• Impact of AI on Clinical Development

    Dates: 11 – 11 Sep, 2019

    How to find us: UCB, 208 Bath Road, Slough, SL1 3WE

    In association with PSI, UCB and Cytel are delighted to invite you to join a symposium, educating on Artificial Intelligence (AI) approaches and their impact on clinical development.

    With so many recent advances in AI, it is important both for statisticians to keep up to date with the most recent methods and be involved in guiding their application to the most pressing statistical challenges. This one-day event will cover cutting edge examples of how data science and statistical sciences are intersecting, and where attendees can fit into that space. Come to learn and discuss why different approaches matter when looking at clinical development data.

    Here's 5 reasons why you can't afford to miss it:

    Hear from the experts leading the way in AI: We have exciting speakers from the University of Oxford, PSI, Roche, Cytel and UCB

    2 Observe real case studies: Learn from current Industry challenges and successes

    3 Statistical mind shift: Appreciate the importance of different approaches when looking at the data and augment existing methods

    4 Making machine learning more accessible: Technology showcase of latest tools

    5 Network with your peers: Exchange insights and help shape the new paradigm




    09:00 - 09:30

    Registration and Coffee

    09:30 - 9:45

    Karim Malki, Head, Predictive Analytics, UCB

    09:45 - 10:30

    Technology Showcase

    10:30 - 11:30

    Understanding activity patterns through wearable devices and AI algorithms
    Chris Holmes, Professor of Biostatistics in Genomics in the Nuffield Department of Clinical Medicine and the Department of Statistics, University of Oxford 

    11:30 - 11:45


    11:45 – 12:30

    The need for data science in the new clinical development paradigm
    Francis Kendall, Senior Director, Biostatistics and Programming, Cytel

    12:30 - 13:30

    Lunch and Networking

    13:30 - 14:15

    Why statisticians should be driving machine learning and AI projects
    Moira Verbelen, Principal Statistician, UCB

    14:15 - 15:00

    Michelle Jones, Senior Director, Clinical Informatics, Covance, on behalf of the PSI Data Science SIG

    15:00 - 15:15

    Coffee Break

    15:15 - 16:00

    Roche's experience developing Advanced Analytics Communities both internally and externally
    Chris Harbron, Expert Statistical Scientist, Roche

    16:00 – 16:45

    Panel Discussion

    16:45 – 17:00

    Closing Remarks

    17:00 – 18:00

    Drinks Reception and Networking

    Please click here to register, please email PSI@mci-group.com to advise of any dietary requirements ASAP. 


    £60 + VAT
    Registration deadline is Friday 6th September 2019




    Technology Showcase

    Abstract: This will be a technical demonstration highlighting some of the latest technology available for performing machine learning and other high intensity computational tasks, including a demonstration of Intel’s portable neural compute stick and a look at how new advances, such as quantum computing, will change the technological landscape.

    Chris Holmes (University of Oxford)

    Understanding activity patterns through wearable devices and AI algorithms

     New measurement technologies such as wearable devices coupled with AI algorithms, that can learn from large scale streaming data, have the potential for improved evaluation and monitoring of treatment interventions. In this talk we review the prospect for AI to better characterise population activity variation through wearable tech including an analysis of accelerometer data from 100,000 participants in UK BioBank.

    Frances Kendall (Cytel)

    The need for Data Science in the New Clinical Development Paradigm

    Abstract: This talk will set the stage on why Data Science is needed to support a New Clinical Development paradigm and what are the drivers of change. It will then put forward an idea on what that Paradigm could look like with examples of work that demonstrate this direction.

    Moira Verbelen (UCB)

    Why statisticians should be driving machine learning and AI projects

    Advancements in computer science have popularised the widespread use of machine learning and AI. Although methods were mainly developed by computer and data scientists, they are rooted in statistical science. Statisticians are ideally placed to guide the implementation of these ‘new’ approaches in pharma. In-depth understanding of statistical concepts and model fitting are essential skills required to avoid pitfalls such as poor algorithm design, overfitting and incorrect interpretation of results. These considerations and the ensuing value of statisticians’ involvement are even more important in clinical development, where datasets tend to be smaller than those typically used for AI.

