2025
A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding
Chen X, Hu L, Li F. A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding. Lifetime Data Analysis 2025, 31: 394-421. PMID: 40227517, DOI: 10.1007/s10985-025-09652-3.Peer-Reviewed Original ResearchConceptsG-formulaBalance scoresHealth system electronic health recordDiscrete survival dataTime-to-event outcomesPosterior sampling algorithmParametric g-formulaElectronic health recordsBayesian additive regression treesTime-varying treatmentsHypothetical intervention scenariosAdditive regression treesLongitudinal observational studyGeneral classModel misspecificationHealth recordsEmpirical performanceSampling algorithmObservational studySurvival dataIntervention scenariosScoresTreatment strategiesMisspecificationCausality analysisOutcome adaptive propensity score methods for handling censoring and high-dimensionality: Application to insurance claims
Du J, Yu Y, Zhang M, Wu Z, Ryan A, Mukherjee B. Outcome adaptive propensity score methods for handling censoring and high-dimensionality: Application to insurance claims. Statistical Methods In Medical Research 2025, 34: 847-866. PMID: 40013476, DOI: 10.1177/09622802241306856.Peer-Reviewed Original ResearchPropensity score modelHigh-dimensional settingsVariable selection procedureTreatment effect estimatesPropensity score estimationAverage treatment effectVariable selection methodsModel misspecificationMultiple treatment groupsSimulation studyRegularization methodStatistical efficiencyBinary outcomesScore estimationOutcome probabilitiesSelection procedureHigh-dimensionalTreatment effectsEffect estimatesVariables related to treatmentCensoringPropensity scoreMisspecificationEstimationPropensity score methods
2024
How to achieve model-robust inference in stepped wedge trials with model-based methods?
Wang B, Wang X, Li F. How to achieve model-robust inference in stepped wedge trials with model-based methods? Biometrics 2024, 80: ujae123. PMID: 39499239, PMCID: PMC11536888, DOI: 10.1093/biomtc/ujae123.Peer-Reviewed Original ResearchConceptsTreatment effect estimandsWorking correlation structureSandwich variance estimatorExchangeable working correlation structureFunction of calendar timeEffect estimandsVariance estimationLink functionStepped wedge trialEstimandsTheoretical resultsCorrelation structureWedge trialsEstimating EquationsCluster randomized trialG-computationLinear mixed modelsInferencePotential outcomesMisspecificationEstimationEffective structureModel-based methodsGeneralized Estimating EquationsMixed modelsMaintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures
Ouyang Y, Taljaard M, Forbes A, Li F. Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures. Statistical Methods In Medical Research 2024, 33: 1497-1516. PMID: 38807552, PMCID: PMC11499024, DOI: 10.1177/09622802241248382.Peer-Reviewed Original ResearchRandom effects structureVariance estimationComplex correlation structureRobust variance estimationFixed effects parametersDegrees of freedom correctionCluster randomized trialEstimates of standard errorsCorrelation structureRandom effectsStepped-wedge cluster randomized trialComprehensive simulation studyLinear mixed modelsStatistical inferenceRandom intercept modelSimulation studyMixed modelsMisspecificationValidity of inferencesRandom interceptContinuous outcomesEstimationComputational challengesIntercept modelStandard errorOptimal and Safe Estimation for High-Dimensional Semi-Supervised Learning
Deng S, Ning Y, Zhao J, Zhang H. Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning. Journal Of The American Statistical Association 2024, 119: 2748-2759. PMID: 40078670, PMCID: PMC11902906, DOI: 10.1080/01621459.2023.2277409.Peer-Reviewed Original ResearchSemi-supervised estimatorConditional mean functionMean functionSupervised estimationParameters of linear modelsSemi-supervised learningRegression parametersEstimation problemLinear modelSupplementary materialsTheoretical resultsParameter estimationSemi-supervised settingUnlabeled dataLabeled dataEstimationMinimaxMisspecificationNumerical simulationsDataFunctionLearningProblemData analysis
