• Maths Meets Medicine 2018

    Dates: 05 – 05 Nov, 2018

    The Careers and Academic Liaison Committee (CALC) will be hosting their third Maths Meets Medicine event this year at the University of Reading. The event provides the opportunity for KS4 students to come and learn about how the mathematical discipline of statistics plays a crucial role in the development of new medicines.  The day will involve three interactive and fun statistics-based workshops, which aim to complement and extend the statistical concepts covered in the KS4 and KS5 curriculums, followed by a campus tour of the university. We hope that this event will raise awareness of some of the interesting career opportunities that are available using mathematics and statistics and inspire students to think beyond more traditional applications of mathematics such as finance and teaching. 

  • EFSPI/PSI Webinar: Do you understand the patient point of view on benefit-risk tradeoffs? Introduction and case studies of stated preference elicitation methods

    Dates: 20 – 20 Nov, 2018

    Time: 15:00- 16:30 (UK time)
     Alexander Schacht (Lilly), Marco Boeri (RTI-HS), Shahrul Mt-Isa (MSD), Brett Hauber (RTI-HS) and Daniel Saure (Lilly)

    Do you know what patients value most in a treatment you are developing to reassure that they would best benefit from the drug, having considered what is important to them? For example, if a patient had to choose between a highly effective drug with a bad side effect profile and a less effective drug with minimal side effects, which would they choose? Rooted in traditional economic theory, stated-preference methods can help achieve a better understanding of the patient view point on benefit risk tradeoffs.

    This webinar proposes a short primer on stated preference methods and how they can be used to explore the patient preferences for specific drug profiles that are currently available or may be developed in the near future. The theory will be then discussed as applied to two case studies using quantitative preference methods.


    Time Agenda
    15:00 – 15:05
    Welcome and Introduction
    Alexander Schacht (Lilly)
    15:05 – 15:20
    A primer on Stated Preference: Definitions/introduction
    Marco Boeri (RTI-HS)
    15:20 – 15:30
    Why is patient view important?
    Shahrul Mt-Isa (MSD)
    15:30 - 16:00

    Case studies
    Brett Hauber (RTI-HS)
    Daniel Saure (Lilly)

    16:00 – 16:30

    Q&A and discussion

    Alexander Schacht (Lilly, moderator)
    Marco Boeri (RTI-HS)
    Shahrul Mt-Isa (MSD)
    Brett Hauber (RTI-HS)
    Daniel Saure (Lilly)


    About the Presenters:

    Alexander Schacht
    Alexander Schacht
    Alexander Schacht (PhD), Principal Research Scientist, Global Statistical Sciences leads a group of 5 European based statisticians driving the statistical activities around launch preparation including HTA submission to support access and commercialization in different auto-immune diseases.

    After 2 years at Boehringer Ingelheim, Alexander joined Lilly in 2004 and held various positions within statistics with a focus on neurosciences working on phase I, III, and IV in areas like Alzheimer, Schizophrenia, ADHD, Depression, and Pain.
    Alexander received his PhD in Biometrics in 2002 from the University of Göttingen on work related to non-parametric analysis of covariance. For the publication based on this, he was awarded the 1st. Gustav-Adolf-Lienert Price in 2009 by the German region of the International Biometrical Society. He has published both methodological papers (e.g. on network-meta-analysis, non-inferiority approaches for time-to-event data) and medical papers including more than 60 papers in peer-reviewed biomedical journals. He is a regular speaker at both medical and statistical international conferences. As the chair of the special interest group on benefit-risk of the European Federation of Statisticians in the Pharmaceutical Industry, Alexander is leading and promoting research on quantitative assessments of benefit-risk. He is interested in all aspects of launching new treatments.

    Marco Boeri
    Marco Boeri

    Marco Boeri (PhD), is a Senior Research Economist at RTI-HS. Dr. Boeri was previously lecturer in Environmental Economics since 2013 and has 9 years of experience in preference assessment in environmental and health economics and 2 years of experience in private financial sector in Marketing. Dr. Boeri has extensive knowledge and experience in experimental design, survey development and modelling data from discrete choice studies in health, food and environmental economics. His research focusses on comparing different and innovating preference analysis methods (i.e. regret minimization vs. utility maximization or structural choice modelling) at individual and household level. He has co-authored the first applications of the Random Regret Minimization model in both environmental and health economics and he has published in several applied economics journals across different disciplines including: Preventive Medicine, Journal of Health Economics, Social Science and Medicine, Environmental and Resource Economics, Transportation Research Part A, Journal of Economic Behavior and Organization, demonstrating the ability of employing his methodological tool at top levels in different topics and fields. Dr. Marco Boeri is interested in environmental and resource economics, health economics, energy economics, micro-econometrics, non-market valuation, choice experiments, preference analysis: regret minimization vs. utility maximization, consumer behavior.

