2022
Is the Product Method More Efficient Than the Difference Method for Assessing Mediation?
Cheng C, Spiegelman D, Li F. Is the Product Method More Efficient Than the Difference Method for Assessing Mediation? American Journal Of Epidemiology 2022, 192: 84-92. PMID: 35921210, PMCID: PMC10144745, DOI: 10.1093/aje/kwac144.Peer-Reviewed Original ResearchMeSH KeywordsBiomedical ResearchEpidemiologic StudiesEswatiniHumansMediation AnalysisModels, StatisticalDiet- and Lifestyle‐Based Prediction Models to Estimate Cancer Recurrence and Death in Patients With Stage III Colon Cancer (CALGB 89803/Alliance)
Cheng E, Ou FS, Ma C, Spiegelman D, Zhang S, Zhou X, Bainter TM, Saltz LB, Niedzwiecki D, Mayer RJ, Whittom R, Hantel A, Benson A, Atienza D, Messino M, Kindler H, Giovannucci EL, Van Blarigan EL, Brown JC, Ng K, Gross CP, Meyerhardt JA, Fuchs CS. Diet- and Lifestyle‐Based Prediction Models to Estimate Cancer Recurrence and Death in Patients With Stage III Colon Cancer (CALGB 89803/Alliance). Journal Of Clinical Oncology 2022, 40: 740-751. PMID: 34995084, PMCID: PMC8887946, DOI: 10.1200/jco.21.01784.Peer-Reviewed Original ResearchMeSH KeywordsAgedAntineoplastic Combined Chemotherapy ProtocolsChemotherapy, AdjuvantColonic NeoplasmsDietFemaleFollow-Up StudiesHumansLife StyleMaleMiddle AgedModels, StatisticalMulticenter Studies as TopicNeoplasm Recurrence, LocalNomogramsPrognosisRandomized Controlled Trials as TopicRisk FactorsSurvival RateConceptsStage III colon cancerDisease-free survivalLifestyle factorsSelf-reported dietPathologic featuresColon cancerPathologic characteristicsMultivariable Cox proportional hazards regressionCox proportional hazards regressionAdjuvant chemotherapy trialsProportional hazards regressionPredictive survivalChemotherapy trialsDFS eventsOverall survivalSurvival prediction modelHazards regressionSurvival outcomesVisual nomogramLifestyle habitsPatient outcomesCancer recurrenceLifestyle exposuresPatientsCancer
2021
Estimating 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 size
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
2009
Power and sample size calculations for longitudinal studies comparing rates of change with a time‐varying exposure
Basagaña X, Spiegelman D. Power and sample size calculations for longitudinal studies comparing rates of change with a time‐varying exposure. Statistics In Medicine 2009, 29: 181-192. PMID: 19899065, PMCID: PMC3772653, DOI: 10.1002/sim.3772.Peer-Reviewed Original Research
2007
Point and interval estimates of partial population attributable risks in cohort studies: examples and software
Spiegelman D, Hertzmark E, Wand HC. Point and interval estimates of partial population attributable risks in cohort studies: examples and software. Cancer Causes & Control 2007, 18: 571-579. PMID: 17387622, DOI: 10.1007/s10552-006-0090-y.Peer-Reviewed Original ResearchConceptsCohort studyRisk factorsPartial population attributable riskNon-modifiable risk factorsSpecific exposuresPopulation attributable risk percentAttributable risk percentPopulation attributable riskBladder cancer incidenceGroups of exposureTarget populationProportion of diseaseAttributable riskDisease burdenModifiable determinantsCancer incidencePublic health researchDiseaseHealth researchExposurePopulationIncidenceProportion
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
2001
Predictors of airborne endotoxin in the home.
