2024
Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure
Tang W, Spiegelman D, Liao X, Wang M. Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure. Epidemiology 2024, 35: 320-328. PMID: 38630507, DOI: 10.1097/ede.0000000000001706.Peer-Reviewed Original ResearchConceptsMismeasured exposureOutcome modelRegression calibrationMeasurement error modelSelection of covariatesNonparametric settingEffect modificationCovariate adjustmentFiber intakeMeasurement errorCardiovascular diseaseEffects of fiber intakeStudy datasetOutcomesCovariatesComprehensive guidanceError modelRegressionHealthEfficiency lossErrorRosnerWillettExposureAdjustment
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
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 modelsChoice