2024
Assessing 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 medicationsAssociation 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 imputationPatient 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
2023
Group sequential two‐stage preference designs
Liu R, Li F, Esserman D, Ryan M. Group sequential two‐stage preference designs. Statistics In Medicine 2023, 43: 315-341. PMID: 38010193, DOI: 10.1002/sim.9962.Peer-Reviewed Original ResearchDesigning individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations
Wang X, Turner E, Li F. Designing individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations. Statistics In Medicine 2023, 43: 358-378. PMID: 38009329, PMCID: PMC10939061, DOI: 10.1002/sim.9966.Peer-Reviewed Original ResearchConceptsSample size proceduresConstant treatment effectCorrelation structureSize proceduresMarginal mean modelClosed-form sample size formulaCorrelation parametersSandwich variance estimatorGroup treatment trialsEquation approachExchangeable correlation structureSample size formulaBinary outcomesVariance estimatorEmpirical powerLinear timeMean modelCorrelation matrixDifferent correlation parametersEstimating EquationsSize formulaEquationsSample size calculationDifferent assumptionsProper sample size calculationA mixed model approach to estimate the survivor average causal effect in cluster‐randomized trials
Wang W, Tong G, Hirani S, Newman S, Halpern S, Small D, Li F, Harhay M. A mixed model approach to estimate the survivor average causal effect in cluster‐randomized trials. Statistics In Medicine 2023, 43: 16-33. PMID: 37985966, DOI: 10.1002/sim.9939.Peer-Reviewed Original ResearchSample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes
Maleyeff L, Wang R, Haneuse S, Li F. Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes. Statistics In Medicine 2023, 42: 5054-5083. PMID: 37974475, PMCID: PMC10659142, DOI: 10.1002/sim.9901.Peer-Reviewed Original ResearchConceptsSample size proceduresSize proceduresEfficient Monte Carlo approachTreatment effect heterogeneitySample size methodsMonte Carlo approachContinuous effect modifiersBinary outcomesEffect heterogeneityCarlo approachNumerical illustrationsNecessary sample sizeGeneralized linear mixed modelLinear mixed modelsPopular classSample size requirementsStatistical powerAverage treatment effectHeterogeneous treatment effectsSample size calculationMixed modelsSize methodSize calculationSize requirementsCluster Randomized TrialImpact of Marital Stress on 1‐Year Health Outcomes Among Young Adults With Acute Myocardial Infarction
Zhu C, Dreyer R, Li F, Spatz E, Caraballo‐Cordovez C, Mahajan S, Raparelli V, Leifheit E, Lu Y, Krumholz H, Spertus J, D'Onofrio G, Pilote L, Lichtman J. Impact of Marital Stress on 1‐Year Health Outcomes Among Young Adults With Acute Myocardial Infarction. Journal Of The American Heart Association 2023, 12: e030031. PMID: 37589125, PMCID: PMC10547344, DOI: 10.1161/jaha.123.030031.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionCardiac-specific qualityGeneric health statusMyocardial infarctionBaseline healthMarital stressHealth outcomesHealth statusWorse patient-reported outcomesMental healthYoung adultsObservational cohort studyPatient-reported outcomesSocioeconomic factorsWorse mental healthReadmission 1Cause readmissionCohort studyYounger patientsRoutine screeningDepressive symptomsGreater oddsAnginaMale participantsOutcomesInformative cluster size in cluster-randomised trials: A case study from the TRIGGER trial
Kahan B, Li F, Blette B, Jairath V, Copas A, Harhay M. Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial. Clinical Trials 2023, 20: 661-669. PMID: 37439089, PMCID: PMC10638852, DOI: 10.1177/17407745231186094.Peer-Reviewed Original ResearchConceptsCluster-randomised trialCluster-level summariesAcute upper gastrointestinal bleedingExchangeable correlation structureRed blood cell transfusionEQ-5D VAS scoreMixed-effects modelsUpper gastrointestinal bleedingBlood cell transfusionMixed effects modelsTreatment effectsCell transfusionGastrointestinal bleedingIschemic eventsVAS scoresOdds ratioMost outcomesTRIGGER trialTreatment effect estimatesEffect estimatesInformative cluster sizeTrialsOutcomesParticipant outcomesCorrelation structureMaximin optimal cluster randomized designs for assessing treatment effect heterogeneity