2020
A note on the estimation and inference with quadratic inference functions for correlated outcomes
Yu H, Tong G, Li F. A note on the estimation and inference with quadratic inference functions for correlated outcomes. Communications In Statistics - Simulation And Computation 2020, 51: 6525-6536. PMID: 36568127, PMCID: PMC9782733, DOI: 10.1080/03610918.2020.1805463.Peer-Reviewed Original ResearchQuadratic inference functionsInference functionScore equationsQuadratic inference function approachRegression parametersFinite samplesCombination of estimatorsGeneral settingEquationsCorrelated outcomesSimulation studyEstimatorFunction approachAnalytical insightsPopular methodInferenceSolutionMultiple setsMisspecificationSetFunctionEstimationAlternative solutionNoteParametersInteraction analysis under misspecification of main effects: Some common mistakes and simple solutions
Zhang M, Yu Y, Wang S, Salvatore M, Fritsche L, He Z, Mukherjee B. Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions. Statistics In Medicine 2020, 39: 1675-1694. PMID: 32101638, DOI: 10.1002/sim.8505.Peer-Reviewed Original ResearchConceptsType I error rateType I error inflationIndependence assumptionWald and score testsCorrect type I error ratesSandwich variance estimatorSandwich estimatorScore testVariance estimationSimulation studyMisspecificationMichigan Genomics InitiativeStatistical practiceBinary outcomesTested interactionsEmpirical factsFlexible modelData modelTest of interactionBiobank studyInflationAssumptionsContinuous outcomesEpidemiological literatureLinear regression models
2018
Posterior Predictive $p$-Values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy Measures
Forastiere L, Mealli F, Miratrix L. Posterior Predictive $p$-Values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy Measures. Bayesian Analysis 2018, 13 DOI: 10.1214/17-ba1062.Peer-Reviewed Original ResearchDiscrepancy measureClassical test statisticsTest statisticIncorrect model specificationPosterior credible intervalsPosterior distributionFisher randomization testModel misspecificationCompliance typeImputation stepAverage causal effectCredible intervalsPermutation testComplier average causal effectGeneral schemeAdditional modelingModel specificationStatisticsRandomization testMisspecificationDifferent approachesOverall approachSchemeValidityRandomized experiments
2016
Variable Selection With Prior Information for Generalized Linear Models via the Prior LASSO Method
Jiang Y, He Y, Zhang H. Variable Selection With Prior Information for Generalized Linear Models via the Prior LASSO Method. Journal Of The American Statistical Association 2016, 111: 355-376. PMID: 27217599, PMCID: PMC4874534, DOI: 10.1080/01621459.2015.1008363.Peer-Reviewed Original ResearchLeast angle regressionGeneralized linear modelPrior informationExtension of LassoLinear modelPopular statistical toolWhole solution pathAsymptotic theoryAngle regressionEstimate parametersVariable selectionSolution pathStatistical toolsCriterion functionLASSOLASSO methodReal dataSimulation resultsGreater robustnessVariables of interestMisspecificationEstimatorModelBiomedical studiesVariables
2009
Graphical diagnostics to check model misspecification for the proportional odds regression model
Liu I, Mukherjee B, Suesse T, Sparrow D, Park S. Graphical diagnostics to check model misspecification for the proportional odds regression model. Statistics In Medicine 2009, 28: 412-429. PMID: 18693299, DOI: 10.1002/sim.3386.Peer-Reviewed Original ResearchConceptsCovariate effectsOrdinal responsesModel misspecificationProportional odds regression modelStudy covariate effectsGoodness-of-fit statisticsClass of modelsNumerical methodFunctional misspecificationBinary responsesGraphical diagnosticsSimulation studyCumulative logitsMisspecificationCumulative sumRegression modelsGraphical methodSumArbogastVA Normative Aging StudyCovariatesProportional odds regressionClass
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
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