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 sizeTesting gene–environment interactions in the presence of confounders and mismeasured environmental exposures
Cheng C, Spiegelman D, Wang Z, Wang M. Testing gene–environment interactions in the presence of confounders and mismeasured environmental exposures. G3: Genes, Genomes, Genetics 2021, 11: jkab236. PMID: 34568916, PMCID: PMC8473983, DOI: 10.1093/g3journal/jkab236.Peer-Reviewed Original ResearchMeSH KeywordsCohort StudiesComputer SimulationEnvironmental ExposureGene-Environment InteractionHumansModels, GeneticConceptsStandard logistic regression approachGreater statistical powerStatistical powerBinary disease outcomeComputational efficiencyIllustrative exampleComputation timeExtensive simulation experimentsMost simulation scenariosMeasurement errorRegression approachConsideration adjustmentsSimulation experimentsExposure measurement errorReverse testLogistic regression approachSimulation scenariosLinear discriminant analysisApproachReverse approachPowerErrorDiscriminant analysis
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
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
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 estimates
2017
Prevalence estimation when disease status is verified only among test positives: Applications in HIV screening programs
Thomas E, Peskoe S, Spiegelman D. Prevalence estimation when disease status is verified only among test positives: Applications in HIV screening programs. Statistics In Medicine 2017, 37: 1101-1114. PMID: 29230839, PMCID: PMC6512805, DOI: 10.1002/sim.7568.Peer-Reviewed Original Research
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
2002
The 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