid control based on multisource exchangeability modelling
Wei W, Blaha O, Esserman D, Zelterman D, Kane M, Liu R, Lin J. A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling. Statistics In Medicine 2024, 43: 2439-2451. PMID: 38594809, PMCID: PMC11325877, DOI: 10.1002/sim.10077.Peer-Reviewed Original ResearchValidation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data
Ganz D, Esserman D, Latham N, Kane M, Min L, Gill T, Reuben D, Peduzzi P, Greene E. Validation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data. The Journals Of Gerontology Series A 2024, 79: glae096. PMID: 38566617, PMCID: PMC11167485, DOI: 10.1093/gerona/glae096.Peer-Reviewed Original ResearchFee-for-serviceFall injuriesMedicare AdvantageMedicare dataTrial armsHealthcare systemDevelop Confidence in EldersArea under the receiver operating characteristic curveMedicare fee-for-serviceStratified resultsMedicareReduce injuriesMedical attentionObservational studyStrideReceiver operating characteristic curveCalendar monthMA dataInjuryData sourcesHealthcareArmReference standardTrialsWindow size
2023
A compressed large language model embedding dataset of ICD 10 CM descriptions
Kane M, King C, Esserman D, Latham N, Greene E, Ganz D. A compressed large language model embedding dataset of ICD 10 CM descriptions. BMC Bioinformatics 2023, 24: 482. PMID: 38105180, PMCID: PMC10726612, DOI: 10.1186/s12859-023-05597-2.Peer-Reviewed Original Research
2022
Regression methods for the appearances of extremes in climate data
Yu C, Blaha O, Kane M, Wei W, Esserman D, Zelterman D. Regression methods for the appearances of extremes in climate data. Environmetrics 2022, 33 DOI: 10.1002/env.2764.Peer-Reviewed Original ResearchBayesian local exchangeability design for phase II basket trials
Liu Y, Kane M, Esserman D, Blaha O, Zelterman D, Wei W. Bayesian local exchangeability design for phase II basket trials. Statistics In Medicine 2022, 41: 4367-4384. PMID: 35777367, PMCID: PMC10279458, DOI: 10.1002/sim.9514.Peer-Reviewed Original ResearchBayesian basket trial design with false-discovery rate control.
Zabor EC, Kane MJ, Roychoudhury S, Nie L, Hobbs BP. Bayesian basket trial design with false-discovery rate control. Clinical Trials (London, England) 2022, 19: 297-306. PMID: 35128970, DOI: 10.1177/17407745211073624.Peer-Reviewed Original Research
2021
Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach
Li X, Kane M, Zhang Y, Sun W, Song Y, Dong S, Lin Q, Zhu Q, Jiang F, Zhao H. Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach. Journal Of Medical Internet Research 2021, 23: e18403. PMID: 34647895, PMCID: PMC8554674, DOI: 10.2196/18403.Peer-Reviewed Original ResearchConceptsRhythm formationPDMS-2Motor developmentCircadian rhythm analysisPeabody Developmental Motor Scales-Second EditionFisher testChildhood motor developmentEarly childhood motor developmentWearable device dataHealthy infantsRhythm analysisClinical studiesLinear regression analysisGross motorMonthsTime pointsAge 6Bonferroni correctionEarly childhoodRhythm developmentRegression analysisOne dayAssociationDaily rhythmsActiwatch dataUnified exact design with early stopping rules for single arm clinical trials with multiple endpoints
Wei W, Esserman D, Kane M, Zelterman D. Unified exact design with early stopping rules for single arm clinical trials with multiple endpoints. Statistical Methods In Medical Research 2021, 30: 1575-1588. PMID: 34159859, PMCID: PMC8959087, DOI: 10.1177/09622802211013062.Peer-Reviewed Original Research
2020
Two‐stage randomized trial design for testing treatment, preference, and self‐selection effects for count outcomes
Shi Y, Cameron B, Gu X, Kane M, Peduzzi P, Esserman DA. Two‐stage randomized trial design for testing treatment, preference, and self‐selection effects for count outcomes. Statistics In Medicine 2020, 39: 3653-3683. PMID: 32875582, DOI: 10.1002/sim.8686.Peer-Reviewed Original ResearchConceptsTrial designPatient preferencesPatient-centered treatment strategiesTreatment effectsTraditional clinical trial designClinical trial designEnd of lifeUse of antimicrobialsTreatment strategiesHealthcare providersPatient psychologyTesting treatmentsTwo-stage designOutcomesParticular treatmentTreatmentBinary outcomesCount outcomesTrialspreference : An R Package for Two-Stage Clinical Trial Design Accounting for Patient Preference
Cameron B, Kane M, Esserman D. preference : An R Package for Two-Stage Clinical Trial Design Accounting for Patient Preference. Journal Of Statistical Software 2020, 94 DOI: 10.18637/jss.v094.c02.Peer-Reviewed Original Research
2019
Basket Designs: Statistical Considerations for Oncology Trials.
