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
Presenters:
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
Description:
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
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
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
BP Hobbs, PF Thall, SH Lin
Journal of the Royal Statistical Society: Series C (Applied Statistics) 65, 2016
Biomarker Studies and Methodology
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
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
