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
2013
Testing for a Changepoint in the Cox Survival Regression Model
Zucker D, Agami S, Spiegelman D. Testing for a Changepoint in the Cox Survival Regression Model. Journal Of Statistical Theory And Practice 2013, 7: 360-380. DOI: 10.1080/15598608.2013.772030.Peer-Reviewed Original ResearchMaximum efficiency robust testCovariate domainSupremum-type testsTest statisticEfficiency robust testSupremum statisticsFirst author's websiteNurses' Health StudyCox survival regression modelType statisticsPower calculationCovariate effectsAuthor's websiteSimulation studySurvival regression modelsLinear combination formStandard Cox modelBest overall choicePopular modelsSimulation resultsFatal myocardial infarctionCox regression modelMATLAB softwareStatisticsRobust test
1999
Evaluation 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 measuresVariance
1990
The Evaluation of Integrals of the form ∫+∞ −∞ f(t)exp(−t 2) dt: Application to Logistic-Normal Models
Crouch E, Spiegelman D. The Evaluation of Integrals of the form ∫+∞ −∞ f(t)exp(−t 2) dt: Application to Logistic-Normal Models. Journal Of The American Statistical Association 1990, 85: 464-469. DOI: 10.1080/01621459.1990.10476222.Peer-Reviewed Original ResearchMeasurement error modelGaussian quadratureLogistic normal distributionEvaluation of integralsMaximum likelihood estimatorSimple matrix transformationAnalytic intractabilityStatistical applicationsLikelihood equationsArbitrary accuracyLogistic-normal modelNumerous approximationsStandard subroutinesLikelihood estimatorMatrix transformationNumerical alternativeSimulation studyQuadratureNew methodComparative calculationsCurrent interestApproximationEquationsIntegralsEstimator
1989
Correction of logistic regression relative risk estimates and confidence intervals for systematic within‐person measurement error
Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within‐person measurement error. Statistics In Medicine 1989, 8: 1051-1069. PMID: 2799131, DOI: 10.1002/sim.4780080905.Peer-Reviewed Original ResearchConceptsLikelihood approximation methodApproximation methodLinear approximation methodSecond-order Taylor series expansionTaylor series expansionEstimation of lambdaMeasurement errorCoverage probabilitySeries expansionLikelihood estimationTrue odds ratioSimulation studyPerson measurement errorSystematic errorsRegression coefficientsErrorCoefficient betaCoefficient lambdaEstimationLogistic regression coefficientsLogistic functionEstimatesTrue exposure