    Shahrul Mt-Isa
    Shahrul Mt-Isa
    Shahrul Mt-Isa (PhD), is an Associate Principal Scientist in the Health Technology Assessment (HTA) Statistics research group in the MSD Research Laboratories (MRL) Biostatistics and Research Decision Sciences (BARDS) organisation. He is responsible for HTA statistical analyses in clinical study portfolios in antiviral, antibacterial, and oncology indications to support HTA dossiers submission globally. Within MSD, he co-leads the MRL Benefit-Risk (BR) Subteam to innovate and implement novel methodologies and processes for BR Assessment; co-supervises postdoctoral research in statistical learning in BR; and leads a MSD-academia research integration and collaboration on incorporating novel BR assessment methodologies into clinical trials. Externally, he is an active member of the EFPSI BR and HTA Special Interest Groups. Prior to joining MSD, he was an academic statistician at the School of Public Health, Imperial College London, where he currently holds an Honorary Fellowship.

    Brett Hauber
    Brett Hauber, PhD

    Brett Hauber (PhD), is a Senior Economist and the Vice President of Health Preference Assessment at RTI-HS. He has more than 20 years of academic, research, and government experience in health and environmental economics. His primary area of specialization is in conducting conjoint analyses and discrete-choice experiments to quantify preferences for medical interventions and health outcomes. He also has extensive experience in conducting benefit-risk analysis of patients and other health care decision makers. He has studied the theoretical and empirical relationships among various health utility measures.

    Dr. Hauber regularly teaches courses on conjoint analysis and discrete-choice experiments. He was a member of to the Patient-Centered Benefit-Risk Steering Committee of the Medical Device Innovation Consortium (MDIC) and was the principal investigator for developing the Catalog of Methods for Assessing Patient Preferences for Benefits and Harms of Medical Technologies for MDIC. He is currently a member of the scientific advisory board for the IMI-PREFER project and an advisor to a number of initiatives led by industry and patient-advocacy organizations to incorporate patient preferences in regulatory and reimbursement decision making in multiple disease areas. Dr. Hauber's research has been published in numerous health outcomes and medical journals.

    Daniel Saure

    Daniel Saure was born in the Bad Arolsen (Germany) in 1986. From 2006-2012 he studied Maths & International Economics in Mainz [B.Sc. Mathematics, M.Sc. Mathematics], before working as study biometrician and research scientist at the  Institute of Medical Biometry and Informatics (IMBI) in Heidelberg [Dr. sc. hum. Medical Biometry]. Daniel's Doctoral thesis was about application of sequential meta-analysis in drug development process in order to early detect safety signals. In June 2016 Daniel joined Lilly, working as a research scientist. His hobbies and interests include Triathlon, Barista and Hiking.



    Registration for this webinar is free. Please click here to register.

  • Introduction to R & Regression Models in R

    London | Dates: 21 Nov, 2018
    For more information, please visit www.rss.org.uk
  • One Day Course: Practicalities in designing, grant funding, setting up and running a Continual Reassessment Method (CRM) dose finding phase I trial

    Dates: 28 – 28 Nov, 2018

    Course Tutors

    This course is delivered by a group of statisticians and trial managers with a wealth of expertise in running CRM trials. The group was formed through the NIHR early phase clinical trials statistics group and consists of:

    Andrew Hall, Debbie Sherratt and Sam Hinsley: Clinical Trials Research Unit, University of Leeds

    Christina Yap and Eszter Nagy: Cancer Research UK Clinical Trials Unit, University of Birmingham

    Graham Wheeler: Cancer Research UK & UCL Cancer Trials Centre, University College London

    Jane Holmes: Oxford Clinical Trials Research Unit, University of Oxford

    Sharon Love: MRC Clinical Trials Unit, University College London

    Target Audience

    This course is designed for anyone running a phase I dose finding trial who wants to learn more about the CRM. The course focuses on the CRM concept and practicalities, rather than the pure associated statistical methodology, and is aimed at clinicians, data managers, trial managers and statisticians, in both academic and pharmaceutical institutions.

    Cost and Registration

    Academic employees - £150

    Industry/commercial employees - £300

    Last registration date - 16th November

    Registration and further information can be found at: https://tinyurl.com/CRMcourse2018

    Questions and queries may be directed by email to Andrew Hall or Sam Hinsley (ctru-crm@leeds.ac.uk)

    Please click here to view the flyer.

