2025
How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators
Lee K, Li F. How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?—A Survey of Estimands and Common Estimators. Biometrical Journal 2025, 67: e70052. PMID: 40302411, PMCID: PMC12041842, DOI: 10.1002/bimj.70052.Peer-Reviewed Original ResearchConceptsInformative cluster sizeIndependence estimating equationsCluster-period sizesParallel cluster randomized trialsTreatment effect estimatesCluster randomized trialInconsistent estimatesSimulation studyEstimandsEstimating EquationsCluster sizeContinuous outcomesEstimationTreatment effectsEffect estimatesImprove mental healthRandomized trialsConvergenceEquationsRural eastern IndiaMental healthMixed-effects modelsYouth teamsNonparametric identification is not enough, but randomized controlled trials are
Aronow P, Robins J, Saarinen T, Sävje F, Sekhon J. Nonparametric identification is not enough, but randomized controlled trials are. Observational Studies 2025, 11: 3-16. PMID: 40487083, PMCID: PMC12139723, DOI: 10.1353/obs.2025.a956837.Peer-Reviewed Original ResearchPropensity score functionResults of RobinsValid confidence intervalsAverage treatment effectParametric rateScoring functionContinuous confoundersNonparametric identificationUnconfoundedness assumptionBinary outcomesStatistical estimationObservational settingPropensity scoreAssumptionsEstimationConfidence intervalsPropensityRobinTreatment effectsFunctionInferenceRejoinder: Nonparametric identification is not enough, but randomized controlled trials are.
Aronow P, Robins J, Saarinen T, Sävje F, Sekhon J. Rejoinder: Nonparametric identification is not enough, but randomized controlled trials are. Observational Studies 2025, 11: 85-90. PMID: 40487084, PMCID: PMC12139717, DOI: 10.1353/obs.2025.a956844.Peer-Reviewed Original ResearchRoot-nPropensity score functionRoot-n rateConditional expectation functionFinite-sampleContinuous covariatesNonparametric identificationExpectation functionUntestable assumptionsRandomized experimentScoring functionPotential outcomesEstimated averageSample sizeEstimationPropensity scoreConfidence intervalsPropensityAssumptionsCovariatesFunctionUniformityRemarksOutcome 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 methodsWeighting methods for truncation by death in cluster-randomized trials
Isenberg D, Harhay M, Mitra N, Li F. Weighting methods for truncation by death in cluster-randomized trials. Statistical Methods In Medical Research 2025, 34: 473-489. PMID: 39885759, PMCID: PMC11951466, DOI: 10.1177/09622802241309348.Peer-Reviewed Original ResearchConceptsSurvivor average causal effectAverage causal effectCluster randomized trialAsymptotic variance estimatorsSubgroup treatment effectsCausal effectsPrincipal stratification frameworkFinite-sampleVariance estimationDistributional assumptionsIdentification assumptionsStratification frameworkPatient-centered outcomesNon-mortality outcomesOutcome modelQuality of lifeRandomized trialsIll patient populationMeasurement time pointsTruncationEstimationLength of hospital stayAssumptionsSurvivorsPatient population
2024
Distributions and Their Approximations for p-Values
Zhou N, Blaha O, Zelterman D. Distributions and Their Approximations for p-Values. 2024, 481-509. DOI: 10.1007/978-3-031-65937-9_16.Peer-Reviewed Original ResearchDistribution of p-valuesStudent-t distributionTesting multiple hypothesesT-distributionAlternative distributionsIdentified hypothesisSimulation studySignificant hypothesesDependent samplesApproximationAlternative hypothesisP-valueLack of independenceMultiple hypothesesExamplesIterationMultiplicationDistributionEstimationStatistical Methods for Accommodating Immortal Time: A Selective Review and Comparison
