Speakers include John Hinde, Claire Brittain, Paul Frewer, Fabio Rigat, Thomas Jaki, Guiyuan Lei and Ricardus Vonk
Traditionally, Translational medicine aimed to improve the flow from laboratory research through clinical testing and evaluation to standard therapeutic practice. Translational Statistics facilitates the integration of biostatistics within clinical research and enhances communication of research findings in an accurate and accessible manner to diverse audiences. Statistical analyses has often focused on methodological approaches for the scientific aspects of the studies; translational statistics aims to make the scientific results useful in practice.
This Scientific Meeting will focus on blurring the hard line between non-clinical and clinical and move to a more iterative discussion and investigates innovative designs in early phases of drug development to increase efficiency of the development process whist also considering reproducibility, relevance, and communication.
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Translational Statistics: Relevance, Reproducibility, and Communication – John Hinde (University of Galway)
Translational Statistics: Relevance, Reproducibility, and Communication
(University of Galway)
Translational medicine, often described as “bench to bedside", promotes the convergence of basic and clinical research disciplines. It aims to improve the flow from laboratory research through clinical testing and evaluation to standard therapeutic practice. This transfer of knowledge informs both clinicians and patients of the benefits and risks of therapies.
In an analogous fashion, we propose the concept of Translational Statistics to facilitate the integration of biostatistics within clinical research and enhance communication of research findings in an accurate and accessible manner to diverse audiences (e.g. policy makers, patients and the media). Much reporting of statistical analyses often focuses on methodological approaches for the scientific aspects of the studies; translational statistics aims to make the scientific results useful in practice.
In this talk we will consider some general principles for translational statistics that include reproducibility, relevance, and communication. We will also consider how modern web-based computing allows the simple development of interactive dynamic tools for communicating and exploring research findings. Various examples will be used to illustrate these ideas.
Translation, a two way street - A Case Study of Event Related Potentials (ERPs) as a Neuroscience Biomarker
Claire Brittain (Lilly)
In early clinical drug development, biomarkers capable of providing proof of mechanism are considered critical tools and can help reduce attrition during phase II clinical trials. However, with neuroscience drugs it’s common to use different measures in rats and humans e.g. Water mazes in rats and ADAS-Cog questionnaires for Alzheimer’s in humans. They are both excellent measures in their own right but translation can be greatly improved if you start by comparing apples with apples.
This gives us 3 options:
1. Ask volunteers to swim in circular tanks looking for a hidden platform
2. Teach rats to answer complex questions on their cognitive impairment
3. Find a new measure that can used in both species and thus allows us to compare directly
This presentation will take you through the journey of how we selected our biomarker (Auditory Evoked Related Potentials) and the considerations in designing and analysing the experiments between species. I hope to show what can be achieved if we blur the hard line between non-clinical and clinical and think of it more of an iterative discussion.
Statistics for Decision Processes: Transitions between Research and Development Phases - Richardus Vonk (Bayer)
The high costs and long duration of clinical development, paired with high levels of attrition, require the quantification of the (un-)certainty when moving from preclinical animal research
to clinical development. One other, less often observed area of translation is the translation between the different development phases, where through the course of development the focus moves towards the target population. In the management of the transition risks, biomarkers may play an important role.
Further to the regulatory requirements, statistics and statistical thinking are integral parts of the internal decision making processes, particularly in early clinical development. This presentation offers an overview of the current challenges, and then concentrates on innovative statistical methods that facilitate the transitional efforts. We review the role of Bayesian methodology in this endeavour. We provide examples from the area of biomarker development, early clinical trials and proof of concept situations.
Improving Design, Evaluation and Analysis of Early Drug Development Studies (IDEAS)
Thomas Jaki (University of Lancaster)
Abstract: Drug development is a long and costly process which suffers from the major shortcoming that frequently failure is often only determined during the final stage. Advanced statistical methods for study design, evaluation and analysis, employed already at the early phases of drug development, have a great potential to increase the efficiency of the development process.
IDEAS is a European training network for 14 early stage researchers working on statistical methods for early drug development. The network is funded by the European Union’s Horizon 2020 research and innovation programme and comprises of 8 full partners and three associated partners at major European universities, the pharmaceutical industry, and consulting companies.
In this talk we will outline the structure of IDEAS and highlight two specific projects that are focusing on translation between pre-clinical and clinical studies.
The use of biomarkers in translating from pre-clinical to first in human trials in Immunology – Alun Bedding (Roche)
Abstract: In the study of immunology the use of specific biomarkers of the immune system is critical to early develop of compounds to treat auto-immune disorders such as type I diabetes and ulcerative colitis. Whilst one biomarker may be elevated for a positive response another may also be elevated, which might lead to a safety concern. It is important to use these understand the dose response of the drug, with respect to these biomarkers, with the hypothesis that they will translate into a clinical effect.
This talk will discuss how a drug program can be developed in the area to ensure proper understanding of the dose response curve. This is first done from the translation of pre-clinical discovery into the first time in human study. The importance of this cannot be underestimated given drugs of this nature have the potential to cause a cytotoxic storm if doses too high. Thoughts around addressing this will be presented.