Park JH, Spiegelman DL, Gold DR, Burge HA, Milton DK. Predictors of airborne endotoxin in the home. Environmental Health Perspectives 2001, 109: 859-864. PMID: 11564624, PMCID: PMC1240416, DOI: 10.1289/ehp.01109859.Peer-Reviewed Original ResearchMeSH KeywordsAir PollutantsAir Pollution, IndoorAllergensAnimalsAnimals, DomesticAsthmaCatsDogsDustEndotoxinsEnvironmental MonitoringEpidemiological MonitoringHousingHumansHumidityHypersensitivityInfantLongitudinal StudiesMassachusettsMiceModels, StatisticalMultivariate AnalysisPredictive Value of TestsRegression AnalysisSeasonsTobacco Smoke PollutionValidation of the Gail et al. Model of Breast Cancer Risk Prediction and Implications for Chemoprevention
Rockhill B, Spiegelman D, Byrne C, Hunter D, Colditz G. Validation of the Gail et al. Model of Breast Cancer Risk Prediction and Implications for Chemoprevention. Journal Of The National Cancer Institute 2001, 93: 358-366. PMID: 11238697, DOI: 10.1093/jnci/93.5.358.Peer-Reviewed Original ResearchConceptsInvasive breast cancerGail et alRisk factor strataBreast cancerDiscriminatory accuracyHealth StudyModest discriminatory accuracyNurses' Health StudySubset of womenBreast cancer casesBreast cancer risk predictionNet health benefitCancer risk predictionTamoxifen useCancer casesPrevention optionsConcordance statisticClinical counselingCancerYoung womenWhite womenRisk estimatesWomenHealth benefitsRisk predictionSex‐specific mortality from adult T‐cell leukemia among carriers of human T‐lymphotropic virus type I
Hisada M, Okayama A, Spiegelman D, Mueller N, Stuver S. Sex‐specific mortality from adult T‐cell leukemia among carriers of human T‐lymphotropic virus type I. International Journal Of Cancer 2001, 91: 497-499. PMID: 11251972, DOI: 10.1002/1097-0215(20010215)91:4<497::aid-ijc1044>3.0.co;2-a.Peer-Reviewed Original ResearchConceptsAdult T-cell leukemiaHTLV-I carriersHuman T-lymphotropic virus type IRelative riskVirus type IT-cell leukemiaIncidence of ATLMiyazaki Cohort StudyUnadjusted relative riskAdjusted relative riskType IPerinatal infectionEarlier average ageCohort studyMale predominanceRisk factorsAverage ageInfectionMortalityNumber of personsLeukemiaEarly ageMalesFemalesSex-specific mortality
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 ResearchA tetranucleotide repeat polymorphism in CYP19 and breast cancer risk
Haiman C, Hankinson S, Spiegelman D, De Vivo I, Colditz G, Willett W, Speizer F, Hunter D. A tetranucleotide repeat polymorphism in CYP19 and breast cancer risk. International Journal Of Cancer 2000, 87: 204-210. PMID: 10861475, DOI: 10.1002/1097-0215(20000715)87:2<204::aid-ijc8>3.0.co;2-3.Peer-Reviewed Original ResearchConceptsBreast cancer riskHealth Study cohortCase-control studyCancer riskHormone levelsNurses' Health Study cohortBreast cancer case-control studyElevated estrogen levelsAdvanced cancer casesCancer case-control studyRepeat polymorphismTetranucleotide repeat polymorphismConversion of androgensGreater frequencyKey steroidogenic enzymesSignificant greater frequencyDistant metastasisStudy cohortEstrogen levelsCYP19 allelesBreast cancerCancer casesAllele carriersEstrone sulfateNonsignificant increase
1999
Design of Validation Studies for Estimating the Odds Ratio of Exposure–Disease Relationships When Exposure is Misclassified
Holcroft C, Spiegelman D. Design of Validation Studies for Estimating the Odds Ratio of Exposure–Disease Relationships When Exposure is Misclassified. Biometrics 1999, 55: 1193-1201. PMID: 11315067, DOI: 10.1111/j.0006-341x.1999.01193.x.Peer-Reviewed Original ResearchMeSH KeywordsBiometryBreast NeoplasmsEpidemiologic MethodsFemaleHumansLeadLogistic ModelsModels, StatisticalOdds RatioReproducibility of ResultsConceptsOdds ratioEvaluation of Old and New Tests of Heterogeneity in Epidemiologic Meta-Analysis