  • PSI Webinar: Avoiding Pitfalls in Supervised/Unsupervised Learning

    Dates: 29 – 29 Nov, 2018
    Time: 14:00 - 15:30 UK Time
    Presenters: Ilya Lipkovich (IQVIA), Alexander Schacht (Lilly) and Andy Nicholls (GSK)

    As the availability of big data increases and statisticians assist with predicting outcomes or understanding patterns in an ever-wider variety of scenarios then supervised and unsupervised learning methods become increasing called upon. Such machine learning algorithms offer the opportunity to understand potential predictors or clusters amongst large datasets, but are also subject to the risks of overfitting or over-interpretation. This Webinar seeks to introduce ideas and share experiences in this field.

    The talks will introduce several supervised and unsupervised learning methods and cover data-driven subgroup identification in clinical trials, and case studies of implementation clustering algorithms.

    Click here to view the flyer. 

    Click here to register.

     PSI Member  Free
     Non-member  £20 (plus VAT) 

  • Multilevel Modelling

    London | Dates: 06 Dec, 2018
    For more information, please visit www.rss.org.uk
  • Advanced Programming in R

    London | Dates: 11 Dec, 2018
    For more information, please visit www.rss.org.uk
  • Decision-making in drug development

    Dates: 12 Dec, 2018
    Taking decisions during the development of a new drug requires combining many and varying pieces of information. The interconnections between them are often only partially known, reflecting the complexity of the context in which drugs are evaluated and the cognitive load required for health care decisions. Decision-makers need quantitative tools to support informed decisions, with transparent processes that synthesize the whole available information in order to evaluate the success associated to different options.

    This meeting aims to bring together statisticians from the pharmaceutical industry and academia to hear about recent advances in statistical methods for quantitative decision-making in drug development.

    Confirmed speakers include:

    Anthony O'Hagan
    University of Sheffield 
    Introduction to decision-making
    Juan Abellan
    Prior elicitation to support quantitative decision-making
    Paul Frewer
    Astra Zeneca
    Decision Making in Early Clinical Development: The framework used within AstraZeneca
    Maria Costa
    Benefit-Risk Assessment in Drug Development
    Nigel Stallard
    University of Warwick
    Decision-making in phase II/III trials using early endpoint data
    Tom Parke
    Berry Consultants
    Using simulations to optimize drug development decision-making

    Poster Session - Call for Abstracts:

    If you wish to present a poster, please send an abstract to Gaëlle Saint-Hilary (gsainthilary@gmail.com) by October 31st 2018. The notification of acceptance will be provided by November 9th 2018.

    For more details and to view the flyer, please click here.
  • PSI One Day Meeting: New Emerging Topics around Estimands and ICH Addendum

    Dates: 29 Jan, 2019

    The draft ICH E9 addendum on estimands and sensitivity analysis was released back in July 2017 and (more than 1000) comments are back. All stakeholders are gaining the necessary experience and familiarity with estimands along with the associated challenges and methodologies. The language and thinking behind causal inference is well suited to this area.

    The PSI Scientific Committee have put together this one day meeting to share and discuss new emerging topics around estimands and the ICH addendum. The aims of the event are to:

    • Share the feedback from the public consultation on the draft ICH E9 addendum
    • Explore the estimand concept within health technology assessments
    • Describe how casual inference fits into the area of estimands
    • Present case studies illustrating the implementation of the estimand framework and the use of causal inference methodology


    Time Agenda 
    09:30 - 10:00

    Registration, Welcome and introduction

    10:00 - 10:40

    ICH E9 addendum:  Key themes raised during public consultation
    Chrissie Fletcher (Amgen on behalf of the ICH E9 Working Group)

    10:40 - 11:20

    The exciting new world of the ‘Estimand’
    Anja Schiel (Norwegian Medicines Agency)

    11:35 - 12:15

    Estimand and analysis considerations of Phase 3 clinical trials involving CAR-T – A case study in lymphoma
    Emmanuel Zuber, on behalf of Novartis team 

    12:15 - 12:55

    How causal inference can fit the needs of a clinical trial (well kind of)
    Michael O’Kelly (IQVIA)

    13:45 - 14:25

    Using causal graphs to understand estimands and estimation
    Ian White (UCL)

    14:25 - 15:05

    Towards more reliable Mendelian randomization investigations
    Stephen Burgess (University of Cambridge)

    15:25 - 16:05

    Non-inferiority causal inference case study

    16:05 - 16:30

    Panel Discussion
    Speakers and Yolanda Barbachano (MHRA)



    Chrissie Fletcher
    (Amgen on behalf of the ICH E9 Working Group)

    ICH E9 addendum:  Key themes raised during public consultation

    Abstract: The draft ICH E9(R1) and addendum to E9 incorporating a new framework on estimands and sensitivity analyses in clinical trials was released for public consultation in the first region at the end of August 2017 and the public consultation ended in the last region by the end of April 2018.  The ICH E9 working group met in June 2018 to review the 1200+ comments that were submitted.  The ICH E9 addendum and E9(R1) is scheduled to reach step 4 and sign-off by all the ICH regions in June 2019. 