    Michelle Jones, Covance (on behalf of PSI Data Science SIG)





    Chris Harbron (Roche)

    Roche's experience developing Advanced Analytics Communities both internally and externally

     Roche has been successful in building an internal advanced analytics community consisting of over 750 data scientists from across the global Roche organization as well as establishing a number of external advanced analytics partnerships.  This talk will discuss how Roche have approached this effort as well highlighting some of the successes and challenges, including crowd sourcing the internal community to tackle key scientific research questions using machine learning.  

  • A PSI Training Course on Improving Influence and Increasing Impact: Communication Skills for Industry Statisticians

    Dates: 16 – 16 Sep, 2019


    UCB Pharma
    Allée de la Recherche 60, 1070 Anderlecht, Belgium. Brussels
    16th September 2019
    Course runs from: 10:00 – 17:00 (registration from 9:30)

    The landscape is changing across the pharma industry and as statisticians, in order to continually add value, we must make sure we adapt. This course will focus on what this means for statisticians outside the technical aspects of their role. Of critical importance is self-awareness during our interactions, working effectively in teams, influencing, being customer focussed and understanding our own consulting and leadership styles.

    Now more than ever we need to be creative and influential thinkers with business acumen who can work with our colleagues from other disciplines, not just be technical experts - we need to be proactive partners with strong communication skills

    This 1-day course will lead participants, in a non-threatening and encouraging atmosphere through steps to focus on improving the key skills of making impactful verbal and written communications.

    The first half of the session will cover topics such as poster preparation, written communication documents such as reports and briefing documents and also verbal communication in presentations. The second half of the session will focus on communication in teams and identifying key roles that need to be taken on.

    It is designed to be interactive and lively, with a focus on the statistician rather than the statistics and will include workshops to practise the skills and behaviours discussed. 

    This course is aimed at statisticians who want to improve their consultancy style interactions within their internal project teams and/or with external customers and understand the impact of their own behaviours and interaction preferences. 

    The following topics will be covered: 

    - Preparing poster layout and content
    - Skills and tips for preparing written reports and abstracts
    - Preparing and delivering presentations
    - Effective team communication and key roles
    - Delivering negative messages to teams with positive impact


    Registration costs (includes lunch and refreshments).


    Registration Fee before 16th August 2019

     Registration Fee after 16th August 2019

    PSI Member     

     £345 + VAT

     £395 + VAT


     £440 + VAT (includes PSI membership for 2019)

      £490 + VAT (includes PSI membership for 2019)

  • Introduction To Industry Training Course 2019

    Dates: 01 Oct, 2019

    Are you a PSI member with approx. 1-3 years' experience as a Statistician or a Statistical Programmer within the industry?


    Final dates to be confirmed

    PSI Member: £1,050 + VAT
    Non-Member: £1,145 + VAT

    AIM: To describe the drug development process from research right through to research, toxicology, data management & role of the CRO, clinical trials, product development & manufacture and marketing.



    For further information contact:

    Alex Godwood & Zelie Bailes

    Sosei Heptares 
    Steinmetz Building
    Granta Park
    CB21 6DG
    United Kingdom


       Email: Alex.Godwood@SoseiHeptares.com ; zelie.a.bailes@gsk.com


  • PSI Webinar: Subgroup Analysis

    Dates: 01 – 01 Oct, 2019
    2:00pm to 5:00pm

    Following the workshop discussing the EMA draft guideline on the investigation of subgroups in confirmatory clinical trials, an EFSPI/PSI Special Interest Group has been exploring methods which could be used within the framework of such guidance.  These approaches have also been discussed with European regulatory statisticians. 

    The main focus of this webinar will be to provide a summary of the key methods recommended by the SIG, presented by Aaron Dane, along with some perspective on the proposed methods provided by Rob Hemmings given his experiences as a European regulatory reviewer.  David Svensson will also outline the future direction and research planned for the EFSPI/PSI SIG, which will be followed by a discussion of the approaches and regulatory perspectives presented.

    Click here to register
  • PSI Scientific Committee Webinar: Statistical Challenges in Analytical Comparability and Biosimilarity Assessment

    Dates: 15 – 15 Oct, 2019
    2:00pm to 3:30pm


    • Thomas Lang (EMA BSWP / AGES)
    • Johanna Mielke (Bayer)
    • Bruno Boulanger (Pharmalex)

    This webinar will discuss statistical requirements for the assessment of analytical comparability and similarity assessments, for example between biosimilars and reference products or before and after manufacturing changes. This topic has been the subject of a recent EMA reflection paper and an EFSPI working group. New strategies are proposed as an alternative to mean comparisons and include the assessment of ranges, inferential approaches or the use of Bayesian methods.

