2023
Correcting for Bias Due to Mismeasured Exposure History in Longitudinal Studies with Continuous Outcomes
Cai J, Zhang N, Zhou X, Spiegelman D, Wang M. Correcting for Bias Due to Mismeasured Exposure History in Longitudinal Studies with Continuous Outcomes. Biometrics 2023, 79: 3739-3751. PMID: 37222518, PMCID: PMC11214728, DOI: 10.1111/biom.13877.Peer-Reviewed Original ResearchMediation analysis in the presence of continuous exposure measurement error
Cheng C, Spiegelman D, Li F. Mediation analysis in the presence of continuous exposure measurement error. Statistics In Medicine 2023, 42: 1669-1686. PMID: 36869626, PMCID: PMC11320713, DOI: 10.1002/sim.9693.Peer-Reviewed Original ResearchConceptsBody mass indexExposure measurement errorPhysical activityMediation proportionHealth Professionals FollowCardiovascular disease incidenceProfessionals FollowMediation analysisMass indexCardiovascular diseaseLower riskStudy designEffect estimatesValidation study designContinuous exposureBiased effect estimatesTrue exposureMediatorsExposureValidation studyBinary outcomesHealth science studiesOutcomesRiskDisease incidence
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
2020
Estimation 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 misclassificationIncidenceDiseaseEstimation in the Cox survival regression model with covariate measurement error and a changepoint
Agami S, Zucker DM, Spiegelman D. Estimation in the Cox survival regression model with covariate measurement error and a changepoint. Biometrical Journal 2020, 62: 1139-1163. PMID: 32003495, DOI: 10.1002/bimj.201800085.Peer-Reviewed Original ResearchConceptsSystolic blood pressure levelsChronic air pollution exposureCox survival regression modelFatal myocardial infarctionBlood pressure levelsCardiovascular disease deathsCox regression modelAir pollution exposureRegression modelsDisease deathsMyocardial infarctionRelative riskStandard Cox modelSurvival regression modelsCox modelPollution exposureSurvival endpointsCovariates of interest
2018
There is no impact of exposure measurement error on latency estimation in linear models
Peskoe SB, Spiegelman D, Wang M. There is no impact of exposure measurement error on latency estimation in linear models. Statistics In Medicine 2018, 38: 1245-1261. PMID: 30515870, PMCID: PMC6542365, DOI: 10.1002/sim.8038.Peer-Reviewed Original ResearchConceptsMeasurement error modelLinear measurement error modelsLeast squares estimatorStandard measurement error modelLinear modelError modelRegression coefficient estimatesLikelihood-based methodsMeasurement errorExposure measurement errorSquares estimatorWide classGeneralized linear modelMean functionStatistical modelCovariance structureError settingsNaive estimatorBody mass indexBehavioral risk factorsLatency parametersExposure-disease relationshipsPrimary disease modelTime-varying exposureCoefficient estimatesA test for gene–environment interaction in the presence of measurement error in the environmental variable
Aschard H, Spiegelman D, Laville V, Kraft P, Wang M. A test for gene–environment interaction in the presence of measurement error in the environmental variable. Genetic Epidemiology 2018, 42: 250-264. PMID: 29424028, PMCID: PMC5851866, DOI: 10.1002/gepi.22113.Peer-Reviewed Original ResearchThe effect of risk factor misclassification on the partial population attributable risk
Wong BHW, Peskoe SB, Spiegelman D. The effect of risk factor misclassification on the partial population attributable risk. Statistics In Medicine 2018, 37: 1259-1275. PMID: 29333614, PMCID: PMC6003717, DOI: 10.1002/sim.7559.Peer-Reviewed Original ResearchConceptsPartial population attributable riskPopulation attributable riskRisk factorsAttributable riskRelative riskMultivariate-adjusted relative riskRed meatHealth Professionals FollowModifiable risk factorsLow folate intakeExposure of interestBackground risk factorsProfessionals FollowAlcohol intakeColorectal cancerFolate intakePublic health researchMultifactorial diseasePreventive interventionsPopulation-level impactJoint prevalenceHealth researchRiskIntakeExposure
2005
Calculating deaths attributable to obesity.
