PSI Pre-Clinical SIG Workshop 2022
Date: Wednesday 21st September - Thursday 22nd September 2022
21st = 12:30-17:00 | 22nd = 09:00-17:00 BST
21st = 13:30-18:00 | 22nd = 10:00-18:00 CEST
Who is this event intended for? Pre-Clinical Statisticians in the Pharmaceutical Industry, with interest and/or some basic knowledge of R/STAN/BUGS and Bayesian statistics.
What is the benefit of attending? This workshop offers the chance to meet with colleagues across industry and learn more about Bayesian Methodology and its applications in pre-clinical.
This is the pre-clinical SIG’s 10th workshop and will be the first one run virtually. Our theme for this workshop is Bayesian; the workshop will run over a day and a half and will include a training course on Bayesian methods (see more below), two presentations on applications of Bayesian methodology in a pre-clinical setting and a breakout session.
Bayesian Statistics for Preclinical Research: New Opportunities
The course will start by introducing the key concepts of Bayesian statistics, emphasizing the context and key objectives of preclinical research in pharmaceutical and medical device development. We then move on to show how Bayesian thinking and practices are a fit-for-purpose paradigm. Over the last decade, preclinical research has been identified in the literature as an area of research suffering from a lack of reproducibility. Causes for this are many, but in this course, we’ll show how to frame a Bayesian strategy to address reproducibility concerns by proposing new study designs, modelling, and decision-making. Preclinical research is a learning process, making Bayesian statistical learning a very natural partnership.
Key topics covered by the course include:
- Define the question and the research objective
- Strategies for determining, using, and checking robustness of prior distributions
- Replacing experiment-based decisions in favor of project-based decision
- Use of informed control groups and unbalanced designs
- Design of the overall project, integrating the potential sources of irreproducibility in advance
- Progress under uncertainty, adoption of adaptive designs
- Designing experiments using Bayesian assurance, rather than power
- Understand risks and predictive probability of success to meet the objective
- Bayesian incorporation of real world evidence (RWE)
- Examples of Bayesian programming using R/STAN/BUGS and SAS
Bruno Boulanger, Senior Director, PharmaLex
Bradley Carlin, Senior Advisor, PharmaLex
Bayesian Tumor volume analysis with BRMS R package
In cancer drug development, demonstrated efficacy in tumor xenograft experiments on severe combined immunodeficient mice who are grafted with human tumor tissues or cells is an important step to bring a promising compound to human. A key outcome variable is tumor volumes measured over a period of time, while mice are treated with certain treatment regimens. The tumor growth inhibition delta T/delta C ratio is commonly used to quantify treatment effects in such drug screening tumor xenograft experiments In this presentation, we propose a Bayesian approach to make a statistical inference of the T/C ratio, including both hypothesis testing and a credibility interval estimate. Through a practical case, implementation, diagnosis, model selection and results with the BRMS R package will be discussed.
A Bayesian, Generalized Frailty Model for Comet Assays
This paper proposes a flexible modelling approach for so-called comet assay data regularly encountered in pre-clinical research. While such data consist of non-Gaussian outcomes in a multi-level hierarchical structure, traditional analyses typically completely or partly ignore this hierarchical nature by summarizing measurements within a cluster. Non-Gaussian outcomes are often modelled using exponential family models. This is true not only for binary and count data, but also for, e.g., time-to-event outcomes. Two important reasons for extending this family are: (1) the possible occurrence of over dispersion, meaning that the variability in the data may not be adequately described by the models which often exhibit a prescribed mean-variance link, and (2) the accommodation of a hierarchical structure in the data, owing to clustering in the data. The first issue is dealt with through so-called over dispersion models. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. In the case of time-to-event data, one encounters, for example, the gamma frailty model (Duchateau and Janssen 2007). While both of these issues may occur simultaneously, models combining both are uncommon. Molen berghs et al (2010) proposed a broad class of generalized linear models accommodating over dispersion and clustering through two separate sets of random effects. In Ghebretinsae et al, we used this method to model data from a comet assay with a three-level hierarchical structure. Whereas a conjugate gamma random effect is used for the over dispersion random effect, both gamma and Normal random effects are considered for the hierarchical random effect. Apart from model formulation, we place emphasis on Bayesian estimation.
This Workshop is open to both Members and Non-Members of PSI. Please see below for confirmation of fees.
PSI Members = £125+VAT
PSI Non-Members = £125+VAT
Please note: this event will take place online via Zoom, and has a limited number of places available.
To register for this workshop, please click here.
|Bruno Boulanger has 25 years of experience in several areas of pharmaceutical research and industry including discovery, toxicology, CMC and early clinical phases. He holds various positions in Europe and in USA. Bruno joined UCB Pharma in 2007 as Director of Exploratory Statistics. Bruno is also since 2000 Lecturer at the Université of Liège, in the School of Pharmacy, teaching Design of Experiments and Statistics. He is also a USP Expert, member of the Committee of Experts in Statistics since 2010. Bruno has authored or co-authored more than 100 publications in applied statistics and co-edited one book in Bayesian statistics for pharmaceutical research.
|Brad Carlin is a statistical researcher, methodologist, consultant, and instructor. He currently serves as Senior Advisor for Data Science and Statistics at PharmaLex, an international pharmaceutical consulting firm. Prior to this, he spent 27 years on the faculty of the Division of Biostatistics at the University of Minnesota School of Public Health, serving as division head for 7 of those years. He has also held visiting positions at Carnegie Mellon University, Medical Research Council Biostatistics Unit, Cambridge University (UK), Medtronic Corporation, HealthPartners Research Foundation, the M.D Anderson Cancer Center, and AbbVie Pharmaceuticals. He has published more than 185 papers in refereed books and journals, and has co-authored three popular textbooks: “Bayesian Methods for Data Analysis” with Tom Louis, “Hierarchical Modeling and Analysis for Spatial Data” with Sudipto Banerjee and Alan Gelfand, and "Bayesian Adaptive Methods for Clinical Trials" with Scott Berry, J. Jack Lee, and Peter Muller. From 2006-2009 he served as editor-in-chief of Bayesian Analysis, the official journal of the International Society for Bayesian Analysis (ISBA). During his academic career, he served as primary dissertation adviser for 20 PhD students. Dr. Carlin has extensive experience teaching short courses and tutorials, and won both teaching and mentoring awards from the University of Minnesota. During his spare time, Brad is a health musician and bandleader, providing keyboards, guitar, and vocals in a variety of venues.
Marie is a biostatistician engineer at IT&M STATS. She was graduated from ENSAI (National School of Statistics and Information Analysis, France) in 2019 with a master's degree specializing in statistics for life sciences. She has been working for SANOFI as a contractor for three years in the team in charge of biostatistical support to non-clinical efficacy & safety studies.
Helena Geys is Global head of the Discovery and Nonclinical Safety Statistics group at Johnson and Johnson. Helena joined J&J 18 years ago during which period she has made significant contributions in various areas of nonclinical statistics: discovery, toxicology, manufacturing. She is an active participant in many professional organizations, and has shown herself a contributor to many successful external and cross-pharma initiatives and academic collaborations leading to impactful successes in drug development strategies. The results of her research have been published in >100 methodological and applied publications on clustered non-normal data, risk assessment, spatial epidemiology, translational medicine and surrogate marker validation. In addition to her assignment at Janssen, Helena has a strong passion for teaching and mentoring. She combines her work at Janssen Pharmaceutica with a position as tenure-track professor in biostatistics at the Data Science Institute of Hasselt University (Belgium) and has mentored and coached >30 master and PhD students.