Wang J, Peduzzi P, Wininger M, Ma S. Statistical Methods for Accommodating Immortal Time: A Selective Review and Comparison. 2024, 53-92. DOI: 10.1007/978-3-031-65937-9_3.Peer-Reviewed Original ResearchIntegrative factor-adjusted sparse generalized linear models
Xu F, Ma S, Zhang Q. Integrative factor-adjusted sparse generalized linear models. Journal Of Statistical Computation And Simulation 2024, 95: 764-780. DOI: 10.1080/00949655.2024.2439450.Peer-Reviewed Original ResearchVariable selection consistencyHigh-dimensional dataIncreased accessibility of dataSelection consistencyConsistency propertiesCorrelated covariatesGeneralized linear modelVariable selectionAnalysis of genetic dataAccessibility of dataIdiosyncratic componentsCompetitive performanceCovariatesGenetic dataLinear modelSample sizeImprove model performanceEstimationIntegrated analysisModel estimatesLatent factorsModel performancePractical useConsistencyOn Optimality of Mallows Model Averaging
Peng J, Li Y, Yang Y. On Optimality of Mallows Model Averaging. Journal Of The American Statistical Association 2024, 120: 1152-1163. DOI: 10.1080/01621459.2024.2402566.Peer-Reviewed Original ResearchNon-nested setAsymptotic optimalityModel selectionCp criterionMallows model averageCandidate modelsOptimal convex combinationMallows' Cp criterionMinimax adaptiveSampling inequalitiesMallows modelConvex combinationTheoretical findingsOptimal riskSupplementary materialsMA estimatesModel averagingTheoretical justificationModel weightsAverage modelNested setMild conditionsMinimaxEstimationInequalityInverting estimating equations for causal inference on quantiles
Cheng C, Li F. Inverting estimating equations for causal inference on quantiles. Biometrika 2024, 112: asae058. DOI: 10.1093/biomet/asae058.Peer-Reviewed Original ResearchUsing Overlap Weights to Address Extreme Propensity Scores in Estimating Restricted Mean Counterfactual Survival Times
Cao Z, Ghazi L, Mastrogiacomo C, Forastiere L, Wilson F, Li F. Using Overlap Weights to Address Extreme Propensity Scores in Estimating Restricted Mean Counterfactual Survival Times. American Journal Of Epidemiology 2024, kwae416. PMID: 39489504, DOI: 10.1093/aje/kwae416.Peer-Reviewed Original ResearchInverse probability of censoring weightingProbability of censoring weightingOverlap weightingCensoring processVariance estimationInterval coverageInverse probability of treatment weightingTarget estimandInverse probabilityBinary outcomesPropensity scoreRMSTProbability of treatment weightingPropensity score weightingEstimationEstimandsLogistic regressionTreatment comparisonsVarianceHow 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 modelsModel‐assisted analysis of covariance estimators for stepped wedge cluster randomized experiments
Chen X, Li F. Model‐assisted analysis of covariance estimators for stepped wedge cluster randomized experiments. Scandinavian Journal Of Statistics 2024, 52: 416-446. DOI: 10.1111/sjos.12755.Peer-Reviewed Original ResearchCluster-randomized experimentANCOVA estimatesFinite population central limit theoremAnalysis of covariance estimatorCentral limit theoremLimit theoremPotential outcomes frameworkCovariance estimationRandomized experimentTarget estimandEstimandsRandomization schemeCovariate adjustmentEstimationTheoremData structureOutcomes FrameworkMultilevel data structureCovariatesRobust methodClassImproving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators
Han P, Li H, Park S, Mukherjee B, Taylor J. Improving prediction of linear regression models by integrating external information from heterogeneous populations: James–Stein estimators. Biometrics 2024, 80: ujae072. PMID: 39101548, PMCID: PMC11299067, DOI: 10.1093/biomtc/ujae072.Peer-Reviewed Original ResearchConceptsJames-Stein estimatorLinear regression modelsIndividual-level dataComprehensive simulation studyRegression modelsNumerical performanceSimulation studyShrinkage methodCoefficient estimatesPredictive meanReduced modelStudy population heterogeneityInternal modelEstimationStudy populationBlood lead levelsInternational studiesCovariatesPatella bonePublished literatureLead levelsExternal studiesSummary informationPopulationSubsetsMultiply robust estimation of principal causal effects with noncompliance and survival outcomes
Cheng C, Guo Y, Liu B, Wruck L, Li F, Li F. Multiply robust estimation of principal causal effects with noncompliance and survival outcomes. Clinical Trials 2024, 21: 553-561. PMID: 38813813, DOI: 10.1177/17407745241251773.Peer-Reviewed Original ResearchConceptsPrincipal strataRight-censored survival outcomesPrincipal causal effectsCausal effectsSensitivity analysis strategyPrincipal ignorabilityRobust estimationIdentification assumptionsCensoringPragmatic clinical trialsTreatment assignmentTreatment noncomplianceMonotonicityEstimationAssess treatment effectsCardiovascular diseaseClinical trialsMultipliersTreatment effectsAssumptionsNoncomplianceMaintaining 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 errorDemystifying estimands in cluster-randomised trials
Kahan B, Blette B, Harhay M, Halpern S, Jairath V, Copas A, Li F. Demystifying estimands in cluster-randomised trials. Statistical Methods In Medical Research 2024, 33: 1211-1232. PMID: 38780480, PMCID: PMC11348634, DOI: 10.1177/09622802241254197.Peer-Reviewed Original ResearchCluster randomised trialPotential outcomes notationTreatment effect estimatesOverview of estimationPublished cluster randomised trialsCluster-level summariesTarget estimandEstimandsTreatment effectsEffect estimatesInterpretation of treatment effectsOdds ratioEstimationRandomised trialsStudy objectiveOptimization in Visual Motion Estimation
Clark D, Fitzgerald J. Optimization in Visual Motion Estimation. Annual Review Of Vision Science 2024, 10: 23-46. PMID: 38663426, PMCID: PMC11998607, DOI: 10.1146/annurev-vision-101623-025432.Peer-Reviewed Original ResearchDoubly robust estimation and sensitivity analysis for marginal structural quantile models
Cheng C, Hu L, Li F. Doubly robust estimation and sensitivity analysis for marginal structural quantile models. Biometrics 2024, 80: ujae045. PMID: 38884127, DOI: 10.1093/biomtc/ujae045.Peer-Reviewed Original ResearchConceptsQuantile modelDistribution of potential outcomesEfficient influence functionPotential outcome distributionsDoubly robust estimatorsTime-varying treatmentsSequential ignorability assumptionSemiparametric frameworkIgnorability assumptionVariance estimationOutcome distributionInfluence functionRobust estimationPotential outcomesEfficient computationFunction approachTime-varying confoundersElectronic health record dataEstimationTreatment assignmentHealth record dataEffect of antihypertensive medicationEquationsRecord dataAntihypertensive medicationsInterpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease
Lila E, Zhang W, Levendovszky S, Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Jack C, Jagust W, Trojanowki J, Toga A, Beckett L, Green R, Saykin A, Morris J, Perrin R, Shaw L, Khachaturian Z, Carrillo M, Potter W, Barnes L, Bernard M, Ho C, Hsiao J, Jackson J, Masliah E, Masterman D, Okonkwo O, Perrin R, Ryan L, Silverberg N, Fleisher A, Weiner M, Fockler J, Conti C, Veitch D, Neuhaus J, Jin C, Nosheny R, Ashford M, Flenniken D, Kormos A, Green R, Montine T, Conti C, Petersen R, Aisen P, Rafii M, Raman R, Jimenez G, Donohue M, Gessert D, Salazar J, Zimmerman C, Cabrera Y, Walter S, Miller G, Coker G, Clanton T, Hergesheimer L, Smith S, Adegoke O, Mahboubi P, Moore S, Pizzola J, Shaffer E, Sloan B, Beckett L, Harvey D, Donohue M, Jack C, Forghanian-Arani A, Borowski B, Ward C, Schwarz C, Jones D, Gunter J, Kantarci K, Senjem M, Vemuri P, Reid R, Fox N, Malone I, Thompson P, Thomopoulos S, Nir T, Jahanshad N, DeCarli C, Knaack A, Fletcher E, Harvey D, Tosun-Turgut D, Chen S, Choe M, Crawford K, Yushkevich P, Das S, Jagust W, Koeppe R, Reiman E, Chen K, Mathis C, Landau S, Morris J, Perrin R, Cairns N, Householder E, Franklin E, Bernhardt H, Taylor-Reinwald L, Shaw L, Trojanowki J, Korecka M, Figurski M, Toga A, Crawford K, Neu S, Saykin A, Nho K, Risacher S, Apostolova L, Shen L, Foroud T, Nudelman K, Faber K, Wilmes K, Weiner M, Thal L, Khachaturian Z, Hsiao J, Silbert L, Lind B, Crissey R, Kaye J, Carter R, Dolen S, Quinn J, Schneider L, Pawluczyk S, Becerra M, Teodoro L, Dagerman K, Spann B, Brewer J, Vanderswag H, Fleisher A, Ziolkowski J, Heidebrink J, Zbizek-Nulph L, Lord J, Zbizek-Nulph L, Petersen R, Mason S, Albers C, Knopman D, Johnson K, Villanueva-Meyer J, Pavlik V, Pacini N, Lamb A, Kass J, Doody R, Shibley V, Chowdhury M, Rountree S, Dang M, Stern Y, Honig L, Mintz A, Ances B, Morris J, Winkfield D, Carroll M, Stobbs-Cucchi G, Oliver A, Creech M, Mintun M, Schneider S, Geldmacher D, Love M, Griffith R, Clark D, Brockington J, Marson D, Grossman H, Goldstein M, Greenberg J, Mitsis E, Shah R, Lamar M, Samuels P, Duara R, Greig-Custo M, Rodriguez R, Albert M, Onyike C, Farrington L, Rudow S, Brichko R, Kielb S, Smith A, Raj B, Fargher K, Sadowski M, Wisniewski T, Shulman M, Faustin A, Rao J, Castro K, Ulysse A, Chen S, Sheikh M, Singleton-Garvin J, Doraiswamy P, Petrella J, James O, Wong T, Borges-Neto S, Karlawish J, Wolk D, Vaishnavi S, Clark C, Arnold S, Smith C, Jicha G, Khouli R, Raslau F, Lopez O, Oakley M, Simpson D, Porsteinsson A, Martin K, Kowalski N, Keltz M, Goldstein B, Makino K, Ismail M, Brand C, Thai G, Pierce A, Yanez B, Sosa E, Witbracht M, Kelley B, Nguyen T, Womack K, Mathews D, Quiceno M, Levey A, Lah J, Hajjar I, Cellar J, Burns J, Swerdlow R, Brooks W, Silverman D, Kremen S, Apostolova L, Tingus K, Lu P, Bartzokis G, Woo E, Teng E, Graff-Radford N, Parfitt F, Poki-Walker K, Farlow M, Hake A, Matthews B, Brosch J, Herring S, van Dyck C, Mecca A, Mecca A, Good S, MacAvoy M, Carson R, Varma P, Chertkow H, Vaitekunis S, Hosein C, Black S, Stefanovic B, Heyn C, Hsiung G, Kim E, Mudge B, Sossi V, Feldman H, Assaly M, Finger E, Pasternak S, Rachinsky I, Kertesz A, Drost D, Rogers J, Grant I, Muse B, Rogalski E, Robson J, Mesulam M, Kerwin D, Wu C, Johnson N, Lipowski K, Weintraub S, Bonakdarpour B, Pomara N, Hernando R, Sarrael A, Rosen H, Miller B, Perry D, Turner R, Johnson K, Reynolds B, MCCann K, Poe J, Sperling R, Johnson K, Marshall G, Yesavage J, Taylor J, Chao S, Coleman J, White J, Lane B, Rosen A, Tinklenberg J, Belden C, Atri A, Spann B, Clark K, Zamrini E, Sabbagh M, Killiany R, Stern R, Mez J, Kowall N, Budson A, Obisesan T, Ntekim O, Wolday S, Khan J, Nwulia E, Nadarajah S, Lerner A, Ogrocki P, Tatsuoka C, Fatica P, Fletcher E, Maillard P, Olichney J, DeCarli C, Carmichael O, Bates V, Capote H, Rainka M, Borrie M, Lee T, Bartha R, Johnson S, Asthana S, Carlsson C, Perrin A, Burke A, Scharre D, Kataki M, Tarawneh R, Kelley B, Hart D, Zimmerman