Fan Li, PhD
Associate Professor of BiostatisticsDownloadHi-Res Photo
Cards
Contact Info
About
Titles
Associate Professor of Biostatistics
Biography
Dr. Fan Li is an Associate Professor in the Department of Biostatistics at the Yale School of Public Health. He received his PhD in Biostatistics from Duke University in 2019, and joined the Yale Biostatistics faculty in July, 2019.
Dr. Li’s research interests include statistical methods for randomized clinical trials, observational studies and a combination of both. He is an expert in the design, monitoring, analysis of parallel-arm, crossover and stepped-wedge cluster randomized trials, which are increasingly seen in pragmatic clinical trials embedded in the health care delivery systems. He has also contributed novel propensity score methods and software to estimate average causal effects with observational data, aimed at improving overlap and internal validity. His recent methods research include generalizability of randomized trials to external target populations, confirmatory or exploratory heterogeneity of treatment effects analyses, complex endpoints in cluster randomized trials, as well as novel study designs to address patient-centered clinical research questions. His methodological research has been supported by multiple NIH and PCORI grants/awards.
Appointments
Biostatistics
Associate Professor on TermPrimary
Other Departments & Organizations
Education & Training
- PhD
- Duke University, Biostatistics (2019)
Research
Overview
Medical Subject Headings (MeSH)
Causality; Comparative Effectiveness Research; Epidemiologic Methods; Longitudinal Studies; Machine Learning; Mediation Analysis; Multilevel Analysis; Observational Study; Pragmatic Clinical Trial; Propensity Score; Randomized Controlled Trial; Research Design; Selection Bias; Survival Analysis
ORCID
0000-0001-6183-1893- View Lab Website
Personal Website
Research at a Glance
Yale Co-Authors
Frequent collaborators of Fan Li's published research.
Publications Timeline
A big-picture view of Fan Li's research output by year.
Research Interests
Research topics Fan Li is interested in exploring.
Guangyu Tong, PhD
F. Perry Wilson, MD, MSCE
Denise Esserman, PhD
Donna Spiegelman, ScD
Ondrej Blaha, PhD
Can Meng, MS, MPH
130Publications
2,061Citations
Research Design
Longitudinal Studies
Propensity Score
Mediation Analysis
Causality
Machine Learning
Publications
2024
A 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 ResearchCitationsAltmetricConceptsCardiac 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 ResearchCitationsAltmetricConceptsRandom 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, 9622802241254197. PMID: 38780480, DOI: 10.1177/09622802241254197.Peer-Reviewed Original ResearchCitationsAltmetricConceptsCluster 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, 9622802241247736. PMID: 38689556, DOI: 10.1177/09622802241247736.Peer-Reviewed Original ResearchConceptsCluster 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 ResearchAltmetricConceptsProportional 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 ResearchAltmetricMeSH Keywords and ConceptsConceptsMultilevel 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 ResearchAltmetricMeSH Keywords and ConceptsConceptsQuantile 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 ResearchAltmetricConceptsPropensity 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 Project
Academic Achievements & Community Involvement
activity Statistics in Medicine
Journal ServiceAssociate EditorDetails03/01/2020 - Presentactivity Clinical Trials (Journal of the Society for Clinical Trials)
Journal ServiceAssociate EditorDetails08/01/2020 - Presentactivity Implementation Science
Journal ServiceEditorial Board MemberDetails04/04/2022 - Presentactivity Epidemiologic Methods
Journal ServiceEditor-in-ChiefDetails2024 - Presenthonor Early Career Investigator Research Award
Yale School of Medicine AwardYale School of Public HealthDetails05/26/2022United States
News & Links
News
- February 05, 2024
Patient Priorities Care Shows Potential for Improving Outcomes for Older Adults With Multiple Chronic Conditions
- March 21, 2022
Yale Center for Methods in Implementation and Prevention Science Faculty Member Dr. Fan Li Joins Editorial Board for the Journal Implementation Science
- March 16, 2022
Yale CMIPS Faculty Member Dr. Fan Li Receives PCORI Grant to Develop New Methods for Planning Cluster Randomized Trials
- September 10, 2021Source: NIH Collaboratory of Pragmatic Clinical Trials
Fan Li Receives PCORI Award to Study Methods for Cluster Randomized Trials
Social Media
Get In Touch
Contacts
Email
Academic Office Number
Locations
135 College Street
Academic Office
Ste Suite 200, Rm Room 229
New Haven, CT 06510