Assistant Professor from The Dartmouth Institute for Health Policy and Clinical Practice, Dartmouth College, United States
Dr. Guanbo Wang is a distinguished postdoctoral research fellow at the CAUSALab, Harvard T.H. Chan School of Public Health. With a robust background in biostatistics and epidemiology, his work primarily focuses on developing innovative statistical methodologies to enhance causal inference in public health research. Dr. Wang’s expertise lies in integrating complex data sources to derive meaningful insights into treatment effects, particularly in the context of randomized clinical trials and observational studies. His interdisciplinary approach combines rigorous statistical theory with practical applications, aiming to inform clinical decision-making and health policy. Throughout his academic and professional journey, Dr. Wang has demonstrated a commitment to advancing public health through methodological innovation and collaborative research.
Professional Profile
Education
Dr. Wang’s academic journey commenced with a Bachelor of Science degree from China Textile University in Shanghai, China, in 1999. He pursued further studies at Shanghai Jiao Tong University, earning a Master of Science in 2002 and a Ph.D. in 2005. His doctoral research laid the foundation for his future endeavors in biostatistics and epidemiology. In 2019, Dr. Wang expanded his academic horizons as a visiting scholar at Harvard University, engaging with leading experts in the field. He culminated his formal education with a Ph.D. in Biostatistics from McGill University in 2022, where he honed his skills in statistical modeling and causal inference. Dr. Wang’s comprehensive educational background equips him with the theoretical knowledge and practical expertise necessary for his contributions to public health research.
Professional Experience
Dr. Wang’s professional experience encompasses a blend of academic research and industry collaboration. From 2015 to 2022, he served as a research assistant at McGill University and the McGill University Health Centre Research Institute, contributing to various projects in biostatistics and epidemiology. His industry experience includes internships and consultancy roles at prominent organizations such as Biogen, Roche, Baker & McKenzie, FINEOS Co., Ltd., KPMG China, and AXA Insurance Group. These roles provided Dr. Wang with practical insights into the application of statistical methods in diverse settings, including pharmaceuticals, legal consulting, and insurance. In 2023, he collaborated with the Center for Biostatistics in AIDS Research (CBAR), further solidifying his expertise in clinical trial analysis. Dr. Wang’s multifaceted professional background reflects his adaptability and commitment to applying statistical methodologies to real-world challenges.
Research Interests
Dr. Wang’s research interests are centered on advancing causal inference methodologies to address complex questions in public health. He focuses on data integration techniques that allow for the extension of causal inferences across different populations and data sources. His work on treatment effect heterogeneity aims to identify subpopulations that may benefit differently from interventions, thereby informing personalized medicine approaches. Dr. Wang also explores innovative trial designs, including adaptive and Bayesian frameworks, to enhance the efficiency and ethical conduct of clinical studies. Additionally, he is interested in incorporating prior knowledge, such as expert opinions, into statistical models to improve their interpretability and relevance. His methodological contributions are applied across various disease areas, including cardiovascular diseases, cancer, infectious diseases, and mental health disorders.
Research Skills
Dr. Wang possesses a comprehensive skill set in statistical methodologies and data analysis. His expertise includes nonparametric and semiparametric statistics, machine learning, high-dimensional data analysis, survival analysis, and optimization techniques. He is proficient in handling complex data structures, such as time-dependent treatments and longitudinal outcomes, commonly encountered in clinical and observational studies. Dr. Wang is adept at utilizing programming languages like R, Python, and C++ for statistical computing and developing analytical tools. His experience spans various data sources, including randomized clinical trials, electronic health records, and administrative databases. This diverse skill set enables him to tackle intricate research questions and contribute to methodological advancements in public health.