Takkouche B, Cadarso-Suárez C, Spiegelman D. Evaluation of Old and New Tests of Heterogeneity in Epidemiologic Meta-Analysis. American Journal Of Epidemiology 1999, 150: 206-215. PMID: 10412966, DOI: 10.1093/oxfordjournals.aje.a009981.Peer-Reviewed Original ResearchConceptsParametric bootstrap versionBootstrap versionLarge simulation studyCorrect type IStatistical powerComputational easeSimulation studyIdentification of heterogeneityHypothesis testQ statisticStudy varianceLow statistical powerNull hypothesisStatisticsPoint of viewBootstrapHomogeneity testVersionPowerDecision criteriaNew testBest choiceKey featuresEffect measuresVarianceMatrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons
Morrissey M, Spiegelman D. Matrix Methods for Estimating Odds Ratios with Misclassified Exposure Data: Extensions and Comparisons. Biometrics 1999, 55: 338-344. PMID: 11318185, DOI: 10.1111/j.0006-341x.1999.00338.x.Peer-Reviewed Original ResearchDietary Fat and Coronary Heart Disease: A Comparison of Approaches for Adjusting for Total Energy Intake and Modeling Repeated Dietary Measurements
Hu F, Stampfer M, Rimm E, Ascherio A, Rosner B, Spiegelman D, Willett W. Dietary Fat and Coronary Heart Disease: A Comparison of Approaches for Adjusting for Total Energy Intake and Modeling Repeated Dietary Measurements. American Journal Of Epidemiology 1999, 149: 531-540. PMID: 10084242, DOI: 10.1093/oxfordjournals.aje.a009849.Peer-Reviewed Original ResearchConceptsCoronary heart diseaseRisk of CHDTotal energy intakeNutrient density modelsBaseline dietHeart diseaseHigh intakeHealth StudyDietary measurementsEnergy intakeMultivariate nutrient density modelsPrevious cohort studiesNurses' Health StudyAssessment of dietEnergy adjustment methodCohort studyEnergy adjustmentFat intakeDietary fatRelative riskRecent dietDietary assessmentIntakeTrans fatsMultivariate model
1997
Fully parametric and semi-parametric regression models for common events with covariate measurement error in main study/validation study designs.
Spiegelman D, Casella M. Fully parametric and semi-parametric regression models for common events with covariate measurement error in main study/validation study designs. Biometrics 1997, 53: 395-409. PMID: 9192443, DOI: 10.2307/2533945.Peer-Reviewed Original ResearchConceptsMain study/validation study designsSemi-parametric methodMeasurement error modelSemi-parametric estimatesCovariate measurement errorSemi-parametric regression modelEmpirical considerationsTrading efficiencyError modelInference proceedsConvenient mathematical propertiesMeasurement errorLikelihood functionModel choiceJoint likelihood functionValidation study designMisspecificationStandard theoryNonparametric formFamily of modelsImportant biasParametric resultsModel covariatesRegression modelsChoiceRegression calibration method for correcting measurement-error bias in nutritional epidemiology
Spiegelman D, McDermott A, Rosner B. Regression calibration method for correcting measurement-error bias in nutritional epidemiology. American Journal Of Clinical Nutrition 1997, 65: s1179-s1186. PMID: 9094918, DOI: 10.1093/ajcn/65.4.1179s.Peer-Reviewed Original ResearchConceptsHealth StudyDietary intakeMassachusetts Women's Health StudyUltradistal radius bone densityCox proportional hazards modelRadius bone densityNurses' Health StudyWomen's Health StudyIncidence rate ratiosRate ratioProportional hazards modelOdds ratioBone densityBreast cancerHazards modelVitamin ANutritional epidemiologyLogistic regressionGold standardLinear regression modelsEpidemiologyIntakeRegression modelsValidation studyPerson errorDietary Fiber, Glycemic Load, and Risk of NIDDM in Men
Salmerón J, Ascherio A, Rimm E, Colditz G, Spiegelman D, Jenkins D, Stampfer M, Wing A, Willett W. Dietary Fiber, Glycemic Load, and Risk of NIDDM in Men. Diabetes Care 1997, 20: 545-550. PMID: 9096978, DOI: 10.2337/diacare.20.4.545.Peer-Reviewed Original ResearchConceptsRisk of NIDDMHigh glycemic loadGlycemic loadCereal fiberRelative riskHigher cereal fiber intakeSemiquantitative food frequency questionnaireCereal fiber intakeLarge glycemic responseDietary glycemic indexFood frequency questionnaireIncidence of NIDDMIntake of carbohydratesTotal energy intakeLow glycemic loadYears of ageDietary fiberFrequency questionnaireIncident casesLowest quintileCardiovascular diseaseFamily historyFiber intakeNIDDMPhysical activity