    This webinar is free to attend, to register please click here.

  • PSI One Day Meeting: Time-to-event and Recurrent Event Endpoints in Clinical Trials

    Dates: 29 Oct, 2019
    Venue:  Novartis Campus, Basel, Switzerland

    Please click here to view the directions

    This exciting one-day workshop will cover a wide range of statistical aspects relating to event-driven trials. We have assembled a group of very knowledgeable speakers, who will share their thoughts, ideas and experiences, including case studies, on a range of particular issues relating to planning, conduct and analysis of survival and recurrent event trials. The first half of the day will be dedicated to time-to-event endpoints and adverse events with the afternoon focusing on recurrent event endpoints that are associated with a terminal event.


    Time Agenda 
    08:30 - 09:00

    Registration, Welcome and introduction

    09:00 - 12:00

    Analysis of time-to-event data and safety events

    Valentine Jehl (Novartis) on quantitative assessment of adverse events in clinical trials - comparison of methods at an interim and the final analysis.

    Qing Wang (Roche)  on comparison of time-to-first event and recurrent event methods in multiple sclerosis trials.

    Filip De Ridder (Janssen) on a Time to event model for early efficacy signal dose finding in epilepsy clinical trials.

    Andrew Thomson (EMA) TBD

    12:00 - 13:00

    Lunch break

    13:00 - 16:00

    Recurrent events with associated terminal events

    Patrick Schlömer & Arno Fritsch (Bayer) on the topic of estimands and estimators for recurrent events with an associated terminal event.

    John Gregson (London School of Hygiene & Tropical Medicine) on the topic of practical experience of modelling repeat events in the REDUCE-IT and COAPT trials.

    Tobias Bluhmki (University of Ulm) on the topic of simulating recurrent events with associated terminal events.



    Valentine Jehl

    Quantitative assessment of adverse events in clinical trials – comparison of methods at an interim and the final analysis.


    In clinical study reports, adverse events (AEs) are commonly summarized using the incidence proportion despite cumulative incidence function been advocated as the most appropriate method to account for different exposure time and competing events.

    In this presentation, we compare different methods to estimate the probability of one selected AE. Besides considering the final analysis at the time of the Clinical Study Report, we especially investigate the capability of the proposed methods to provide a reasonable estimate of the AE probability at an early interim analysis. Robustness of the methods in the presence of a competing event is evaluated using data from a breast cancer study. The potential bias of each method is quantified in a simulation study.


    Valentine Jehl is a senior quantitative safety scientist at Novartis. She received her Master’s degree in applied mathematics at the Louis Pasteur University in Strasbourg.

    She started her carrier as statistician with a CRO in Brussel. She then joined Novartis in Basel, where she supported major submissions and development programs for the oncology franchise. After 9 years in this role, Valentine joined the quantitative safety group in April 2016, where she now promotes the use of quantitative methods for safety, with a particular focus on Adverse Drug Reactions.

    Qing Wang

    Comparison of time-to-first event and recurrent event methods in multiple sclerosis trials.


    Randomized clinical trials in multiple sclerosis (MS) frequently use the time to the first confirmed disability progression (CDP) on the Expanded Disability Status Scale (EDSS) as an endpoint. However, especially in progressive forms of MS where CDP is typically the primary endpoint, a substantial proportion of subjects may experience repeated disability events. Recurrent event analyses could therefore increase study power and improve clinical interpretation of results.

    We present results from two simulation studies which compare analyses of the time to the first event with recurrent event analyses (including negative binomial, Andersen-Gill, and Lin, Wei, Ying, and Yang models). The first simulation study is generic and recurrent events data is simulated according to a mixed non-homogeneous Poisson process.  The second simulation study is MS-specific: we first simulate longitudinal measurements of the ordinal EDSS scale using a multi-state model and then derive recurrent event data based on this.  Simulation parameters are chosen to mimic typical MS trial populations in relapsing-remitting or primary progressive MS, respectively, and include scenarios with heterogeneity (frailties). Based on the results from the simulation studies, the presentation will conclude with recommendations for the choice of the endpoint, and analysis method of MS trials with disability progression endpoints.