Awards and Honors
Dr. Wang’s contributions to biostatistics and public health research have been recognized through numerous awards and honors. He received the Fonds de Recherche du Québec – Santé Doctoral Training Grant (2019-2022), acknowledging his potential in health research. In 2021, he was honored with the Centre de Recherches Mathématiques StatLab Graduate Award and the McGill GREAT Award, reflecting his academic excellence. His achievements also include the McGill Graduate Excellence Award (2016-2021), the McGill Mobility Award (2019), and the McGill University Health Centre Studentship Fellowship (2019). Additionally, he was awarded the Statistical Society of Canada Biostatistics Travel Award in 2018. These accolades underscore Dr. Wang’s dedication to advancing statistical methodologies and their application in public health.
Conclusion
Dr. Guanbo Wang exemplifies a commitment to enhancing public health through methodological innovation in biostatistics and epidemiology. His extensive education and diverse professional experiences have equipped him with a robust foundation to address complex health research questions. Dr. Wang’s research endeavors aim to refine causal inference techniques, improve clinical trial designs, and integrate diverse data sources for comprehensive analyses. His contributions have the potential to inform evidence-based decision-making and personalized healthcare strategies. As he continues his work at the CAUSALab, Dr. Wang remains at the forefront of developing statistical methodologies that bridge the gap between data and impactful public health interventions.
Publications Top Notes
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Causal inference with multiple concurrent medications: A comparison of methods and an application in multidrug-resistant tuberculosis
Authors: A.A. Siddique, M.E. Schnitzer, A. Bahamyirou, G. Wang, T.H. Holtz, G.B. Migliori, et al.
Journal: Statistical Methods in Medical Research, 28(12), 3534–3549
Year: 2019
Citations: 31
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Estimating treatment importance in multidrug-resistant tuberculosis using Targeted Learning: An observational individual patient data network meta-analysis
Authors: G. Wang, M.E. Schnitzer, D. Menzies, P. Viiklepp, T.H. Holtz, A. Benedetti
Journal: Biometrics, 76(3), 1007–1016
Year: 2020
Citations: 17
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Using Effect Scores to Characterize Heterogeneity of Treatment Effects
Authors: G. Wang, P.J. Heagerty, I.J. Dahabreh
Journal: Journal of the American Medical Association
Year: 2024
Citations: 16
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Modeling treatment effect modification in multidrug-resistant tuberculosis in an individual patient data meta-analysis
Authors: Y. Liu, M.E. Schnitzer, G. Wang, E. Kennedy, P. Viiklepp, M.H. Vargas, et al.
Journal: Statistical Methods in Medical Research, 31(4), 689–705
Year: 2022
Citations: 10
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Evaluating hybrid controls methodology in early-phase oncology trials: A simulation study based on the MORPHEUS-UC trial
Authors: G. Wang, M.P. Costello, H. Pang, J. Zhu, H.J. Helms, I. Reyes-Rivera, R.W. Platt, et al.
Journal: Pharmaceutical Statistics, 23(1), 31–45
Year: 2023
Citations: 8
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Predictive factors of detectable viral load in HIV-infected patients
Authors: A. Bouchard, F. Bourdeau, J. Roger, V.T. Taillefer, N.L. Sheehan, M. Schnitzer, G. Wang, et al.
Journal: AIDS Research and Human Retroviruses, 38(7), 552–560
Year: 2022
Citations: 8
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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
Journal: Biometrics, 81(1)
Year: 2024
Citations: 5
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Robust integration of external control data in randomized trials
Authors: G. Wang, R. Karlsson, J.H. Krijthe, I.J. Dahabreh
Journal: arXiv preprint, arXiv:2406.17971
Year: 2024
Citations: 4
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Association between Sitting Time and Urinary Incontinence in the US Population: Data from the National Health and Nutrition Examination Survey (NHANES) 2007 to 2018
Authors: D. Xingpeng, Y. Chi, X. Liyuan, W. Guanbo, L. Banghua
Journal: Heliyon, 10(6)
Year: 2024
Citations: 4
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Integrating complex selection rules into the latent overlapping group Lasso for constructing coherent prediction models
Authors: G. Wang, S. Perreault, R.W. Platt, R. Wang, M. Dorais, M.E. Schnitzer
Journal: Statistics in Medicine
Year: 2025
Citations: 3