Steven Novick | Nonclinical Biostatistics | Best Researcher Award

Posted on 11/06/202511/06/2025 by TOT Awards

Dr. Steven Novick | Nonclinical Biostatistics | Best Researcher Award

Senior Director at Takeda , United States

Dr. Steven J. Novick is a highly accomplished biopharmaceutical statistician with over 25 years of experience in experimental design, statistical analysis, and decision-making within the pharmaceutical industry. As Senior Director of Biostatistics at Takeda, he leads global initiatives in nonclinical and Chemistry, Manufacturing, and Controls (CMC) statistics. His previous leadership roles at Eli Lilly and AstraZeneca reflect his depth of experience in managing diverse teams and driving innovation in preclinical research. A committed mentor and educator, Dr. Novick regularly delivers training on Bayesian statistics and advanced data analysis techniques. He plays a prominent role in the American Statistical Association (ASA), currently serving as Chair-Elect of the Biopharmaceutical Section (ASA-BIOP), and has led numerous conferences and workgroups. With more than 50 peer-reviewed publications and numerous industry accolades, Dr. Novick is recognized for his strategic leadership, scientific integrity, and dedication to enhancing drug development through statistically sound and ethically responsible methodologies.

professional profile

ORCID

Education

Dr. Novick holds a Ph.D. in Statistics (2000) and a Master’s in Statistics (1998), both from North Carolina State University, where he graduated with a perfect 4.0 GPA. His academic foundation was built on a Master of Science in Applied Mathematics from Lehigh University (1995), complemented by dual Bachelor’s degrees in Mathematics and Natural Science from Muhlenberg College (1992), earned summa cum laude. During his academic journey, Dr. Novick received numerous honors including the NSF VIGRE Fellowship, Gertrude M. Cox Fellowship, and several outstanding student paper and achievement awards. His early academic work focused on statistical theory, simulation techniques, and probabilistic modeling, laying the groundwork for a career that would integrate academic excellence with applied pharmaceutical science. These credentials underscore his deep understanding of statistical methodologies and their real-world application in complex biomedical research environments.

Work Experience

Dr. Novick has held senior statistical roles across leading pharmaceutical companies. At Takeda (2023–present), he leads global nonclinical and CMC statistical efforts. Prior, he was Executive Director of Biostatistics at Eli Lilly (2023), overseeing Discovery and CMC statistics. At AstraZeneca (2015–2023), he directed U.S. discovery biostatistics and bioinformatics teams, launching high-impact initiatives across R&D. He served as Senior Statistician at Arlenda (2015), providing Bayesian consulting to major firms. At GlaxoSmithKline (2002–2015), he supported target validation, dose-response modeling, and clinical trial design, earning multiple accolades. Earlier positions include roles at Schering-Plough, Becton Dickinson, and academic appointments at NC State University. Across all roles, Dr. Novick has been a catalyst for methodological innovation, workforce development, and data-driven decision-making. He is also a renowned educator and leader in professional forums, shaping statistical practice through mentoring, publishing, and organizing scientific meetings and training sessions.

Awards and Honors🏆

Dr. Novick is a 2022 Fellow of the American Statistical Association (ASA), recognized for his significant contributions to pharmaceutical statistics. He is a three-time recipient of the Stan Altan Best Nonclinical Biostatistics Paper Award (1st place: 2015, 2019; 2nd place: 2021). He earned the Outstanding Manuscript award from AAPS PharmSciTech in 2010. At AstraZeneca, he received the prestigious 3Rs Award and Early Oncology Award in 2021 for innovating dose-response designs that significantly reduced animal use. Additional recognitions include numerous internal awards at AstraZeneca and GlaxoSmithKline for leadership, innovation, training, and statistical excellence. As a graduate student, Dr. Novick received the NSF VIGRE Fellowship, Gertrude M. Cox Fellowship, and the R.L. Anderson Student Paper Award. These accolades highlight a career grounded in scientific rigor, ethical responsibility, and collaborative impact across academic, corporate, and professional communities.

