2021
A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design
Chen X, Chang J, Spiegelman D, Li F. A Bayesian approach for estimating the partial potential impact fraction with exposure measurement error under a main study/internal validation design. Statistical Methods In Medical Research 2021, 31: 404-418. PMID: 34841964, DOI: 10.1177/09622802211060514.Peer-Reviewed Original ResearchConceptsPotential impact fractionImpact fractionExposure measurement errorHealth professionalsStudy designColorectal cancer incidenceValidation study designBurden of diseaseRisk factorsCancer incidenceHealth StudyDisease casesPublic health studiesRed meatContinuous exposureExposureProfessionalsIncidenceReclassification approachValidation designDiseaseIntakeEstimating the natural indirect effect and the mediation proportion via the product method
Cheng C, Spiegelman D, Li F. Estimating the natural indirect effect and the mediation proportion via the product method. BMC Medical Research Methodology 2021, 21: 253. PMID: 34800985, PMCID: PMC8606099, DOI: 10.1186/s12874-021-01425-4.Peer-Reviewed Original ResearchConceptsInterval estimatorsApproximate estimatorExact estimatorMultivariate delta methodFinite sample performanceProduct methodNon-negligible biasBinary outcomesRare outcome assumptionExact expressionDelta methodVariance estimationEmpirical performanceEstimatorCommon data typesBootstrap approachBinary mediatorNatural indirect effectSample sizeswdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials
Chen J, Zhou X, Li F, Spiegelman D. swdpwr: A SAS macro and an R package for power calculations in stepped wedge cluster randomized trials. Computer Methods And Programs In Biomedicine 2021, 213: 106522. PMID: 34818620, PMCID: PMC8665077, DOI: 10.1016/j.cmpb.2021.106522.Peer-Reviewed Original ResearchMeSH KeywordsCluster AnalysisCross-Sectional StudiesHumansRandomized Controlled Trials as TopicResearch DesignSample SizeConceptsWedge clusterIntracluster correlation coefficientContinuous outcomesCross-sectional cohortBinary outcomesExchangeable correlation structureWedge designPublic health intervention evaluationsHealth services researchClosed cohort designPower calculationCohort designClosed cohortStudy designIntracluster correlationIntervention evaluationNeeds of investigatorsOutcomesTrialsCohortServices researchInvestigatorsPrevious studiesSWD
2020
How are qualitative methods used in implementation science research? A scoping review protocol.
Hagaman A, Rhodes EC, Nyhan K, Katague M, Schwartz A, Spiegelman D. How are qualitative methods used in implementation science research? A scoping review protocol. JBI Evidence Synthesis 2020, 19: 1344-1353. PMID: 33323772, DOI: 10.11124/jbies-20-00120.Peer-Reviewed Original ResearchMeSH KeywordsDelivery of Health CareHumansImplementation ScienceResearch DesignSystematic Reviews as TopicConceptsQualitative methodsQualitative research methodsPerspectives of stakeholdersCountry of originImplementation science outcomesImplementation researchMore qualitative methodsImplementation scienceField of studyScience researchPolicy makersImplementation science frameworkImplementation science researchImplementation research studyResearch methodsEvidence-based practicePrimary dataHealth practitionersScience FrameworkDepth understandingResearch studiesType of settingImplementation strategiesIssuesArticleEstimation and inference for the population attributable risk in the presence of misclassification
Wong BHW, Lee J, Spiegelman D, Wang M. Estimation and inference for the population attributable risk in the presence of misclassification. Biostatistics 2020, 22: 805-818. PMID: 32112073, PMCID: PMC8966954, DOI: 10.1093/biostatistics/kxz067.Peer-Reviewed Original ResearchConceptsPopulation attributable riskAttributable riskPartial population attributable riskHigh red meat intakeColorectal cancer incidenceRed meat intakeAlcohol intakeRisk factorsCancer incidenceMeat intakeEpidemiologic studiesPublic health researchDisease casesStudy designValidation study designInternal validation studyHealth researchTarget populationIntakeValidation studyRiskHealth evaluation methodPresence of misclassificationIncidenceDisease
2019
On the analysis of two‐phase designs in cluster‐correlated data settings
Rivera‐Rodriguez C, Spiegelman D, Haneuse S. On the analysis of two‐phase designs in cluster‐correlated data settings. Statistics In Medicine 2019, 38: 4611-4624. PMID: 31359448, PMCID: PMC6736737, DOI: 10.1002/sim.8321.Peer-Reviewed Original ResearchConceptsSmall-sample operating characteristicsInverse probability weighting estimatorData settingClosed-form expressionTwo-phase designStatistical efficiencyComprehensive simulation studyWeighting estimatorCovariance structureSandwich estimatorInvalid inferencesValid inferencesSimulation studyCovariate dataInverse probability weightingEstimatorNaïve methodSampling designNovel analysis approachInferenceRobust sandwich estimatorAnalysis methodAnalysis approachNational antiretroviral treatment programmeCategorical risk
2017
Evaluating Public Health Interventions: 7. Let the Subject Matter Choose the Effect Measure: Ratio, Difference, or Something Else Entirely.
