Dr. Brian Hobbs joined Cleveland Clinic as Associate Staff and Section Head of Cancer Biostatistics in the Lerner Research Institute in 2017. He holds a joint appointment in Cleveland Clinic’s Taussig Cancer Institute. He also serves as Co-Director of the Biostatistics and Bioinformatics Core for the Case Comprehensive Cancer Center. His methodological expertise comprises Bayesian inference, subtyping, prediction, and trial design as well as cancer radiomics. Before joining Cleveland Clinic, Dr. Hobbs was a tenured Associate Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center in Houston, Texas. In 2010, he completed a doctoral degree in biostatistics at the University of Minnesota and then joined MD Anderson as a postdoctoral fellow. Eastern North American Region of International Biometric Society selected his thesis paper for the John Van Ryzin Award in 2010. In 2016, Dr. Hobbs was selected by The University of Minnesota for the Emerging Leader Award, an honor bestowed on alumni on the basis of impactful contributions within 10 years of graduating from one of The School of Public Health’s 20 programs. In 2017, Dr. Hobbs was invited to lead the publication of National Cancer Institute’s Clinical Trials Design Task Force with the goal of providing national, consensus recommendations for first-in-human cancer drug trials that use seamless designs.
Hobbs BP, Barata PC, Kanjanapan Y, Paller CJ, Perlmutter J, Pond GR, Prowell TM, Rubin EH, Seymour LK, Wages NA, Yap TA, Feltquate D, Garrett-Mayer E, Grossman W, Hong DS, Ivy SP, Siu LL, Reeves SA, Rosner GL (2018). Seamless Designs: Current Practice and Considerations for Early-Phase Drug Development in Oncology. JNCI: Journal of the National Cancer Institute, 111(2): 118-128. https://doi.org/10.1093/jnci/djy196 ; https://academic.oup.com/jnci/article/111/2/118/5245491
Hobbs BP, Kane MJ, Hong DS, Landin R (2018). Statistical challenges posed by basket trials: sensitivity analysis of the Vemurafenib study. Annals of Oncology, 29(12): 2296-2301. https://doi.org/10.1093/annonc/mdy457 ; https://doi.org/10.1093/annonc/mdy457
Hobbs BP and Landin R (2018). Bayesian basket trial design with exchangeability monitoring. Statistics in Medicine, 37(25): 3557-3572. https://doi.org/10.1002/sim.7893; https://doi.org/10.1002/sim.7893
Tang C, Hobbs B*, Amer A, Li X, Behrens C, Canales JR, Cuentas EP, Villalobos P, Chang J, Hong D, Welsh J, Sepesi B, Wistuba I, Koay E (2018). Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer. Scientific Reports, 8(1) Article number: 1922. https://doi.org/10.1038/s41598-018-20471-5
Hobbs BP, Chen N, Lee JJ (2018). Controlled multi-arm platform design using predictive probability. Statistical Methods in Medical Research, 27(1): 65-78. Controlled multi-arm platform design using predictive probability. Statistical Methods in Medical Research, 27(1): 65-78. https://journals.sagepub.com/doi/full/10.1177/0962280215620696?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%3dpubmed
Kaizer AM, Hobbs BP, Koopmeiners JS (2018). A Multi-source Adaptive Platform Design for Testing Sequential Combinatorial Therapeutic Strategies. Biometrics, 74(3): 1082-1094. https://doi.org/10.1111/biom.12841
Kaizer AM, Koopmeiners JS, Hobbs BP (2017). Bayesian hierarchical modeling based on multi-source exchangeability. Biostatistics, 19(2): 169-184. https://academic.oup.com/biostatistics/article/19/2/169/3930935
Azadeh S, Hobbs BP, Ma L, Nielsen D, Moeller FG, Baladandayuthapani V (2016). Integrative Bayesian Analysis of Neuroimaging-Genetic Data with Application to Cocaine Dependence. NeuroImage, 125: 813-824, e-Pub 10/2015. https://www.sciencedirect.com/science/article/pii/S105381191500943X
Hobbs BP, Thall PF, Lin SH (2016). Bayesian group sequential clinical trial design using total toxicity burden and progression-free survival. Journal of the Royal Statistical Society: Series C, 65(2): 273-297, e-Pub 10/2015. https://rss.onlinelibrary.wiley.com/doi/10.1111/rssc.12117
Ma J, Stingo FC, Hobbs BP (2016). Bayesian predictive modeling for genomic based personalized treatment selection. Biometrics, 72(2): 575-583, e-Pub 11/2015. https://onlinelibrary.wiley.com/doi/10.1111/biom.12448
Hobbs BP, Carlin BP, Sargent DJ (2013). Adaptive adjustment of the randomization ratio using historical control data. Clinical Trials, 10: 430-440. https://journals.sagepub.com/doi/full/10.1177/1740774513483934
Hobbs BP, Carlin BP, Mandrekar S, Sargent DJ (2011). Hierarchical commensurate and power prior models for adaptive incorporation of historical information in clinical trials. Biometrics, 67: 1047–1056. https://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2011.01564.x
Breakthroughs in cancer biology have defined new research programs emphasizing the development of therapies that target specific pathways in tumor cells or promote anti-cancer immunity. Innovations in clinical trials have followed with statistical designs devised to consolidate traditional phases of oncologic drug development as well as facilitate inclusive eligibility and evaluations of multiple indications. These so-called “seamless” trial designs have many potential benefits but have not yet been objectively considered.