2024
A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials.
Tong G, Nevins P, Ryan M, Davis-Plourde K, Ouyang Y, Pereira Macedo J, Meng C, Wang X, Caille A, Li F, Taljaard M. A review of current practice in the design and analysis of extremely small stepped-wedge cluster randomized trials. Clinical Trials 2024, 17407745241276137. PMID: 39377196, DOI: 10.1177/17407745241276137.Peer-Reviewed Original ResearchSmall-sample correctionsStepped-wedge cluster randomized trialCluster randomized trialSample size calculation methodGeneralized linear mixed modelsLongitudinal correlation structureSize calculation methodLinear mixed modelsPermutation testSample sizeBayesian approachRandomized trialsCorrelation structureMixed modelsBayesian analysisGeneralized estimating equationsPermutationMedian sample sizeIntervention conditionRandomization methodEquationsModel‐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 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 methodClassEvaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs
Moyer J, Li F, Cook A, Heagerty P, Pals S, Turner E, Wang R, Zhou Y, Yu Q, Wang X, Murray D. Evaluating analytic models for individually randomized group treatment trials with complex clustering in nested and crossed designs. Statistics In Medicine 2024 PMID: 39225281, DOI: 10.1002/sim.10206.Peer-Reviewed Original ResearchGroup treatmentRandomized group treatment trialsTreatment trialsDeliver treatmentNominal type I error rateData generating mechanismRating inflationType I error rateMultiple membershipsType I error rate inflationParticipantsAgent settingMultiple agentsOutcome measuresSingle agent settingTrial armsSimulation studyStudy designTherapistsStudy armsEvaluate analytical modelsContinuous outcomesA Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression
Oikonomou E, Holste G, Yuan N, Coppi A, McNamara R, Haynes N, Vora A, Velazquez E, Li F, Menon V, Kapadia S, Gill T, Nadkarni G, Krumholz H, Wang Z, Ouyang D, Khera R. A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression. JAMA Cardiology 2024, 9: 534-544. PMID: 38581644, PMCID: PMC10999005, DOI: 10.1001/jamacardio.2024.0595.Peer-Reviewed Original ResearchCardiac magnetic resonanceAortic valve replacementCardiac magnetic resonance imagingAV VmaxSevere ASAortic stenosisCohort studyPeak aortic valve velocityCohort study of patientsAortic valve velocityCohort of patientsTraditional cardiovascular risk factorsAssociated with faster progressionStudy of patientsCedars-Sinai Medical CenterAssociated with AS developmentCardiovascular risk factorsCardiovascular imaging modalitiesIndependent of ageModerate ASEjection fractionEchocardiographic studiesValve replacementRisk stratificationCardiac structureOptimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials.
Liu J, Li F. Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials. Stat Methods Med Res 2024, 9622802241247717. PMID: 38813761, DOI: 10.1177/09622802241247717.Peer-Reviewed Original ResearchMaintaining 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, 9622802241248382. PMID: 38807552, 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 objectiveSample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs
Wang X, Chen X, Goldfeld K, Taljaard M, Li F. Sample size and power calculation for testing treatment effect heterogeneity in cluster randomized crossover designs. Statistical Methods In Medical Research 2024, 33: 1115-1136. PMID: 38689556, PMCID: PMC11347095, DOI: 10.1177/09622802241247736.Peer-Reviewed Original ResearchCluster randomized crossover designSample size formulaTreatment effect heterogeneityAverage treatment effectHeterogeneity of treatment effectsSize formulaRandomized crossover designCluster-randomized crossover trialRandomized crossover trialEffect heterogeneitySampling schemeCluster randomized designTreatment effectsDifferential treatment effectsCrossover designFormulaContinuous outcomesLinear mixed modelsSample sizeCrossover trialInteraction testMixed modelsCovariatesClinical characteristicsStatistical methodsCausal interpretation of the hazard ratio in randomized clinical trials.
Fay M, Li F. Causal interpretation of the hazard ratio in randomized clinical trials. Clinical Trials 2024, 17407745241243308. PMID: 38679930, DOI: 10.1177/17407745241243308.Peer-Reviewed Original ResearchProportional hazards assumptionHazard ratioHazards assumptionConstant hazard ratioRandomized clinical trialsMeasure of treatment effectTime-varying effectsEstimandsRate ratiosUntestable assumptionsIndividual-levelPopulation-level interpretationCausal effectsClinical trialistsIndividual-level interpretationsClinical trialsAssumptionsCausal interpretationAverage changeTreatment effectsPotential outcomesReply to Heitjan's commentary.