E, Celmins D, Miller D, Ponto L, Smith K, Koleva H, Shim H, Nam K, Schultz S, Williamson J, Craft S, Cleveland J, Yang M, Sink K, Ott B, Drake J, Tremont G, Daiello L, Drake J, Sabbagh M, Ritter A, Bernick C, Munic D, Mintz A, O’Connelll A, Mintzer J, Wiliams A, Masdeu J, Shi J, Garcia A, Sabbagh M, Newhouse P, Potkin S, Salloway S, Malloy P, Correia S, Kittur S, Pearlson G, Blank K, Anderson K, Flashman L, Seltzer M, Hynes M, Santulli R, Relkin N, Chiang G, Lin M, Ravdin L, Lee A, Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Green R, Harvey D, Jack C, Jagust W, Morris J, Saykin A, Shaw L, Toga A, Trojanowki J, Neylan T, Grafman J, Green R, Montine T, Weiner M, Petersen R, Aisen P, Jimenez G, Donohue M, Gessert D, Salazar J, Zimmerman C, Walter S, Adegoke O, Mahboubi P, Hergesheimer L, Danowski S, Coker G, Clanton T, Pizzola J, Shaffer E, Nguyen-Barrera C, Neylan T, Hayes J, Finley S, Harvey D, Donohue M, Jack C, Bernstein M, Borowski B, Gunter J, Senjem M, Kantarci K, Ward C, Tosun-Turgut D, Chen S, Landau S, Koeppe R, Foster N, Reiman E, Chen K, Morris J, Perrin R, Franklin E, Shaw L, Trojanowki J, Korecka M, Figurski M, Toga A, Neu S, Saykin A, Foroud T, Potkin S, Shen L, Faber K, Kim S, Nho K, Wilmes K, Schneider L, Pawluczyk S, Becerra M, Teodoro L, Dagerman K, Spann B, Brewer J, Vanderswag H, Fleisher A, Stern Y, Honig L, Mintz A, Shah R, Sood A, Blanchard K, Fleischman D, Arfanakis K, Duara R, Varon D, Greig M, Doraiswamy P, Petrella J, James O, Borges-Neto S, Wong T, Porsteinsson A, Goldstein B, Martin K, Thai G, Pierce A, Reist C, Yanez B, Sosa E, Witbracht M, Sadowsky C, Martinez W, Villena T, Rosen H, Perry D, Turner R, Johnson K, Reynolds B, MCCann K, Poe J, Sperling R, Johnson K, Marshall G, Belden C, Atri A, Spann B, Clark K, Zamrini E, Sabbagh M, Obisesan T, Ntekim O, Wolday S, Nwulia E, Nadarajah S, Johnson S, Asthana S, Carlsson C, Peskind E, Petrie E, Li G, Yesavage J, Taylor J, Chao S, Coleman J, White J, Lane B, Rosen A, Tinklenberg J, Lin M, Chiang G, Ravdin L, Relkin N, O’Connelll A, Mintzer J, Wiliams A, Mackin S, Aisen P, Raman R, Jimenez-Maggiora G, Donohue M, Gessert D, Salazar J, Zimmerman C, Walter S, Adegoke O, Mahboubi P, Mackin S, Weiner M, Aisen P, Raman R, Jack C, Landau S, Saykin A, Toga A, DeCarli C, Koeppe R, Green R, Drake E, Weiner M, Aisen P, Raman R, Donohue M, Mackin S, Nelson C, Bickford D, Butters M, Zmuda M, Jack C, Bernstein M, Borowski B, Gunter J, Senjem M, Kantarci K, Ward C, Reyes D, Koeppe R, Landau S, Toga A, Crawford K, Neu S, Saykin A, Foroud T, Faber K, Nho K, Nudelman K, Mackin S, Rosen H, Nelson C, Bickford D, Au Y, Scherer K, Catalinotto D, Stark S, Ong E, Fernandez D, Butters M, Zmuda M, Lopez O, Oakley M, Simpson D. Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer’s disease. Journal Of The Royal Statistical Society Series B Statistical Methodology 2024, 86: 1013-1044. PMID: 39279915, PMCID: PMC11398888, DOI: 10.1093/jrsssb/qkae023.Peer-Reviewed Original ResearchFunctional linear regression modelClassification of functional dataOut-of-sample prediction errorsFunctional predictorsFunctional dataCovariance structureDifferential regularizationCortical surface geometryClassification problemDiscriminant directionsManifold domainsParkinson's Progression Markers InitiativeNonlinear domainProgression Markers InitiativePrediction errorLinear regression modelsAlzheimer's Disease Neuroimaging InitiativeTheoretical analysisDiscriminant analysisSimulation settingsAlzheimer's diseaseClassificationEstimationFeatures of Alzheimer's diseaseNeuroscience literature
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