Assist. Prof. Dr. Guanbo Wang | Biostatistics | Best Researcher Award

Assistant professor at The Dartmouth Institute, United States

Dr. Guanbo Wang is an Assistant Professor at The Dartmouth Institute for Health Policy and Clinical Practice and the Department of Biomedical Data Science, Dartmouth College. He specializes in biostatistics, causal inference, and machine learning, with a commitment to advancing statistical methodologies for improving public health. His work focuses on data integration, trial design, and personalized medicine using multi-source data such as randomized clinical trials and electronic health records. Dr. Wang holds a Ph.D. and M.Sc. in Biostatistics from McGill University, with research stints at Harvard University. He has collaborated with leading institutions like Harvard T.H. Chan School of Public Health, Biogen, and Roche, contributing to diverse therapeutic areas including cancer, cardiovascular diseases, and pediatric health. His academic contributions are evidenced by his prolific publication record and roles in high-impact research projects. Dr. Wang is also an active mentor, reviewer, and member of professional societies in statistics and epidemiology. 📊

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Education

Dr. Guanbo Wang earned his Ph.D. in Biostatistics (2017–2022) and M.Sc. in Biostatistics (2015–2017) from McGill University, under esteemed mentors such as Robert Platt, Mireille Schnitzer, and Andrea Benedetti. His doctoral and master’s work emphasized causal inference, high-dimensional data, and clinical applications. He also completed a visiting scholar stint at Harvard University’s Department of Population Medicine in 2019, guided by Rui Wang. Currently, he is completing a Postdoctoral Fellowship (2022–2025) in Epidemiology at CAUSALab, Harvard T.H. Chan School of Public Health, mentored by Issa Dahabreh. His academic training integrates theoretical statistics with real-world medical challenges, contributing to a solid foundation in methodological development and applied health sciences. With additional statistical training from China, Dr. Wang’s global academic background uniquely equips him to tackle multifaceted health data challenges. 📘📈

Work Experience

Dr. Wang’s professional journey spans academia, industry, and collaborative health research. Since 2023, he has served as a research collaborator at Harvard’s CBAR on pediatric HIV/AIDS studies. Previously, he interned at Genentech/Roche, where he augmented early-phase oncology trials using external data. He consulted for Biogen on machine learning in neurology and supported various clinical research projects at McGill-affiliated hospitals. Earlier roles include internships with firms such as KPMG, AXA Insurance, and the National Bureau of Statistics of China, where he developed analytics and business strategies. Dr. Wang has also taught graduate courses and mentored students at McGill and Harvard. These experiences reflect his ability to bridge complex statistical theory with practical solutions in both research and corporate settings. 🏥📊

Awards and Honors

Dr. Wang has earned widespread recognition through prestigious awards and fellowships reflecting his scholarly excellence. He is a co-investigator on the $31M PCORI-funded RAPTOR-CIED project, supporting cardiac device care optimization. He was awarded the FRQS Doctorate Fellowship (2019–2022), valued at CAD 70,000, for his work on machine learning in anticoagulant effectiveness. Additional honors include the Research Institute of MUHC Fellowship (CAD 9,125), Harvard Pilgrim Fellowship (USD 4,000), and the CRM StatLab Award. Dr. Wang has also received the McGill Graduate Excellence Award (CAD 40,227), the McGill GREAT Award, and travel grants from the Statistical Society of Canada. His competitive success in acquiring NIH and CIHR funding as a trainee and collaborator further reflects his leadership in health data science. These distinctions underscore his impact on high-stakes research, statistical innovation, and mentorship within interdisciplinary academic and clinical communities. 🏆📑

Research Focus

Dr. Wang’s research lies at the intersection of causal inference, machine learning, and health policy. He focuses on developing robust statistical methods for data integration—particularly combining randomized controlled trials with real-world data sources to generate generalizable and actionable evidence. In personalized medicine, his work aims to evaluate treatment heterogeneity by leveraging multi-source longitudinal datasets and time-to-event outcomes. He also develops advanced methods for complex clinical trial designs, including adaptive, early-phase, and cluster-randomized trials. Another research stream explores how to incorporate prior clinical knowledge into high-dimensional prediction models to improve interpretability and clinical relevance. Methodologically, Dr. Wang draws on non/semi-parametric statistics, optimization, survival analysis, and computational methods. His work addresses diseases across pediatrics, oncology, cardiology, infectious diseases, and mental health. With a mission to inform precision health decisions, Dr. Wang’s research supports a deeper understanding of treatment effects across diverse populations and care settings. 🧬🧠📉

Conclusion

Dr. Guanbo Wang is an exceptional candidate for the Research for Best Researcher Award. His profile demonstrates a rare blend of theoretical innovation, real-world applicability, and academic leadership. His interdisciplinary and collaborative approach, especially in causal inference for healthcare, aligns perfectly with the award’s objectives to recognize researchers who push the boundaries of impactful, rigorous science.

Publications to Noted

Title: Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis
Authors: AA Siddique, ME Schnitzer, A Bahamyirou, G Wang, TH Holtz, GB Migliori, …
Citations: 31
Year: 2019

Title: Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning: An observational individual patient data network meta-analysis
Authors: G Wang, ME Schnitzer, D Menzies, P Viiklepp, TH Holtz, A Benedetti
Citations: 18
Year: 2020

Title: Using Effect Scores to Characterize Heterogeneity of Treatment Effects
Authors: G Wang, PJ Heagerty, IJ Dahabreh
Citations: 17
Year: 2024

Title: Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patient data meta-analysis
Authors: Y Liu, ME Schnitzer, G Wang, E Kennedy, P Viiklepp, MH Vargas, …
Citations: 11
Year: 2022

Title: Evaluating hybrid controls methodology in early-phase oncology trials: a simulation study based on the MORPHEUS-UC trial
Authors: G Wang, MP Costello, H Pang, J Zhu, HJ Helms, I Reyes-Rivera, RW Platt, …
Citations: 9
Year: 2023

Title: Predictive factors of detectable viral load in HIV-infected patients
Authors: A Bouchard, F Bourdeau, J Roger, VT Taillefer, NL Sheehan, M Schnitzer, G Wang, …
Citations: 9
Year: 2022

Title: Robust integration of external control data in randomized trials
Authors: G Wang, R Karlsson, JH Krijthe, IJ Dahabreh
Citations: 6
Year: 2024

Title: Penalized G-estimation for effect modifier selection in the structural nested mean models for repeated outcomes
Authors: A Jaman, G Wang, A Ertefaie, M Bally, R Lévesque, R Platt, M Schnitzer
Citations: 6
Year: 2024

Title: Combining an experimental study with external data: study designs and identification strategies
Authors: L Ung, G Wang, S Haneuse, M Hernán, I Dahabreh
Citations: 4
Year: 2024

Guanbo Wang | Biostatistics | Best Researcher Award