Kaizer AM, Koopmeiners JS, Kane MJ, Roychoudhury S, Hong DS, Hobbs BP. Basket Designs: Statistical Considerations for Oncology Trials. JCO Precision Oncology 2019, 3: 1-9. PMID: 35100726, DOI: 10.1200/PO.19.00194.Peer-Reviewed Original ResearchA two‐stage phase II clinical trial design with nested criteria for early stopping and efficacy
DeVeaux M, Kane M, Wei W, Zelterman D. A two‐stage phase II clinical trial design with nested criteria for early stopping and efficacy. Pharmaceutical Statistics 2019, 18: 700-713. PMID: 31507079, PMCID: PMC6996237, DOI: 10.1002/pst.1965.Peer-Reviewed Original Research
2018
Factors Associated With Cancer Disparities Among Low-, Medium-, and High-Income US Counties
O’Connor J, Sedghi T, Dhodapkar M, Kane MJ, Gross CP. Factors Associated With Cancer Disparities Among Low-, Medium-, and High-Income US Counties. JAMA Network Open 2018, 1: e183146. PMID: 30646225, PMCID: PMC6324449, DOI: 10.1001/jamanetworkopen.2018.3146.Peer-Reviewed Original ResearchConceptsCancer death ratesDeath rateHigh-income countiesCancer disparitiesPossible mediatorsAge-standardized cancer death ratesLow-income countiesCross-sectional studyClinical care factorsIncome-related disparitiesNon-Hispanic blacksHealth risk behaviorsLow-quality careUS countiesPhysical inactivityCare factorsMAIN OUTCOMEFair healthDeath recordsMedian household incomeMedian incomeHealth StatisticsRisk behaviorsHealth policyCounty income levelsClinical Trial Design Using A Stopped Negative Binomial Distribution.
