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    Short Course: Drug development of non-cytotoxics

    Short Course: Drug development of non-cytotoxics

    Subtype Identification and Strategies for Trial Design

    Half-day Shortcourse

    Shortcourse: Drug development of non-cytotoxics: Subtype Identification and Strategies for Trial Design




    Brian P. Hobbs, (hobbsb@ccf.org)

    Associate Staff; Section Head of Cancer Biostatistics

    Taussig Cancer Institute and Lerner Research Institute

    Cleveland Clinic


    Michael J. Kane, (michael.kane@yale.edu)

    Assistant Professor of Biostatistics

    Yale School of Public Health

    Yale University




    Advances in biology and immunology continue to refine our understanding of cancer pathogenesis, elucidating potential mechanisms of tumor-cell growth, survival, angiogenesis and the systematic suppression of cancer immunity progressing toward precision medicine. Implicit to the concept of precision medicine is heterogeneity of treatment benefit among patients and patient sub-populations. Yet, precision medicine presents challenges to traditional paradigms of clinical translational for which estimates of population-averaged effects are used as the basis for selecting dose-scheduling strategies as well as demonstrating comparative benefit with randomized study.


    Aspects of the traditional clinical research paradigm may not ideally suit the development of non-cytotoxics that challenge its foundational assumptions pertaining to dose-response and inter-patient exchangeability. Tumor biology and/or host immunity may better delineate target treatment populations than histology. Several emerging molecularly targeted and immunotherapeutic agents have produced durable responses in first-in-human trials. The U.S. regulatory landscape has also changed, with a growing number of accelerated approvals on the basis of single-arm trials. Collectively, these phenomena have prompted innovations with drug development strategies devised to consolidate phases in the traditional paradigm and rapidly expand accrual with “seamless” trial designs. This short-course is intended to elucidate issues that limit the effectiveness of traditional clinical research and offer solutions to elucidate subpopulation heterogeneity from clinical datasets and subsequent trial designs devised to characterize and adapt to evidence of benefit heterogeneity.


    Specifically, analysis and design methodology will consider characterization of localized treatment benefit, basket trial design based on multi-source exchangeability modeling (MEM), and how to leverage existing databases to design single-arm trials that incorporate baseline prognostic classifiers and thereby facilitate counterfactual comparisons in early-phase trials. The methodology will be illustrated by actual clinical cancer trials, including analysis and permutation studies of actual data reported from a recent basket trial designed to estimate the effectiveness of vemurafenib in BRAF mutant non-melanoma among six clinical sites.


    Selected Literature

    Trial Design Methodology


    Seamless Designs: Current Practice and Considerations for Early-Phase Drug Development in Oncology

    BP Hobbs, PC Barata, Y Kanjanapan, CJ Paller, J Perlmutter, GR Pond, …

    JNCI: Journal of the National Cancer Institute 2018 Dec 17. doi: 10.1093/jnci/djy196.


    Bayesian basket trial design with exchangeability monitoring

    BP Hobbs, R Landin

    Statistics in medicine 37 (25), 3557-3572, 2018


    Statistical challenges posed by uncontrolled master protocols: sensitivity analysis of the vemurafenib study

    BP Hobbs, MJ Kane, DS Hong, R Landin

    Annals of Oncology 29 (12), 2296-2301, 2018


    Controlled multi-arm platform design using predictive probability

    BP Hobbs, N Chen, JJ Lee

    Statistical methods in medical research 27 (1), 65-78, 2018


    Web-based statistical tools for the analysis and design of clinical trials that incorporate historical controls

    N Chen, BP Carlin, BP Hobbs

    Computational Statistics & Data Analysis 127, 50-68, 2018


    A multi‐source adaptive platform design for testing sequential combinatorial therapeutic strategies

    AM Kaizer, BP Hobbs, JS Koopmeiners

    Biometrics 2018 Sep; 74(3):1082-1094. doi: 10.1111/biom.12841. Epub 2018 Jan 22.


    Bayesian hierarchical modeling based on multisource exchangeability

    AM Kaizer, JS Koopmeiners, BP Hobbs

    Biostatistics 19 (2), 169-184, 2017


    Bayesian group sequential clinical trial design using total toxicity burden and progression‐free survival

    BP Hobbs, PF Thall, SH Lin

    Journal of the Royal Statistical Society: Series C (Applied Statistics) 65, 2016


    Biomarker Studies and Methodology


    Bayesian personalized treatment selection strategies that integrate predictive with prognostic determinants.

    J Ma, FC Stingo, BP Hobbs

    Biometrical journal. Biometrische Zeitschrift, 2019, in press


    Development of an immune-pathology informed radiomics model for non-small cell lung cancer

    C Tang, **B Hobbs, A Amer, X Li, C Behrens, JR Canales, EP Cuentas, …

    Scientific reports 8 (1), 1922, 2018


    Estimating mean local posterior predictive benefit for biomarker-guided treatment strategies

    M Huang, BP Hobbs

    Statistical methods in medical research, 0962280218788099, in press


    Integrating genomic signatures for treatment selection with Bayesian predictive failure time models

    J Ma, BP Hobbs, FC Stingo

    Statistical methods in medical research, 27 (7), 2093-2113, 2018


    Development and validation of a predictive radiomics model for clinical outcomes in stage I non-small cell lung cancer

    W Yu, C Tang, BP Hobbs, X Li, EJ Koay, II Wistuba, B Sepesi, C Behrens, …

    International Journal of Radiation Oncology* Biology* Physics 102 (4), 1090-1097, 2018


    Bayesian predictive modeling for genomic based personalized treatment selection

    J Ma, FC Stingo, BP Hobbs

    Biometrics 72 (2), 575-583, 2016


    Biomarker Driven Studies

    Statistical methods for establishing optimal treatment rules for matching patients to therapy.
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    Seamless Trial Designs

    Designs to consolidate the phases of drug development through rapid cohort expansion.
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    Master Protocols

    Designs to simultaneously evaluating multiple drugs and/or disease populations in multiple substudies, allowing for efficient and accelerated drug development.
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