Spiegelman D, Khudyakov P, Wang M, Vanderweele TJ. Evaluating Public Health Interventions: 7. Let the Subject Matter Choose the Effect Measure: Ratio, Difference, or Something Else Entirely. American Journal Of Public Health 2017, 108: 73-76. PMID: 29161073, PMCID: PMC5719681, DOI: 10.2105/ajph.2017.304105.Peer-Reviewed Original ResearchMeSH KeywordsCost-Benefit AnalysisHumansModels, StatisticalOdds RatioPublic HealthQuality-Adjusted Life YearsResearch DesignRisk FactorsConceptsRisk factor distributionRisk ratioLife yearsEffect measuresDisability-adjusted life yearsIncremental cost-effectiveness ratioPopulation attributable riskQuality-adjusted life yearsCost-effectiveness ratioPublic health interventionsPublic health evaluationYears of lifeMeasure of effectRisk factorsRelative riskStudy populationRisk differenceHealth interventionsIntervention effectsAbsolute effect measuresHealth evaluationExternal generalizabilityRiskAbsolute measuresPopulation
2012
Cohort studies around the world: Methodologies, research questions and integration to address the emerging global epidemic of chronic diseases
Nair H, Shu XO, Volmink J, Romieu I, Spiegelman D. Cohort studies around the world: Methodologies, research questions and integration to address the emerging global epidemic of chronic diseases. Public Health 2012, 126: 202-205. PMID: 22325615, DOI: 10.1016/j.puhe.2011.12.013.Peer-Reviewed Original ResearchMeSH KeywordsChronic DiseaseCohort StudiesEpidemicsGlobal HealthHumansInternationalityResearch DesignConceptsChronic diseasesCohort studyChronic non-communicable diseasesNon-communicable chronic diseasesDual disease burdenCommunicable infectious diseasesNon-communicable diseasesMultiple cohort studiesResource-constrained settingsDisease burdenRisk factorsLifestyle changesGlobal epidemicInfectious diseasesDiseaseDisease distributionEpidemicBurdenPopulationSettingDramatic increase
2005
Correlated errors in biased surrogates: study designs and methods for measurement error correction
Spiegelman D, Zhao B, Kim J. Correlated errors in biased surrogates: study designs and methods for measurement error correction. Statistics In Medicine 2005, 24: 1657-1682. PMID: 15736283, DOI: 10.1002/sim.2055.Peer-Reviewed Original Research
2004
Fruit and Vegetable Intake and Risk of Major Chronic Disease
Hung HC, Joshipura KJ, Jiang R, Hu FB, Hunter D, Smith-Warner SA, Colditz GA, Rosner B, Spiegelman D, Willett WC. Fruit and Vegetable Intake and Risk of Major Chronic Disease. Journal Of The National Cancer Institute 2004, 96: 1577-1584. PMID: 15523086, DOI: 10.1093/jnci/djh296.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAnalysis of VarianceCardiovascular DiseasesCause of DeathChronic DiseaseConfidence IntervalsConfounding Factors, EpidemiologicDiet SurveysFeeding BehaviorFemaleFollow-Up StudiesFruitHealth PersonnelHumansIncidenceMaleMiddle AgedNeoplasmsProportional Hazards ModelsProspective StudiesResearch DesignRisk AssessmentRisk FactorsSurveys and QuestionnairesUnited StatesVegetablesConceptsMajor chronic diseasesVegetable intakeCardiovascular diseaseChronic diseasesRelative riskHealth StudyVegetable consumptionGreen leafy vegetable intakeSemiquantitative food frequency questionnaireCox proportional hazards analysisOverall cancer incidenceTotal fruitNurses' Health StudyFood frequency questionnaireProportional hazards analysisStrong inverse associationProspective cohortGreen leafy vegetablesHighest quintileInverse associationCancer incidenceFood groupsHealth professionalsOverall healthDietary informationDairy Foods, Calcium, and Colorectal Cancer: A Pooled Analysis of 10 Cohort Studies