    The key themes and topics raised during the public consultation of the ICH E9 addendum will be presented.  A summary of the E9 working group discussion of the key aspects raised during public consultation and an update of how the E9 working group are trying to address the comments in the final E9 addendum will be provided. 

    Anja Schiel
    Anja Schiel (Norwegian Medicines Agency)

    The exciting new world of the ‘Estimand’

    In August 2017 the ‘ICH E9 (R1) Addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials’ was released for consultation and has stirred discussion and excitement, mainly in the regulatory world. But, what does this new framework offer to other stakeholders? The uptake of new concepts into health technology assessment is notoriously difficult because there are fundamental differences in the evidence requirements needed to establish a risk-benefit and those to establish relative effectiveness. The Estimand framework offers new possibilities to allow studies to be designed with both perspectives in mind, wherever possible. To designs studies based on the Estimand concept requires a wider communication between the different specialists involved in drug development, from pharmacologists to clinical experts and statisticians. However, this dialogue should in fact be expanded all the way to the health economists to fully harness the potential of this new framework.

    Emmanuel Zuber, on behalf of Novartis team  Estimand and analysis considerations of Phase 3 clinical trials involving CAR-T – A case study in lymphoma

    Marked by the recent approval of the first chimeric antigen receptor T cell (CAR-T) therapies, these autologous therapies provided patients with new options to fight cancer. Unique challenges arises in the design and analysis of randomized studies involving autologous CAR-T therapies. Because the CAR-T treatment strategy involves personalized manufacturing before patients can receive the final product, the scientific objective and its associated estimand must be carefully thought through to allow appropriate interpretation of study results. Different testing procedures and estimation methods will be discussed in a case study of Phase 3 clinical trial.

    Michael O’Kelly (IQVIA)


    How causal inference can fit the needs of a clinical trial (well kind of)

    Abstract: Randomisation can be thought of as providing “a ‘reasoned basis’ of testing the null hypothesis of no effect without resort to distributional assumptions such as normality” (Fisher); and indeed randomisation has been accepted as providing as close to causal inference as was needed for the approval of new treatments. However, clinical triallists are now seeing that, over the time of follow-up, intercurrent events (ICEs) result in changes to treatment. Because of this, the planned trial of a randomized treatment regimen can morph into no more than a survey whose only inference from randomisation is confined to the mere act of assigning a plan of treatment, a survey whose inference about the treatment regimen itself loses much of its credibility because those ICEs constitute non-randomized changes and distortions of the regimen to be tested. This presentation tries to convey in a non-technical manner the idea of causal inference and how it can work and be of use in clinical trials, making at least a gesture towards inference about outcomes as actually planned. Noting the overlap with missing data research, the presentation then shows a detailed example of the use of one approach to causal inference for an outcome censored by death. From the example it may be concluded that, while causal inference is probably invaluable for many clinical trial designs including the example presented, results from causal inference have their own limitations and will often need to be interpreted alongside other results, even if the other results are more open to bias than those from causal inference.

     Ian White

    Ian White (UCL)


    Using causal graphs to understand estimands and estimation

    I will talk about causal inference and estimands from the perspective of DAGs (directed acyclic graphs), which are widely used for causal inference in observational studies. DAGs allow complex statistical issues to be represented pictorially yet still rigorously. I will briefly describe the ideas of DAGs, then draw suitable DAGs for randomised trials with non-randomised treatment changes, and use them to discuss some key estimands and how these may be estimated. The talk will be conceptual rather than mathematical, and will point to types of approach rather than specific approaches.

    Stephen Burgess (University of Cambridge)

    Towards more reliable Mendelian randomization investigations

    Mendelian randomization is a technique for assessing the causal role of a modifiable risk factor on a disease outcome using genetic data. If genetic variants associated with the risk factor are also associated with the outcome, this increases the plausibility that the risk factor is a causal determinant of the outcome. However, if the genetic variants in the analysis do not have a specific biological link to the risk factor, then causal claims can be spurious. Recent advances in genome-wide association studies and the increasing availability of publicly available summary data on associations of genetic variants with risk factors and disease outcomes in large sample sizes have enabled powerful Mendelian randomization analyses to be performed relatively quickly and simply. I will present an overview of Mendelian randomization approaches from monogenic analyses, in which genetic variants are taken from a single gene region, to polygenic analyses, which include variants from multiple regions. In particular, I will discuss the reliability of such analyses and present statistical approaches for increasing their reliability.

     PSI Member  £40 + VAT
     Non-Member  £135 + VAT (This includes PSI membership for 2019)

    To register, please click here.