Ryan M, Esserman D, Li F. Maximin optimal cluster randomized designs for assessing treatment effect heterogeneity. Statistics In Medicine 2023, 42: 3764-3785. PMID: 37339777, PMCID: PMC10510425, DOI: 10.1002/sim.9830.Peer-Reviewed Original ResearchSample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances
Tong G, Taljaard M, Li F. Sample size considerations for assessing treatment effect heterogeneity in randomized trials with heterogeneous intracluster correlations and variances. Statistics In Medicine 2023, 42: 3392-3412. PMID: 37316956, DOI: 10.1002/sim.9811.Peer-Reviewed Original ResearchConceptsGroup treatment trialsTreatment effect modificationRandomized trialsTreatment trialsEffect modificationEffect modifiersIntracluster correlation coefficientIndividual-level effect modifiersStudy armsTreatment effect heterogeneityOutcome observationsContinuous outcomesTrialsGroup treatmentTreatment effectsOutcome varianceEffect heterogeneityIntracluster correlationSample sizeSample size formulaIs low-risk status a surrogate outcome in pulmonary arterial hypertension? An analysis of three randomised trials
Blette B, Moutchia J, Al-Naamani N, Ventetuolo C, Cheng C, Appleby D, Urbanowicz R, Fritz J, Mazurek J, Li F, Kawut S, Harhay M. Is low-risk status a surrogate outcome in pulmonary arterial hypertension? An analysis of three randomised trials. The Lancet Respiratory Medicine 2023, 11: 873-882. PMID: 37230098, PMCID: PMC10592525, DOI: 10.1016/s2213-2600(23)00155-8.Peer-Reviewed Original ResearchConceptsPulmonary arterial hypertensionPulmonary arterial hypertension trialsWorsening pulmonary arterial hypertensionFood and Drug AdministrationLow-risk statusClinical worseningLong-term outcomesRisk scoreArterial hypertensionPAH associated with connective tissue diseaseIdiopathic pulmonary arterial hypertensionPulmonary arterial hypertension treatmentSurrogate outcomesObservational study of outcomesLong-term follow-upDiscontinuation of study treatmentWHO functional classUS Food and Drug AdministrationMeta-analysisMeta-analysis of RCTsAll-cause deathConnective tissue diseaseEffects of therapyPredictive of outcomeTreatment effectsA randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes
Wilson F, Yamamoto Y, Martin M, Coronel-Moreno C, Li F, Cheng C, Aklilu A, Ghazi L, Greenberg J, Latham S, Melchinger H, Mansour S, Moledina D, Parikh C, Partridge C, Testani J, Ugwuowo U. A randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes. Nature Communications 2023, 14: 2826. PMID: 37198160, PMCID: PMC10192367, DOI: 10.1038/s41467-023-38532-3.Peer-Reviewed Original ResearchConceptsAcute kidney injuryUsual care groupKidney injuryCare groupAcute Kidney Injury OutcomesAlert groupNon-steroidal anti-inflammatory drugsComposite of progressionHours of randomizationMedications of interestAldosterone system inhibitorsClasses of medicationsProton pump inhibitorsRandomized clinical trialsAnti-inflammatory drugsClinical decision support systemNephrotoxic medicationsHospitalized adultsDiscontinuation ratesCertain medicationsPrimary outcomeSubstantial morbiditySystem inhibitorsPump inhibitorsParallel groupORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals
Meng C, Ryan M, Rathouz P, Turner E, Preisser J, Li F. ORTH.Ord: An R package for analyzing correlated ordinal outcomes using alternating logistic regressions with orthogonalized residuals. Computer Methods And Programs In Biomedicine 2023, 237: 107567. PMID: 37207384, DOI: 10.1016/j.cmpb.2023.107567.Peer-Reviewed Original ResearchConceptsOrdinal outcomesSandwich estimatorR packageSimulation studyCorrelated ordinal dataFinite sample biasesNumber of clustersCovariance estimationMarginal modelsEquationsParameter estimatesOrdinal responsesAssociation parametersCluster associationsBias correctionOrdinal dataEstimatorEstimating EquationsNominal levelMarginal meansResidualsEstimationPairwise odds ratiosAssociation modelGEE modelCausal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia
Blette B, Granholm A, Li F, Shankar-Hari M, Lange T, Munch M, Møller M, Perner A, Harhay M. Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia. Scientific Reports 2023, 13: 6570. PMID: 37085591, PMCID: PMC10120498, DOI: 10.1038/s41598-023-33425-3.Peer-Reviewed Original ResearchConceptsCritical COVID-19Better long-term outcomesCOVID-19Entire trial populationStandardized dosing protocolsMultiple patient characteristicsDose of dexamethasoneLong-term outcomesIL-6 inhibitorsDexamethasone doseSevere hypoxemiaMost patientsPatient characteristicsRespiratory supportDiabetes mellitusClinical outcomesDosing protocolTrial populationTreatment effect heterogeneityPatient featuresPatientsAdditional studiesDexamethasoneDoseMore evidenceAccounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity
Tong J, Li F, Harhay M, Tong G. Accounting for expected attrition in the planning of cluster randomized trials for assessing treatment effect heterogeneity. BMC Medical Research Methodology 2023, 23: 85. PMID: 37024809, PMCID: PMC10077680, DOI: 10.1186/s12874-023-01887-8.Peer-Reviewed Original ResearchConceptsSample size methodsSample size proceduresSize proceduresTreatment effect heterogeneityHeterogeneous treatment effectsSize methodMissingness ratesSample size formulaSample size estimationMissingness indicatorsEffect heterogeneityReal-world examplesSimulation studyIntracluster correlation coefficientInflation methodSize formulaAverage treatment effectResultsSimulation resultsSample size estimatesSize estimationMissingnessSample sizeClustersEstimationFormulaAccounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control
Ouyang Y, Kulkarni M, Protopopoff N, Li F, Taljaard M. Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control. BMC Medical Research Methodology 2023, 23: 64. PMID: 36932347, PMCID: PMC10021932, DOI: 10.1186/s12874-023-01871-2.Peer-Reviewed Original ResearchA scoping review described diversity in methods of randomization and reporting of baseline balance in stepped-wedge cluster randomized trials
Nevins P, Davis-Plourde K, Pereira Macedo J, Ouyang Y, Ryan M, Tong G, Wang X, Meng C, Ortiz-Reyes L, Li F, Caille A, Taljaard M. A scoping review described diversity in methods of randomization and reporting of baseline balance in stepped-wedge cluster randomized trials. Journal Of Clinical Epidemiology 2023, 157: 134-145. PMID: 36931478, PMCID: PMC10546924, DOI: 10.1016/j.jclinepi.2023.03.010.Peer-Reviewed Original ResearchConceptsStepped-wedge clusterIndividual-level characteristicsMethod of randomizationCross-sectional designControl armBaseline imbalancesCohort designMedian numberElectronic searchPrimary analysisBaseline balanceStudy designPrimary reportsBaselineTrialsIntervention conditionSW-CRTsRandomizationReportingMediation analysis in the presence of continuous exposure measurement error
Cheng C, Spiegelman D, Li F. Mediation analysis in the presence of continuous exposure measurement error. Statistics In Medicine 2023, 42: 1669-1686. PMID: 36869626, PMCID: PMC11320713, DOI: 10.1002/sim.9693.Peer-Reviewed Original ResearchConceptsBody mass indexExposure measurement errorPhysical activityMediation proportionHealth Professionals FollowCardiovascular disease incidenceProfessionals FollowMediation analysisMass indexCardiovascular diseaseLower riskStudy designEffect estimatesValidation study designContinuous exposureBiased effect estimatesTrue exposureMediatorsExposureValidation studyBinary outcomesHealth science studiesOutcomesRiskDisease incidence