Integrative modelling of experimental medicine clinical data shows that target engagement predicts clinically relevant biological effects – Fabio Rigat (GSK)
Abstract: Experimental Medicine studies measure multiple dependent parameters in a relatively small number of subjects. This situation, commonly known in Statistics as the “large p, small N” paradigm, calls for the use of multivariate inference to extract robust low-dimensional inferences for data interpretation and to support clinical decision making. This talk demonstrates that quadratic discriminant analysis, a simple and well established multivariate method, can be used in this context to identify a relation between target engagement (TE) of a therapeutic monoclonal antibody and the downstream changes in multiple biomarkers at the site of action. The multivariate distribution of all biomarker changes within each TE class (high or low) is used to estimate the Bayes-optimal allocation of each subject within each TE class. The accuracy of the resulting classification is found to be higher than that resulting from a random allocation, thereby establishing the statistical significance of a relation between TE and the biomarker changes. Leveraging this result, the probability distribution of the most clinically meaningful biomarker is used for planning of a proof of concept (PoC) trial endpoint focussing in on patient transitions across different disease statuses. The marginal transition probabilities showing patients’ improvement during the trial are estimated using a conjugate Bayesian Multinomial classification model.
Design of Drug Development Programs with Biomarkers: A stratified medicine approach – Paul Frewer (AZ)
Abstract: In oncology is it becoming increasing common to have targeted treatments based on a patient’s biomarker status. The talk will focus on types of designs which could be used in investigating a treatment with this intention. For example designs allowing assessment of both biomarker positive and negative populations and designs incorporating a number of different investigational medicines targeted at specific patients. There are advantages and challenges to the designs and we will focus on the statistical considerations to be aware of when developing the clinical plan.
Numbers are limited to 70 for this free event and therefore registration is a commitment to attend. Registration includes all refreshments and lunch. Registration for this meeting has now closed.
An opportunity to meet statisticians from across the pharmaceutical industry in a relaxed and informal setting. An exciting program of events and a chance to work in small groups on a data analysis challenge. Lunch provided.
A Non-PSI Event - Protecting confidentiality and privacy in clinical trial and medical data sets
We are increasingly living in a data driven world. Data are collected in many different ways for a variety of purposes. As such, concerns around protecting the privacy of individuals have increased in recent times.
A PSI Training Course - Practical Approaches to Designing Adaptive Clinical Trials
This hands-on course will provide a deep dive into 4 software packages used to design adaptive clinical trials.
The course will start by providing a general overview of adaptive designs, explaining the different type of adaptations possible and the benefits of each design. Following this, participants will be given the opportunity to have a go at designing trials in R (using RPACTS), EAST, FACTS, and nQuery.
PSI Training Course - Bayesian Practical Course using R and SAS
This practical training course will give a deep dive into performing Bayesian analyses in R and SAS. It is aimed at statisticians who need to be able to conduct Bayesian analyses as part of their day to day work. By the end of the course participants will be able to conduct their own analyses.
This webinar will address operational issues of paramount importance within the healthcare industry with a view to using statistics for the benefit of patients. In attending this webinar, you'll hear more about work being conducted to address some operational issues we face in the health care industrys e.g. patient rectuitment, drug supply and meeting NHS 18 week targets.
PSI Toxicology SIG workshop – 16th and 17th March 2020
The Toxicology SIG provides a forum for statisticians working in regulatory/investigative toxicology, as well as most other pre-clinical areas, to discuss issues and interact with one another.
This 1.5-day workshop will involve approximately 20 statisticians, focusing on discussions around “best practice” in the statistical analysis of various data types.
The afternoon of Day 1 will include a 4.5 hour Bayesian training course focused towards applications in toxicology/pre-clinical, provided by Prof. Dr. Katja Ickstadt and is included in the workshop fee.
The cost will be £270 including VAT per delegate, inclusive of food and one night’s accommodation (and the training course). The workshop is being held at the Crowne Plaza Hotel, Heathrow.
The agenda and topics that will be discussed are yet to be finalised, but please get in touch with firstname.lastname@example.org if you have suggestions. Full details will be circulated in the coming weeks.
This course is aimed at Statisticians and Programmers experienced in SAS, but little or no experience with R.
An Introduction to R studio and the R language, statistical graphics, programming statistical models, simulations and more…
Non-proportional hazards and applications in immuno-oncology
Designs of clinical trials with time to event primary endpoints usually rely on hazards being constant over time. A major challenge in immuno-oncology is the delayed onset of benefit with such therapies and the presence of non-proportional hazards. The impact of this needs to be accounted for in sample size calculations, analysis methodology and reporting. At this meeting, we will examine possible strategies to handle such features, which may not be fully known when the trial is initiated.
The ITIT course will take 25 delegates new to the industry on a complete drug development experience from discovery to marketing. They will visit 6 companies from October 2020 to July 2021 to learn about 6 topics from experts in their field. The ITIT course will have 6 sessions in continental Europe and 3 - 4 sessions in the UK. It promises to be a truly memorable course.