Research Focus

Dr. Novick’s research bridges statistical theory and pharmaceutical application, focusing on preclinical and CMC biostatistics. His core interests include Bayesian inference, assay development, experimental design, dose-response modeling, and drug combination analysis. He is a pioneer in Bayesian methods for analytical similarity and assay validation and has authored foundational work on robust outlier detection and statistical reproducibility in drug discovery. He contributed to industry guidance on the use of p-values and led initiatives on Quality by Design in preclinical assay development. Dr. Novick also mentors statisticians in Bayesian modeling, machine learning, and regulatory science. Through his leadership in ASA-BIOP, he has championed scientific workstreams that yield real-world regulatory and industry tools. His scholarship and consulting underscore the power of statistical thinking to transform pharmaceutical R&D—from experimental rigor to regulatory compliance and ethical decision-making.

Conclusion

Dr. Steven J. Novick exemplifies the ideal candidate for the Research for Best Researcher Award. His prolific research, sustained leadership, strategic impact in biostatistics, and dedication to mentorship and innovation underscore his outstanding contributions to science and industry. He not only meets but significantly exceeds the typical criteria for this prestigious honor.

Publications to Noted

Jones J., Zhang B., Zhang X., Konings P., Hansson P., Backmark A., Serrano A., Künzel U., Novick S. (2025). Quality by Design for Preclinical In Vitro Assay Development. Pharmaceutical Statistics, 24(1).

Novick S., Zhang T. (2025). Strategy for Designing In Vivo Dose–Response Comparison Studies. Pharmaceutical Statistics, 24(1).

Zhang T., Novick S. (2023). A Comparison of Statistical Methods for Animal Oncology Studies. Pharmaceutical Statistics, 23(1).

Novick S., Zhang T. (2021). Mean Comparisons and Power Calculations to Ensure Reproducibility in Preclinical Drug Discovery. Statistics in Medicine, 40(6), 1345–1358.

Miranda E., Roberts J., Novick S., Lapointe J-M., Bruijnzeel-Koomen C., Thijs J.L., Sleeman M.A., May R.D., Hijnen D.J., Strickland I. (2021). Immunohistochemical Characterization of the IL-13:IL-4 Receptor α Axis in the Skin of Adult Patients with Moderate to Severe Atopic Dermatitis and Healthy Controls. Journal of Investigative Dermatology, 141(2), 382–392.

Novick S.J., Christian E., Farmer E., Tejada M. (2020). A Bayesian Statistical Approach to Continuous Qualification of a Bioassay. PDA Journal of Pharmaceutical Science and Technology, 74(5), 499–512.

Sondag P., Zeng L., Yu B., Yang H., Novick S. (2020). Comparisons of Outlier Tests for Potency Bioassays. Pharmaceutical Statistics, 19(3), 230–242.

Zhao W., Vicini P., Novick S., Anderton J., Davies G., D’Angelo G., O’Day T., Yu B., Harper J., Narwal R. (2019). Detecting Bliss Synergy in In Vivo Combination Studies with a Tumor Kinetic Model. Pharmaceutical Statistics, 18(6), 688–699.

Guanbo Wang | Biostatistics | Best Researcher Award

Posted on 02/06/202502/06/2025 by TOT Awards

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. 📊

professional profile

Google Scholar

Scopus

ORCID

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

Steven Novick | Nonclinical Biostatistics | Best Researcher Award

Dr. Steven Novick | Nonclinical Biostatistics | Best Researcher Award

professional profile

Education

Work Experience

Awards and Honors🏆

Research Focus

Conclusion

Publications to Noted

Guanbo Wang | Biostatistics | Best Researcher Award

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

professional profile

Education

Work Experience

Awards and Honors

Research Focus

Conclusion

Publications to Noted

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