Fan Li, PhD
Associate Professor of BiostatisticsDownloadHi-Res Photo
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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
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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
Can Meng, MS, MPH
Denise Esserman, PhD
Donna Spiegelman, ScD
Kendra Plourde, PhD
136Publications
2,238Citations
Research Design
Longitudinal Studies
Propensity Score
Mediation Analysis
Causality
Machine Learning
Publications
2024
Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias
Ouyang Y, Li F, Li X, Bynum J, Mor V, Taljaard M. Estimates of intra-cluster correlation coefficients from 2018 USA Medicare data to inform the design of cluster randomized trials in Alzheimer’s and related dementias. Trials 2024, 25: 732. PMID: 39478608, PMCID: PMC11523597, DOI: 10.1186/s13063-024-08404-2.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsConceptsIntra-cluster correlation coefficientIntra-cluster correlation coefficient estimationSample size calculationED visitsMedicare dataMedicare fee-for-service beneficiariesEmergency departmentFee-for-service beneficiariesSize calculationDiagnosis of ADRDNational Medicare dataCluster randomized trialHospital referral regionsHospital service areasHealth care systemBackgroundCluster randomized trialsPopulation-level dataRandomized trialsDesign of cluster randomized trialsEvaluate interventionsReferral regionsCare systemICC estimatesADRDCorrelation coefficientUsing 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 ResearchConceptsInverse 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 comparisonsVarianceA 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 ResearchAltmetricConceptsSmall-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 methodEquationsHow 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsTreatment 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 DOI: 10.1111/sjos.12755.Peer-Reviewed Original ResearchCitationsAltmetricConceptsCluster-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, 43: 4796-4818. PMID: 39225281, DOI: 10.1002/sim.10206.Peer-Reviewed Original ResearchAltmetricConceptsGroup 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 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, 33: 1497-1516. PMID: 38807552, PMCID: PMC11499024, 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, 33: 1211-1232. PMID: 38780480, PMCID: PMC11348634, 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 objective
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 - Presentactivity Annals of Applied Statistics
Journal ServiceAssociate EditorDetails11/01/2024 - Present
News & Links
News
- October 25, 2024
Can a ‘Kidney Action Team’ Improve Patient Outcomes?
- October 24, 2024
New Analytics Center for Cardiovascular Medicine
- 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
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