Fay M, Li F. Reply to Heitjan's commentary. Clinical Trials 2024, 17407745241243311. PMID: 38679936, DOI: 10.1177/17407745241243311.Peer-Reviewed Original ResearchAssessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials
Blette B, Halpern S, Li F, Harhay M. Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials. Statistical Methods In Medical Research 2024, 33: 909-927. PMID: 38567439, PMCID: PMC11041086, DOI: 10.1177/09622802241242323.Peer-Reviewed Original ResearchConceptsMultilevel multiple imputationHeterogeneous treatment effectsCluster randomized trialPotential effect modifiersMultiple imputationAssess treatment effect heterogeneityEffect modifiersTreatment effect heterogeneityComplete-case analysisMissingness mechanismIntracluster correlationSimulation studyUnder-coverageRandomized trialsEffect heterogeneityHealth StudyTreatment effectsContinuous outcomesClinical practiceImputationModel specificationMissingnessData methodsModified dataTrialsDoubly 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 medicationsMultiply robust generalized estimating equations for cluster randomized trials with missing outcomes
Rabideau D, Li F, Wang R. Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes. Statistics In Medicine 2024, 43: 1458-1474. PMID: 38488532, DOI: 10.1002/sim.10027.Peer-Reviewed Original ResearchPropensity score modelMarginal regression parametersWeighted generalized estimating equationsRobust estimationCluster randomized trialRegression parametersMarginal meansMean modelIterative algorithmMonte Carlo simulationsGeneralized estimating equationsOutcome modelBotswana Combination Prevention ProjectCarlo simulationsEquationsCorrelation parametersEstimationReduce HIV incidenceHIV prevention measuresScore modelMultipliersRandomized trialsHIV incidencePrevention ProjectBayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event
Tian X, Ciarleglio M, Cai J, Greene E, Esserman D, Li F, Zhao Y. Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event. Journal Of The Royal Statistical Society Series C (Applied Statistics) 2024, 73: 598-620. PMID: 39072299, PMCID: PMC11271983, DOI: 10.1093/jrsssc/qlae003.Peer-Reviewed Original ResearchSemi-parametric inferenceRecurrent eventsAccelerated failure time modelFailure time modelEfficient sampling algorithmFrailty distributionDirichlet processPosterior inferenceSampling algorithmTime modelTerminal eventSurvival processesComplex data structuresDirichletInferenceData structureFall injury preventionAlgorithmA BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL.
Chen X, Harhay M, Tong G, Li F. A BAYESIAN MACHINE LEARNING APPROACH FOR ESTIMATING HETEROGENEOUS SURVIVOR CAUSAL EFFECTS: APPLICATIONS TO A CRITICAL CARE TRIAL. The Annals Of Applied Statistics 2024, 18: 350-374. PMID: 38455841, PMCID: PMC10919396, DOI: 10.1214/23-aoas1792.Peer-Reviewed Original ResearchAssociation of marital/partner status with hospital readmission among young adults with acute myocardial infarction.
Zhu C, Dreyer R, Li F, Spatz E, Caraballo C, Mahajan S, Raparelli V, Leifheit E, Lu Y, Krumholz H, Spertus J, D'Onofrio G, Pilote L, Lichtman J. Association of marital/partner status with hospital readmission among young adults with acute myocardial infarction. PLOS ONE 2024, 19: e0287949. PMID: 38277368, PMCID: PMC10817183, DOI: 10.1371/journal.pone.0287949.Peer-Reviewed Original ResearchConceptsMarital/partner statusPsychosocial factorsAcute myocardial infarctionYoung adultsHospital dischargeYear of hospital dischargeYoung acute myocardial infarctionAssociated with 1.3-foldCohort of young adultsLong-term readmissionCox proportional hazards modelsStatus interactionSimilar-aged menMyocardial infarctionProportional hazards modelUnpartnered statusPatient interviewsPhysician panelCardiovascular healthHospital readmissionSocioeconomic factorsAMI survivorsSequential adjustmentCardiac readmissionMultiple imputationTransporting randomized trial results to estimate counterfactual survival functions in target populations
Cao Z, Cho Y, Li F. Transporting randomized trial results to estimate counterfactual survival functions in target populations. Pharmaceutical Statistics 2024, 23: 442-465. PMID: 38233102, DOI: 10.1002/pst.2354.Peer-Reviewed Original ResearchSurvival functionFinite-sample performanceSample average treatment effectApproximate variance estimatorsIncorrect model specificationAverage treatment effectRight censoringDistributions of treatment effect modifiersVariance estimationInverse probability weightingSimulation studyRobust estimationComplex surveysAverage treatmentProbability weightingTreatment effect modifiersEstimationTreatment effectsModel specificationCensoringTarget populationDifferential distributionSurvey weightsEffect modifiersFunctionModel-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments
Wang B, Park C, Small D, Li F. Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments. Journal Of The American Statistical Association 2024, ahead-of-print: 1-13. DOI: 10.1080/01621459.2023.2289693.Peer-Reviewed Original ResearchCluster-randomized experimentCluster size variationNuisance functionsParametric working modelsFlexible covariate adjustmentCovariate-adjusted estimatesCovariate adjustment methodsCovariate adjustmentModel-based covariate adjustmentEfficient estimationSimulation studyRobust inferenceSupplementary materialsEstimandsEstimating equationsModel-based estimatesG-computationCluster averagesEstimationTreatment assignmentRoutine practice conditionsRisk of biasEquationsTreatment effectsCovariatesCorrection: Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials
Wang X, Goldfeld K, Taljaard M, Li F. Correction: Sample Size Requirements to Test Subgroup-Specific Treatment Effects in Cluster-Randomized Trials. Prevention Science 2024, 25: 1004-1004. PMID: 38180545, PMCID: PMC11390812, DOI: 10.1007/s11121-023-01615-0.Peer-Reviewed Original ResearchPatient Priorities–Aligned Care for Older Adults With Multiple Conditions
Tinetti M, Hashmi A, Ng H, Doyle M, Goto T, Esterson J, Naik A, Dindo L, Li F. Patient Priorities–Aligned Care for Older Adults With Multiple Conditions. JAMA Network Open 2024, 7: e2352666. PMID: 38261319, PMCID: PMC10807252, DOI: 10.1001/jamanetworkopen.2023.52666.Peer-Reviewed Original ResearchConceptsPatient Priorities CarePatient-Reported Outcomes Measurement Information SystemPerceived treatment burdenUsual careUC participantsOutcomes Measurement Information SystemPatients' health prioritiesTreatment burden scoresHealth outcome goalsHealth care preferencesMeasurement Information SystemTreatment burdenDecision quality measuresPrescribing decision-makingFollow-upNonrandomized controlled trialsPatient-reported outcomesAlign careCare preferencesPriority careMultisite practiceHealth professionalsHealth priorityChronic conditionsSocial roles