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 ResearchMeSH KeywordsBiasConfounding Factors, EpidemiologicData Interpretation, StatisticalHumansPharmacoepidemiologyResearch DesignSelection BiasConceptsQuantitative bias analysisBias analysisValidity of study findingsPharmacoepidemiological studiesRobustness of studiesEffects of medicationStudy designEffect estimatesResidual biasStudy findingsSelection biasConfoundingEstimated effectsPotential biasPharmacoepidemiologyBiasMedicationCore conceptsStudyMeasurement error
2023
Prognostic Factors of COVID‐19: An Umbrella Review Endorsed by the International Society for Pharmacoepidemiology
Sarri G, Liu W, Zabotka L, Freitag A, Claire R, Wangge G, Elvidge J, Dawoud D, Bennett D, Wen X, Li X, Rentsch C, Uddin J, Ali M, Gokhale M, Déruaz‐Luyet A, Moga D, Guo J, Zullo A, Patorno E, Lin K. Prognostic Factors of COVID‐19: An Umbrella Review Endorsed by the International Society for Pharmacoepidemiology. Clinical Pharmacology & Therapeutics 2023, 114: 604-613. PMID: 37342987, DOI: 10.1002/cpt.2977.Peer-Reviewed Original ResearchMeSH KeywordsAdultChildCOVID-19FemaleHospitalizationHumansMalePharmacoepidemiologyPost-Acute COVID-19 SyndromePrognosisConceptsPrognostic factorsUmbrella reviewHigh riskIntensive care unit admissionShort-term adverse outcomesCOVID-19AMSTAR-2 toolSystematic literature reviewCare unit admissionRisk of hospitalizationKey prognostic factorsHigh-risk groupCoronavirus disease 2019 (COVID-19) pandemicCOVID-19 outcomesDisease 2019 pandemicComparative effectiveness researchInternational SocietyCOVID-19 disparitiesUnit admissionLong COVIDMale sexAdverse outcomesOptimal careFemale sexHeart disease
2022
ISPE‐Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID‐19: Opportunities and Trade‐Offs
Sarri G, Bennett D, Debray T, Deruaz‐Luyet A, Gabarró M, Largent JA, Li X, Liu W, Lund JL, Moga DC, Gokhale M, Rentsch CT, Wen X, Yanover C, Ye Y, Yun H, Zullo AR, Lin KJ. ISPE‐Endorsed Guidance in Using Electronic Health Records for Comparative Effectiveness Research in COVID‐19: Opportunities and Trade‐Offs. Clinical Pharmacology & Therapeutics 2022, 112: 990-999. PMID: 35170021, PMCID: PMC9087010, DOI: 10.1002/cpt.2560.Peer-Reviewed Original ResearchMeSH KeywordsComparative Effectiveness ResearchCOVID-19Electronic Health RecordsHumansPandemicsPharmacoepidemiologyConceptsComparative effectiveness researchElectronic health recordsRoutine careCOVID-19Health recordsCoronavirus disease 2019 (COVID-19) pandemicLong-term treatment effectsEHR dataEffectiveness researchDisease 2019 pandemicRigorous study designsCOVID-19-related questionsAscertainment of outcomesVaccine effectivenessComplex patientsExposure statusHealthcare databasesTherapeutic interventionsOptimal managementHealthcare professionalsClinical researchStudy designTreatment effectsAppropriate statistical methodsInternational Society
2021
Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans
Gerlovin H, Posner DC, Ho YL, Rentsch CT, Tate JP, King JT, Kurgansky KE, Danciu I, Costa L, Linares FA, Goethert ID, Jacobson DA, Freiberg MS, Begoli E, Muralidhar S, Ramoni RB, Tourassi G, Gaziano JM, Justice AC, Gagnon DR, Cho K. Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans. American Journal Of Epidemiology 2021, 190: 2405-2419. PMID: 34165150, PMCID: PMC8384407, DOI: 10.1093/aje/kwab183.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAnti-Bacterial AgentsAzithromycinCOVID-19COVID-19 Drug TreatmentDrug Therapy, CombinationFemaleHospitalizationHumansHydroxychloroquineIntention to Treat AnalysisMachine LearningMaleMiddle AgedPharmacoepidemiologyRetrospective StudiesSARS-CoV-2Treatment OutcomeUnited StatesVeteransConceptsUS veteransCOVID-19Veterans Affairs Health Care SystemRecent randomized clinical trialsAdministration of hydroxychloroquineEffectiveness of hydroxychloroquineRisk of intubationEffect of hydroxychloroquineElectronic health record dataRandomized clinical trialsTreatment of patientsUS veteran populationCOVID-19 outcomesCoronavirus disease 2019Health record dataRigorous study designsHealth care systemSurvival benefitTreat analysisEarly therapyHospitalized populationClinical trialsObservational studyDisease 2019Hydroxychloroquine
2018
Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection
Rentsch CT, Cartwright EJ, Gandhi NR, Brown ST, Rodriguez-Barradas MC, Goetz MB, Marconi VC, Gibert CL, Re VL, Fiellin DA, Justice AC, Tate JP. Provider verification of electronic health record receipt and nonreceipt of direct-acting antivirals for the treatment of hepatitis C virus infection. Annals Of Epidemiology 2018, 28: 808-811. PMID: 30195616, PMCID: PMC6318448, DOI: 10.1016/j.annepidem.2018.08.007.Peer-Reviewed Original ResearchConceptsHepatitis C virus infectionCorporate Data WarehouseChronic HCV infectionC virus infectionPositive predictive valuePredictive valueHCV infectionHCV treatmentVirus infectionVeterans Health Administration Corporate Data WarehouseChronic hepatitis C virus (HCV) infectionStudy periodModern treatment eraRetrospective cohort studyElectronic health record dataPharmacy fill recordsHealth record dataNegative predictive valueElectronic health recordsAntiviral regimenHCV therapyTreatment eraChart reviewCohort studyAntiviral treatment