    Qing is a statistician working at Roche Basel. She is currently the project lead statistician for the Ocrevus (ocrelizumab) program, and had been supporting the program from initial study readouts, filing preparations, US and EU approvals, to market access and scientific communication over the past years. Before joining Roche in 2014 she has worked in HIV research at the Institute for Clinical Epidemiology and Biostatistics at University Hospital Basel. She received her Master in Mathematics and PhD in Biostatistics at the University of Cambridge (UK).

    Filip De Ridder

    A time to event model for early efficacy signal dose finding in epilepsy clinical trials.


    Time to-event endpoints have been proposed as alternatives to establish the effect of anti-epileptic drugs in clinical trials. These endpoints may reduce exposure to placebo or ineffective treatments, thereby facilitating trial recruitment and improving safety. Time to baseline seizure count is defined as the number of days until a subject experienced a number of seizures equal to the baseline seizure count.  A post hoc analysis of completed Phase III trails with perampanel showed that an analysis of the time to baseline count endpoint is consistent with the classical endpoints (median % seizure rate reduction, percentage of patients achieving a 50% or greater reduction in seizure frequency)1.

    We investigated the performance of the time to baseline seizure endpoint by (1) a post hoc analysis of topiramate and carisbamate clinical trial data and (2) clinical trial simulation using a longitudinal model for daily seizures counts. This model included key features of daily seizure count data, such as a large between subject variability in baseline seizure rate and drug response, a large variability of the number of seizures per day and clustering of seizures over time.

    The re-analysis of topiramate and carisbamate clinical trial data confirmed the relationship between the median time to baseline seizure count and the classical endpoint of median % seizure rate reduction that was observed with perampanel. In addition, the observed relationship agreed with the one that was predicted by the simulation model.

    Clinical trial simulations were used to investigate the performance of a proof-of-concept study design using the time to baseline seizure count endpoint. The study consisted of a 4-week prospective baseline, followed by a 4-week double blind treatment period, after which subjects would exit the study if they had reached or exceeded their baseline seizure count, or would continue for another 8-weeks. These simulations showed that (1) with relatively small sample sizes (~ 20/arm) the design is able to identify clinical relevant treatment effects (30% - 50% seizure rate reduction); (2) a 4-week baseline period provides enough information on the baseline seizure count and (3) the length of exposure of subjects to placebo or an inactive treatment is strongly reduced as compared to a classical design. 


    Filip De Ridder is a Senior Scientific Director in the Statistical Modeling & Methodology group of Janssen R&D. Twenty years ago, he was one of founders of the Modeling & Simulation group at Janssen bringing together statisticians and pharmacometricians to apply modeling & simulation techniques in clinical drug development.  Since then he has worked on M&S projects in the context of PK/PD modeling, dose response modeling and clinical trial design, mainly in neuroscience and infectious diseases.


    Andrew Thomson



    Arno Fritsch & Patrick Schlömer (Bayer)

    Estimands for recurrent events in the presence of a terminal event – Considerations and simulations for chronic heart failure trials.


    In this presentation, we will discuss potential estimands according to the ICH E9 addendum framework that can be addressed for recurrent events when there is a non-negligible risk for a terminal event, typically death.

    As an application, we consider trials in chronic heart failure (HF). Here in the past, the standard (composite) primary endpoint was the time to either hospitalization for HF or cardiovascular (CV) death. Since many patients experience recurrent HF hospitalizations, there is interest to include these events into the primary endpoint. We consider two estimands, one that focuses only on the total number of recurrent HF hospitalizations and another one that includes CV death as an additional composite event.

    We present results of an extensive simulation study that investigated which standard methods for analyzing recurrent event data estimate the above-mentioned estimands. In addition, we compared the efficiency of recurrent event estimands and time-to-first event estimands.


    Arno Fritsch received his PhD in Statistics from the University of Dortmund, Germany, in 2010. Since then he has been working at Bayer as a clinical statistician, mainly on the design, analysis and submission of cardiovascular trials. Since 2017 he has the position as Group Leader Europe in the cardiovascular statistics department. His methodological interests include handling of missing data, analysis of subgroups and recurrent events. He is one of the co-authors of the application for an EMA qualification opinion on use of recurrent events.

