Brian P. Hobbs, PhD
(Joint appointment with the Taussig Cancer Institute)
Lerner Research Institute,
9500 Euclid Avenue, Cleveland, Ohio 44195
Phone: (216) 444-3738
Fax: (216) 444-9464
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.
Selected from 83 peer-reviewed publications
Li X, Guindani M, Ng CS, Hobbs BP (2019). Spatial Bayesian modeling of GLCM with application to malignant lesion characterization. Journal of Applied Statistics, 46(2): 230-246. https://doi.org/10.1080/02664763.2018.1473348
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. Journal of the National Cancer Institute, 111(2): 118-128. https://doi.org/10.1093/jnci/djy196
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
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
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
Yu W, Tang C, Hobbs BP, Li X, Court L, Koay EJ, Chang JY (2018). Development and validation of a predictive radiomics model for clinical outcomes in stage I non-small cell lung cancer. International Journal of Radiation Oncology, Biology, Physics, 102(4): 1090-1097. https://doi.org/10.1016/j.ijrobp.2017.10.046
Koay EJ, Lee Y, Cristini V, Lowengrub JS, Kang Y, San Lucas FA, Hobbs BP, et al. (2018). A visually apparent and quantifiable CT imaging feature identifies biophysical subtypes of pancreatic ductal adenocarcinoma. Clinical Cancer Research, 24(23): 5883-5894. https://doi.org/10.1158/1078-0432.CCR-17-3668
Hobbs BP, Chen N, Lee JJ (2018). Controlled multi-arm platform design using predictive probability. Statistical Methods in Medical Research, 27(1): 65-78. https://doi.org/10.1177/0962280215620696
Ma J, Hobbs BP, Stingo FC (2018). Integrating genomic signatures for treatment selection with Bayesian predictive failure time models. Statistical Methods in Medical Research, 27(7): 2093-2113. https://doi.org/10.1177/0962280216675373
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
Chen N, Carlin BP, Hobbs BP (2018). Web-Based Statistical Tools for the Analysis and Design of Clinical Trials that Incorporate Historical Controls. Computational Statistics and Data Analysis, 127: 50-68. https://doi.org/10.1016/j.csda.2018.05.002
Ng CS, Altinmakas E, Ghosh P, Wei W, Grubbs EG, Perrier NA, Prieto VG, Lee JE, Hobbs BP (2018). Differentiation of malignant and benign adrenal lesions with delayed CT imaging: multivariate analysis and prediction models. American Journal of Roentgenology, 210(4): W156-163. https://doi.org/10.2214/AJR.17.18428
Ng CS, Altinmakas E, Ghosh P, Wei W, Grubb EG, Perrier NA, Lee JE, Prieto V, Hobbs BP (2018). Utility of intermediate-delay washout CT images for differentiation of malignant and benign adrenal lesions: A multivariate analysis. American Journal of Roentgenology, 211(2): W109-115. https://doi.org/10.2214/AJR.17.19103
Mohamed ASR, Hobbs BP, Hutcheson KA, Murri M, Garg N, Song J, Gunn GB, Sandulache V, et al. (2017). Dose-volume correlates of mandibular osteoradionecrosis in oropharynx cancer patients receiving intensity-modulated radiotherapy: Results from a case-matched comparison. Radiotherapy and Oncology, 124(2): 232-239. https://doi.org/10.1016/j.radonc.2017.06.026
Kaizer AM, Koopmeiners JS, Hobbs BP (2017). Bayesian hierarchical modeling based on multi-source exchangeability. Biostatistics, 19(2): 169-184.
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://doi.org/10.1016/j.neuroimage.2015.10.033
Sandulache VC, Hobbs BP, Mohamed ASR, Frank SJ, Song J, Ding Y, Kalpathy-Cramer J, Hazle JD, et al. (2016). Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) detects acute radiotherapy-induced alterations in mandibular bone microvasculature: Results from a prospective assessment of quantitative imaging biomarkers of normal tissue injury. Scientific Reports, 6: 29864. https://doi.org/10.1038/srep29864.
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://doi.org/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://doi.org/10.1111/biom.12448
Raghav KPS, Mahajan S , Yao JC, Hobbs BP, Berry DA, Pentz RD, Tam A, Hong WK, Ellis LM, Abbruzzese J, Overman MJ (2015). From protocols to publications: a study in selective reporting of outcomes in randomized trials in oncology. Journal of Clinical Oncology, 33(31): 3583-3590, e-Pub 8/2015. https://doi.org/10.1200/JCO.2015.62.4148.
Wang Y, Hobbs BP, Hu J, Ng C, Do KA (2015). Predictive classification of correlated targets with application to detection of metastatic cancer using functional CT imaging. Biometrics, 71(3): 792-802. https://doi.org/10.1111/biom.12304
Ng CS, Hobbs BP, Chandler AG, Anderson EF, Herron DH, Charnsangavej C, Yao J (2013). Metastases to the liver from neuroendocrine tumors: Effect of duration of scan acquisition on CT perfusion values. Radiology, 269(3): 758-767. https://doi.org/10.1148/radiol.13122708
Hobbs BP, Carlin BP, Sargent DJ (2013). Adaptive adjustment of the randomization ratio using historical control data. Clinical Trials, 10: 430-440. https://doi.org/10.1177/1740774513483934
Hobbs BP, Sargent DJ, Carlin BP (2012). Commensurate priors for incorporating historical information in clinical trials using general and generalized linear models. Bayesian Analysis, 7: 639–674. https://doi.org/10.1214/12-BA722
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://doi.org/10.1111/j.1541-0420.2011.01564.x
A complete list of publications can be found on Google Scholar
Open Source Statistical Software
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.