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Fan Li, PhD

Associate Professor of Biostatistics and Associate Professor of Medicine (Cardiovascular Medicine)
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About

Titles

Associate Professor of Biostatistics and Associate Professor of Medicine (Cardiovascular Medicine)

Biography

Dr. Fan Li is an Associate Professor in the Department of Biostatistics at Yale School of Public Health and holds a secondary appointment in the Department of Internal Medicine (Section of Cardiovascular Medicine) at Yale School of Medicine. He is a faculty member in the Center for Methods in Implementation and Prevention Science, and is also affiliated with the Yale Center for Analytical Sciences as well as the Clinical and Translational Research Accelerator. He received his Ph.D. in Biostatistics from Duke University in 2019. His research is centered on advancing statistical methodology in causal inference, clinical trial design, and mediation analysis, integrating modern semiparametric and machine learning techniques to address complex challenges in pragmatic trials, clinical research, biomedical science, and public health.

Several examples of Dr. Li’s ongoing research agenda include: (1) improving study planning, estimands construction, and model-robust methods that enhance rigor and efficiency of cluster randomized trials and stepped wedge designs; (2) advancing propensity score methods, doubly or multiply robust methods, principal stratification, and causal mediation techniques to address challenges such as censored endpoints, mismeasured exposures, and intermediate variables; (3) developing and integrating flexible and principled frequentist and Bayesian machine learning algorithms to improve both average and individualized causal effect estimation in clinical trials and observational studies with clustered and longitudinal structures, time-varying treatments and intercurrent events. He has published over 150 peer-reviewed research articles and has led or contributed to the development of many statistical software packages that support robust analyses of biomedical, clinical and public health data, ensuring that methodological innovations translate into real-world impact. A full list of his scholarly work can be found on this Google Scholar Page.

Dr. Li leads and collaborates on multiple extramurally funded research projects, working closely with clinicians, biostatisticians, epidemiologists, and data scientists to advance evidence-based medicine, precision medicine, healthcare delivery science, and implementation science. He is also committed to mentoring students and postdoctoral researchers, training the next generation of biostatisticians at the intersection of statistical methodology, computation, and real-world application. He has multiple editorial roles, including serving as the Co-Editor-in-Chief for Epidemiologic Methods and Associate Editor for Statistics in Medicine, Annals of Applied Statistics, and Clinical Trials.

Appointments

Education & Training

PhD
Duke University, Biostatistics (2019)

Research

Overview

Below is an overview of my research agenda and contributions. I welcome opportunities to collaborate with researchers from diverse disciplines and invite strong students and postdoctoral scholars to reach out regarding potential research opportunities. I am particularly interested in working with students who have strong mathematical foundations and/or exceptional computing skills. I have had the privilege of collaborating with many graduate students, postdoctoral researchers, and faculty colleagues, whose insights and dedication have played a crucial role in shaping and advancing our research projects together.

Cluster Randomized Trials & Stepped Wedge Designs: My team develops statistical methods to enhance the design and efficiency of cluster randomized trials (CRTs), including parallel-arm, crossover, stepped wedge, and individually randomized group treatment designs. We are interested in expanding the statistical toolbox for CRTs to handle missing covariate and outcome data, to properly address complex survival and composite endpoints, and to enable detection of measured and unmeasured treatment effect heterogeneity. We are also advancing an estimand-aligned and efficiency-focused framework, contributing both theoretical advancements and applied innovations that shape modern CRT and pragmatic clinical trial research.

Propensity Score/Principal Score Methods: My team develops statistical methods to improve causal effect estimation in both randomized and observational studies. Our work focuses on advancing propensity score weighting—for example, the overlap weighting approach, and other semiparametric efficient techniques to address confounding and selection bias. We are also extending principal stratification methods—for example, the principal score approach, to handle intermediate variables (also referred to as "intercurrent events" under the ICH E9 Estimands Framework), addressing challenging such as censored survival outcomes, multiple treatments, clustering, and non-monotonicity.

Causal Mediation Methods to Uncover Mechanisms: My team develops statistical methods for regression and causal mediation analysis in complex biomedical settings, including mismeasured exposures, survival endpoints, complex mediators, and post-treatment confounding. We focus on developing robust and efficient estimators and sensitivity analysis techniques that strengthen inference under transparent and interpretable assumptions. Our recent interest includes advancing semiparametric efficient and flexible outcome modeling methods for studying natural mediation effects and spillover mediation effects within a multilevel data context, to improve our understanding of causal mechanisms in modern pragmatic and implementation trials.

De-biased Causal Machine Learning & Bayesian Causal Machine Learning: My team studies principled methodologies that integrate modern causal machine learning to improve robustness, efficiency, and interpretability in clinical trials and observational studies. Two typical examples include (a) de-biased machine learning methods that integrate flexible nuisance function estimators for estimating the average causal effects, quantile causal effects, natural direct and indirect effects and principal causal effects; (b) Bayesian additive regression trees methods that flexibly model complex outcome surfaces for addressing conditional average causal effects, conditional principal average causal effects, and time-varying causal effects.

Statistical Software Development: My team has been developing open-source statistical software to support causal inference, (clustered or non-clustered) clinical trials, observational studies, and mediation analysis. Examples of our past contributions include R packages for propensity score methods (e.g., PSweight), variance estimation with finite-sample adjustments (e.g., CoxBCV), mediation analysis (e.g., mediateP), constrained randomization (e.g., cvcrand), and marginal modeling (e.g., geeCRT), among others. We focus on disseminating computational tools that facilitate the practical implementation of new statistical methods, making a small but meaningful effort to translate methodological innovations into practice change for clinical, biomedical, and public health research.

Empirical Research & Interdisciplinary Collaborations: While my team consists primarily of biostatisticians, we have a strong interest in collaborating with researchers from diverse backgrounds. We have worked on multiple systematic reviews on pragmatic clinical trials, fostering a deep appreciation for empirical research as a means of understanding the current landscape of statistical and methodological practice. These efforts have largely motivated my work in addressing complex statistical challenges that are factually grounded and well-motivated. My team actively collaborates with clinical scientists and epidemiologists across a wide range of disciplines, including but not limited to cardiology, nephrology, aging and dementia, emergency medicine, critical and intensive care, and implementation science.

Medical Research Interests

Cardiovascular Diseases; 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

Public Health Interests

Aging; Cardiovascular Diseases; Clinical Trials; End-of-life Care; Epidemiology Methods; Health Policy; Health Systems Strengthening; Bayesian Statistics; Health Equity, Disparities, Social Determinants and Justice; Implementation Science; Randomized Trials; Survival Analysis

Research at a Glance

Yale Co-Authors

Frequent collaborators of Fan Li's published research.

Publications

2025

2024

Academic Achievements & Community Involvement

  • activity

    Statistics in Medicine

  • activity

    Clinical Trials (Journal of the Society for Clinical Trials)

  • activity

    Implementation Science

  • activity

    Epidemiologic Methods

  • activity

    Annals of Applied Statistics

Get In Touch

Contacts

Academic Office Number

Locations

  • 135 College Street

    Academic Office

    Ste Suite 200, Rm Room 229

    New Haven, CT 06510