    Patrick Schlömer received his PhD in Statistics from the University of Bremen, Germany, in 2014 for his work on group sequential and adaptive designs for three-arm non-inferiority trials. Since then he has been working at Bayer as a clinical statistician in the cardio-renal area with increasing responsibilities, now holding the position Lead Statistician. His methodological interests include group sequential and adaptive designs, multiple comparison procedures and recurrent events. He is one of the co-authors of the application for an EMA qualification opinion on use of recurrent events.

    John Gregson
    (London School of Hygiene & Tropical Medicine)

    The value of including recurrent events in the analysis of cardiovascular outcomes trials.


    Including recurrent events in analyses of clinical trials can increase power and lead to a more complete assessment of treatment benefit. There are several strategies to analysing repeat events, but little practical guidance as to which are best in any given scenario. Several methods for analyses of repeat events in trials will be compared, including Andersen-Gill, Wei-Lin-Weissfeld, negative binomial regression, and joint frailty models. The assumptions underlying each of these methods, and their various advantages and disadvantages will be outlined using data from recent large cardiovascular trials.


    John Gregson is an Assistant Professor in Medical Statistics at the London School of Hygeine and Trpoical Medicine. He has a range of experience in the analysis of cardiovascular clinical trials, many of which have been published in high impact journals (e.g. NEJM, Lancet, JACC).  As well as an interest in the applied analysis of randomised clinical trials and epidemiological studies, a major research interest of his is in methodological research into statistical issues which commonly arise in such studies. He holds a PhD in Epidemiology from Cambridge University and a Masters in Medical Statistics from Southampton University.

    Tobias Bluhmi (University of Ulm)

    Resampling complex time-to-event data without individual patient data, with a view toward recurrent events.


    In this talk we consider non- and semi-parametric resampling of multistate event histories by simulating individual trajectories from an empirical multivariate hazard measure.  

    One advantage is that it does not necessarily require individual patient data, but may be based on published information. This is also attractive for both study planning and simulating realistic real‐world event history data in general.  A special focus is on simulating recurrent events data with associated terminal events. We demonstrate that our proposal gives a more natural interpretation of how such data evolve over the course of timethan many of the competing approaches. The multistate perspective avoids any latent failure time structure and sampling spaces impossible in real life, whereas its parsimony follows the principle of Occam's razor. We also suggest empirical simulation as a novel bootstrap procedure to assess estimation uncertainty in the absence of individual patient data. This is not possible for established procedures such as Efron's bootstrap.


    Tobias Bluhmki studied Mathematical Biometry at Ulm University from 2009 to 2014 and was honored with the "Bernd-Streitberg Award" by the International Biometric Society - German Region for his Master's Thesis. Since then, he has been research assistant at the Institute of Statistics, Ulm University, Germany. He has recently defended his PhD thesis supervised by Jan Beyersmann at the Faculty of Mathematics and Economics and is now postdoctoral researcher. His research focuses on statistical methodology in clinical trials and epidemiological studies based on survival and event history techniques.

    He has published several articles in biostatistical, epidemiological and medical journals and is the current co-lead of the "Team of Young Statisticians" of the International Biometric Society - German Region. 

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

    Please click here to register

  • A PSI Training Course on ICH for Statisticians

    Dates: 19 – 20 Nov, 2019

    Venue: Crowne Plaza Hotel, Stockley Road, West Drayton, London UB7 9NA
    Presenters: Christine Fletcher (Amgen), Kerry Gordon (Quintiles), James Matcham (AZ) and Caroline Pothet (GSK).
    Course timings: 10:00-17:30 (registration from 09:30) on day 1
                              09:00-16:00 on day 2

    The course will describe key guidelines from regulatory bodies such as EMA, FDA, PDMA and CDE.  The focus of the course will be on the content of ICH E9 (Statistical Principles for Clinical Trials) and ICH E10 (Choice of Control Group in Clinical Trials), ICH E6 (Good Clinical Practice) and E17 (Multi-Regional Clinical Trials) but other key regulatory guidance documents will also be highlighted. The course will also include workshops, a Q&A session and guidance on how to seek advice from regulators.


      Registration Fee before 18th October 2019  Registration Fee after 18th October 2019 
    PSI member   £595 + VAT  £695 + VAT
     PSI non-Member   £690 + VAT (includes PSImembership) £790 + VAT (includes PSI membership) 

    Please click here to register.

    Please email PSI@mci-group.com to advise of any dietary requirements and accessibility issues ASAP.