DeVeaux M, Kane MJ, Zelterman D. Clinical Trial Design Using A Stopped Negative Binomial Distribution. Statistics And Its Interface 2018, 11: 699-707. PMID: 30655933, PMCID: PMC6333309, DOI: 10.4310/sii.2018.v11.n4.a13.Peer-Reviewed Original ResearchLung Nodule Detection via Deep Reinforcement Learning
Ali I, Hart GR, Gunabushanam G, Liang Y, Muhammad W, Nartowt B, Kane M, Ma X, Deng J. Lung Nodule Detection via Deep Reinforcement Learning. Frontiers In Oncology 2018, 8: 108. PMID: 29713615, PMCID: PMC5912002, DOI: 10.3389/fonc.2018.00108.Peer-Reviewed Original Research
2016
Modeling risk of occupational zoonotic influenza infection in swine workers
Paccha B, Jones RM, Gibbs S, Kane MJ, Torremorell M, Neira-Ramirez V, Rabinowitz PM. Modeling risk of occupational zoonotic influenza infection in swine workers. Journal Of Occupational And Environmental Hygiene 2016, 13: 577-587. PMID: 26950677, DOI: 10.1080/15459624.2016.1159688.Peer-Reviewed Original ResearchConceptsRisk of infectionZoonotic influenza infectionSwine workersInfluenza infectionInfluenza strainsN95 respiratorsInfectivity of IAVNovel influenza strainsOutbreak of influenzaTransmission of influenzaLow-dose exposureHuman health care settingsHealth care settingsUse of respiratorsPrevious infectionPolymerase chain reactionIAV exposureCare settingsPandemic potentialPreventive programsPartial immunityZoonotic infectionInfectionZoonotic transmissionExposure of workers
2015
Global Burden of Leptospirosis: Estimated in Terms of Disability Adjusted Life Years
Torgerson PR, Hagan JE, Costa F, Calcagno J, Kane M, Martinez-Silveira MS, Goris MG, Stein C, Ko AI, Abela-Ridder B. Global Burden of Leptospirosis: Estimated in Terms of Disability Adjusted Life Years. PLOS Neglected Tropical Diseases 2015, 9: e0004122. PMID: 26431366, PMCID: PMC4591975, DOI: 10.1371/journal.pntd.0004122.Peer-Reviewed Original ResearchConceptsDisability Adjusted Life YearsGlobal burdenAdjusted Life YearsLife yearsResource-poor tropical countriesHighest burden estimatesSum of YLLsBurden of leptospirosisSignificant health burdenLife-threatening diseaseYears of lifeDiverse epidemiological settingsGBD 2010Febrile illnessDisease burdenHealth burdenBurden estimatesLife LostMortality rateUrban slum dwellersDisease estimatesEpidemiological settingsTotal burdenLikely sequelaeLeptospirosisGlobal Morbidity and Mortality of Leptospirosis: A Systematic Review
Costa F, Hagan JE, Calcagno J, Kane M, Torgerson P, Martinez-Silveira MS, Stein C, Abela-Ridder B, Ko AI. Global Morbidity and Mortality of Leptospirosis: A Systematic Review. PLOS Neglected Tropical Diseases 2015, 9: e0003898. PMID: 26379143, PMCID: PMC4574773, DOI: 10.1371/journal.pntd.0003898.Peer-Reviewed Original ResearchConceptsCase fatality ratioDisease morbidityFatality ratioSystematic reviewMortality of leptospirosisPulmonary hemorrhage syndromeBurden of leptospirosisLife-threatening diseaseDiverse epidemiological settingsResource-poor countriesNumber of deathsHigh morbidityGlobal morbidityGBD regionsGlobal burdenMorbidityZoonotic causesHaemorrhagic feverMortality studyUrban slum dwellersImportant causeDisease statusEpidemiological settingsDisease incidenceWHO regions
2014
Proximity to Natural Gas Wells and Reported Health Status: Results of a Household Survey in Washington County, Pennsylvania
Rabinowitz PM, Slizovskiy IB, Lamers V, Trufan SJ, Holford TR, Dziura JD, Peduzzi PN, Kane MJ, Reif JS, Weiss TR, Stowe MH. Proximity to Natural Gas Wells and Reported Health Status: Results of a Household Survey in Washington County, Pennsylvania. Environmental Health Perspectives 2014, 123: 21-26. PMID: 25204871, PMCID: PMC4286272, DOI: 10.1289/ehp.1307732.Peer-Reviewed Original ResearchConceptsHealth symptomsUpper respiratory symptomsPublic health impactRespiratory symptomsNeurological symptomsSymptom SurveyRespiratory conditionsSkin conditionsHypothesis generatingHealth statusSymptomsFurther studiesGastrointestinal conditionsHealth impactsHousehold educationPrevalenceWashington CountyHousehold proximityPersonsSmokingWater exposureHousehold surveyWork type