Hu FB, Willett WC, Stampfer MJ, Spiegelman D, Colditz GA. Calculating deaths attributable to obesity. American Journal Of Public Health 2005, 95: 932; author reply 932-3. PMID: 15914810, PMCID: PMC1449281, DOI: 10.2105/ajph.2005.062836.Peer-Reviewed Original ResearchCorrelated 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
Commentary: Correlated errors and energy adjustment—where are the data?
Spiegelman D. Commentary: Correlated errors and energy adjustment—where are the data? International Journal Of Epidemiology 2004, 33: 1387-1388. PMID: 15333623, DOI: 10.1093/ije/dyh315.Peer-Reviewed Original Research
2002
Segmented Regression in the Presence of Covariate Measurement Error in Main Study/Validation Study Designs
Staudenmayer J, Spiegelman D. Segmented Regression in the Presence of Covariate Measurement Error in Main Study/Validation Study Designs. Biometrics 2002, 58: 871-877. PMID: 12495141, DOI: 10.1111/j.0006-341x.2002.00871.x.Peer-Reviewed Original ResearchThe Performance of Methods for Correcting Measurement Error in Case-Control Studies
Stürmer T, Thürigen D, Spiegelman D, Blettner M, Brenner H. The Performance of Methods for Correcting Measurement Error in Case-Control Studies. Epidemiology 2002, 13: 507-516. PMID: 12192219, DOI: 10.1097/00001648-200209000-00005.Peer-Reviewed Original Research
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 Research
1998
Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists.
Spiegelman D, Valanis B. Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists. American Journal Of Public Health 1998, 88: 406-12. PMID: 9518972, PMCID: PMC1508329, DOI: 10.2105/ajph.88.3.406.Peer-Reviewed Original ResearchConceptsMeasurement error modelInterval estimatesExposure measurement errorMeasurement errorError modelPrevalence ratiosRelative riskLikelihood-based methodsLog relative riskNondifferential measurement errorStatistical methodsRelative risk estimatesOutcomes of interestOccupational exposurePublic health researchHospital pharmacistsLogistic regressionRisk estimatesWeekly numberFirst methodHealth effectsUsual pointHealth researchErrorPharmacists
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 errorMeasurement Error Correction for Logistic Regression Models with an “Alloyed Gold Standard”
Spiegelman D, Schneeweiss S, McDermott A. Measurement Error Correction for Logistic Regression Models with an “Alloyed Gold Standard”. American Journal Of Epidemiology 1997, 145: 184-196. PMID: 9006315, DOI: 10.1093/oxfordjournals.aje.a009089.Peer-Reviewed Original Research
1992
Correction of Logistic Regression Relative Risk Estimates and Confidence Intervals for Random Within-Person Measurement Error
Rosner B, Spiegelman D, Willett WC. Correction of Logistic Regression Relative Risk Estimates and Confidence Intervals for Random Within-Person Measurement Error. American Journal Of Epidemiology 1992, 136: 1400-1413. PMID: 1488967, DOI: 10.1093/oxfordjournals.aje.a116453.Peer-Reviewed Original ResearchConceptsRelative risk estimatesRisk factorsLogistic regressionRisk estimatesCoronary risk factorsCoronary heart diseaseGold standardConfidence intervalsFramingham Heart StudyExamination 4Extreme quintilesHeart diseaseOdds ratioHeart StudyExamination 2Exposure assessmentSubstudyCovariatesMenMain studyReproducibility dataRegressionFactorsQuintileIncidenceDietary fat and fiber in relation to risk of breast cancer. An 8-year follow-up.
Willett WC, Hunter DJ, Stampfer MJ, Colditz G, Manson JE, Spiegelman D, Rosner B, Hennekens CH, Speizer FE. Dietary fat and fiber in relation to risk of breast cancer. An 8-year follow-up. JAMA 1992, 268: 2037-44. PMID: 1328696, DOI: 10.1001/jama.268.15.2037.Peer-Reviewed Original ResearchConceptsTotal fat intakeBreast cancer incidenceFat intakeBreast cancerPostmenopausal womenCancer incidenceProtective effectHealth StudySelf-administered food frequency questionnaireEnergy intakePositive associationDietary fat increasesNurses' Health StudyProspective cohort studyFood frequency questionnaireTotal energy intakeMiddle-aged womenYears of ageDietary questionnaireCohort studyFrequency questionnaireIncident casesRisk factorsDietary fatCorresponding RRs