Our Team

Dr. Guangyu Tong is an Assistant Professor of Cardiovascular Medicine at the Yale School of Medicine and holds a secondary appointment as Assistant Professor of Biostatistics at the Yale School of Public Health. He currently directs the Cardiovascular Medicine Analytics Center (CMAC), which serves as a vital hub for study design and analytic support across Yale’s cardiovascular research community. Dr. Tong also holds research roles across several interdisciplinary initiatives, including faculty affiliations with the Center for Methods of Implementation and Prevention Science (CMIPS), the Data Management and Statistics Core of the Alzheimer’s Disease Research Center (ADRC-DMSC), the Center for Interdisciplinary Research on AIDS (CIRA), and the Interdepartmental Foci of Firearm Injury Prevention (FIP)—a university-wide initiative aimed at cross-sector collaboration to reduce firearm-related harm. On the international stage, he contributes to editorial leadership as Statistical Editor of the Journal of the American College of Cardiology (JACC), Statistical Advisor of The Lancet, and Associate Editor for BMC Medicine. He is frequently invited to present his work at scientific conferences and institutions across North America, Europe, and Asia. He is a recognized contributor to numerous NIH-funded trials and global health studies in cardiovascular medicine and implementation science. His work bridges the development of innovative statistical methods with real-world implementation in medicine and public health. At the core of Dr. Tong’s methodological expertise is the design and analysis of pragmatic trials, especially cluster randomized trials (CRTs), individually randomized group treatment trials (IRGTs), and stepped wedge cluster randomized trials (SW-CRTs). His research has led to major advances in sample size estimation, treatment effect heterogeneity, and complex trial designs with unequal cluster sizes or truncated outcomes. His methodological research has advanced how such trials are powered, especially under real-world constraints like unequal cluster sizes, outcome truncation, or heterogeneous intraclass correlations. He has contributed to methods for modified Poisson models in binary outcomes, generalized estimating equations, and the planning of trials with complex multilevel structures. His methodological work has routinely appeared in journals such as Annals of Applied Statistics, Statistics in Medicine, Statistical Methods in Medical Research, Biometrical Journal, American Journal of Epidemiology, Clinical Trials, and Contemporary Clinical Trials, where he has helped shape best practices for evaluating treatment heterogeneity, sample size calculation under complex clustering, and design in real-world interventions. He is an experienced biostatistician in the design and analysis of multiple NIH-funded pragmatic trials, including PULESA-UGANDA (UG3-HL154501), TRANSFORM-HF (U01-HL125511), TRUE HAVEN (R01MD017526), G4H (R01CE003267), iDOVE2 (R01HD093655), WISHES (R18HS029812), C4+3MV (R01MH138225), and HPTN096 (UM1AI068619). In parallel, Dr. Tong is an expert in Bayesian statistics and causal inference, developing advanced techniques to handle post-randomization complications such as death censoring and outcome heterogeneity. Supported by the Patient-Centered Outcomes Research Institute (PCORI ME-2020C1-19220), he developed hierarchical Bayesian models for cluster trials with varying outcome variance and led the creation of frameworks to estimate survivor average causal effects (SACE), particularly in trials where mortality precludes full data collection. This series of work had earned his recognition as a 2023 Faculty Scholar with the National Institute on Aging's IMPACT Collaboratory. Additionally, he co-developed PSweight, a widely used R package for flexible propensity score weighting analysis that supports multiple arm comparison, survey weights, and machine learning methods, reinforcing his commitment to transparent, reproducible causal inference methods in observational research. Another major pillar of Dr. Tong’s work focuses on cardiovascular medicine, where he serves as biostatistician and co-investigator on several high-impact studies. He led several statistical analyses of pooled data from the REVIVED-BCIS2 and STICH trials, providing comparative effectiveness insights on PCI vs. CABG for ischemic heart failure. Under the global health setting, he has also contributed to understanding education-based disparities in cardiovascular health across 36 low- and middle-income countries. His collaborations span randomized clinical trials and meta-analyses—such as identifying optimal pharmacologic strategies to prevent postoperative delirium through Bayesian network modeling—highlighting both his clinical relevance and statistical innovation. Beyond his work in cardiovascular medicine and trial methodology, Dr. Tong has developed a line of research focused on mental health and firearm injury prevention, areas that lie at the intersection of public safety, trauma, and health equity. He serves as a biostatistician on federally funded intervention studies, including G4H (R01CE003267) and iDOVE2 (R01HD093655), which aim to prevent youth firearm injury, peer violence, and depressive symptoms through school- and community-based interventions. These projects integrate both epidemiologic modeling and implementation science, and Dr. Tong’s contributions ensure rigorous analytic frameworks are applied to measure intervention effectiveness and longitudinal outcomes. In addition, Dr. Tong serves as a senior statistician for the Great Smoky Mountain Study (GSMS; R01MH117559)—a landmark, multi-decade longitudinal cohort that tracks children from adolescence into adulthood. Through this role, he has co-authored over ten publications examining how early exposures—such as cardiometabolic health, access to firearms, experiences of violent victimization, or involvement with psychiatric and juvenile justice systems—shape long-term mental health, criminal, functional, cardiometabl outcomes, and firearm-related behaviors. His work has directly informed policy debates around “red flag” laws, age-based firearm restrictions, and trauma-informed interventions. These studies not only bring together multiple NIH priority areas—child mental health, firearm injury prevention, and health disparities—but also showcase Dr. Tong’s capacity to lead methodologically sophisticated, socially impactful research. His analytical approaches combine causal inference, longitudinal modeling, missing data, and time-to-event modeling to support evidence-based, ethically sound public health strategies.