Cho E, Smith-Warner SA, Spiegelman D, Beeson WL, van den Brandt PA, Colditz GA, Folsom AR, Fraser GE, Freudenheim JL, Giovannucci E, Goldbohm RA, Graham S, Miller AB, Pietinen P, Potter JD, Rohan TE, Terry P, Toniolo P, Virtanen MJ, Willett WC, Wolk A, Wu K, Yaun SS, Zeleniuch-Jacquotte A, Hunter DJ. Dairy Foods, Calcium, and Colorectal Cancer: A Pooled Analysis of 10 Cohort Studies. Journal Of The National Cancer Institute 2004, 96: 1015-1022. PMID: 15240785, DOI: 10.1093/jnci/djh185.Peer-Reviewed Original ResearchMeSH KeywordsAdenomaAdultAgedAnimalsCalcium, DietaryCohort StudiesColorectal NeoplasmsDairy ProductsEatingEuropeFemaleHumansIncidenceMaleMiddle AgedMilkMultivariate AnalysisProportional Hazards ModelsProspective StudiesResearch DesignRisk AssessmentRisk FactorsSurveys and QuestionnairesUnited StatesConceptsColorectal cancerRelative riskMilk intakeCalcium intakeCohort studyPooled multivariable relative risksMultivariable relative risksFood frequency questionnaireColorectal cancer riskUsual dietary intakeConfidence intervalsDairy foodsFrequency questionnaireIncident casesDietary calciumPooled analysisLowest quintileInverse associationDietary intakeDistal colonEpidemiologic studiesCancer riskLower riskTotal calciumCancer
2000
Measurement error correction using validation data: a review of methods and their applicability in case-control studies
Thürigen D, Spiegelman D, Blettner M, Heuer C, Brenner H. Measurement error correction using validation data: a review of methods and their applicability in case-control studies. Statistical Methods In Medical Research 2000, 9: 447-474. PMID: 11191260, DOI: 10.1177/096228020000900504.Peer-Reviewed Original ResearchEfficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument
Spiegelman D, Carroll R, Kipnis V. Efficient regression calibration for logistic regression in main study/internal validation study designs with an imperfect reference instrument. Statistics In Medicine 2000, 20: 139-160. PMID: 11135353, DOI: 10.1002/1097-0258(20010115)20:1<139::aid-sim644>3.0.co;2-k.Peer-Reviewed Original Research
1999
Re: Meta-analysis: Dietary Fat Intake, Serum Estrogen Levels, and the Risk of Breast Cancer
Holmes M, Schisterman E, Spiegelman D, Hunter D, Willett W. Re: Meta-analysis: Dietary Fat Intake, Serum Estrogen Levels, and the Risk of Breast Cancer. Journal Of The National Cancer Institute 1999, 91: 1511-1512. PMID: 10469759, DOI: 10.1093/jnci/91.17.1511.Peer-Reviewed Original ResearchRationale and Design of the Tanzania Vitamin and HIV Infection Trial
Fawzi W, Msamanga G, Spiegelman D, Urassa E, Hunter D. Rationale and Design of the Tanzania Vitamin and HIV Infection Trial. Contemporary Clinical Trials 1999, 20: 75-90. PMID: 10027501, DOI: 10.1016/s0197-2456(98)00045-2.Peer-Reviewed Original ResearchConceptsHIV infectionVitamin APregnant womenPolymerase chain reactionVitamin supplementsHIV-positive pregnant womenVertical transmissionHIV Infection TrialInfant immune functionEligible pregnant womenPlacebo-controlled trialClinical staging systemHIV-positive womenGenital tract secretionsUse of supplementsHIV diseaseMost HIVPrevention trialsViral loadMain endpointBreast milkStaging systemCohort retentionPosttest counselingTract secretions