• PSI Scientific Committee Webinar: Statistical Challenges in Analytical Comparability and Biosimilarity Assessment

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

    Speakers: 

    • Thomas Lang (EMA BSWP / AGES) - Setting the Scene: The regulatory landscape – a journey through time
    • Bruno Boulanger (Pharmalex) - Analytical similarity and comparability: what is the question ?
    • Johanna Mielke (Bayer) - The assessment of quality attributes for biosimilars: a statistical perspective on current practice and a proposal

    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.

    Registration: 

    This webinar is free for PSI members to attend but has a charge of £20 for non-members.  To register please click here.

         



    TLang foto





    Thomas Lang
    (EMA BSWP / AGES)
       

    Thomas Lang is biostatistician by training. He spent eight years in academic research, followed by three years in clinical research in the pharmaceutical industry. He currently works as senior statistical assessor for the Austrian Agency for Health and Food Safety. For more than nine years he was a member of the Scientific Advice Working Party at EMA. In his current role of a member of the Biostatistics Working Party Thomas acted as rapporteur for the EMA Reflection paper on statistical methodology for the comparative assessment of quality attributes in drug development.

        
     Bruno_Boulanger_2019





    Bruno Boulanger (Pharmalex)
     

    Bruno 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.

     foto_johanna_mielke
    Johanna Mielke (Bayer)
      

    Johanna studied Statistics at TU Dortmund University (Germany). Afterwards, she joined the Statistical Methodology Group at Novartis in Basel (Switzerland) as a Doctoral Candidate. Her research was focussed on the development of statistical methodologies for clinical biosimilar development. After completing her Ph.D. in 2018, Johanna started to work as a Data Scientist at Bayer in Wuppertal (Germany).


  • The PSI & DIA’s Journal Club: Subgroup Analyses Webinar 24th October 2019, 4pm - 5:00pm (BST)

    Dates: 24 – 24 Oct, 2019

    Please click here to view the flyer.

    REGISTER HERE:  https://attendee.gotowebinar.com/register/1104192650747595019

    Join us in our discussion on Subgroup Analyses at 4pm (BST) on Thursday 24th October.

    Our two presenters are Cynthia Huber from the Department of Medical Statistics, University Medical Centre Gottingen and Christoph Muysers and Bodo Kirsch from Bayer. The chair of the webinar is Anna Berglind from AstraZeneca.

    (PSI)  A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations – Cynthia Huber, Norbert Benda and Tim Friede

    (DIA) A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies – Christoph Muysers et al.

    The webinar is sponsored by Wiley who will make the PSI paper free to access for a few weeks before and after the webinar.

  • 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


    Please click here for a flyer on the event

    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.


    Agenda

    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) on Estimators and Estimands for safety events in time-to-event studies: a regulatory perspective.

    12:00 - 13:00

    Lunch break

    13:00 - 16:30

    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.

    Rob Hemmings (Consilium): Rejoinder



    Abstracts

      

    Valentine Jehl
    (Novartis)

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

    Abstract

    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.

    Biography

    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
    (Roche)


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

    Abstract

    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.

    Biography

    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).

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    Filip De Ridder
    (Janssen)

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

    Abstract

    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. 

    Biography

    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

    Andrew Thomson
    (EMA)

     

    Abstract

    The treatment of recurrent safety events and terminal events requires careful consideration underlying the estimands in question, and the assumptions in the methods used to estimate them. In this talk I shall give a regulatory perspective on these issues, focussing on how and why the EU system summarises data as it does, where the gaps are in the methodology, and how we can progress to ensure that data are summarised appropriately. I will consider whether we need to move beyond the methods currently used, and what questions we truly need to be answering (and how). In particular I shall argue that we need to be sure that when no true raised risk exists, the method we use to summarise said risk should provide an unbiased average effect of 0, but in time-to-event studies this is not always as quite straightforward as it seems.

     

    Biography.

    Andrew Thomson is a statistician at the EMA Office of Biostatistics and Methodology Support, joining in 2014. He supports the methodological aspects of the assessments of Marketing Authorisation Applications, as well as Scientific Advice, and methodological aspects of Paediatric Investigational Plans. He has worked extensively on the methodological aspects of the EMA Reflection Paper on the use of extrapolation of efficacy in paediatric studies.

     

    Prior to the EMA, he worked at the UK regulator, the Medicines and Healthcare product Regulatory Agency. Here he worked initially as a statistical assessor in the Licensing Division, assessing Marketing Application Authorisations and providing Scientific Advice to companies. After rising to Senior Statistical Assessor, he moved to the Vigilance and Risk Management of Medicines Division, to be Head of Epidemiology. Here he managed a team of statisticians, epidemiologists and data analysts providing support to the assessment of post-licensing observational studies and meta-analyses. He also managed the team’s design, conduct and analysis of epidemiology studies, using the UK Clinical Practice Research

     

    Arno Fritsch & Patrick Schlömer (Bayer)

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

    Abstract

    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.

    Biography

    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 2
    John Gregson
    (London School of Hygiene & Tropical Medicine)


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

    Abstract

    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.

    Biography

    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 Bluhmki (University of Ulm)

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

    Abstract

    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.

    Biography

    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.

    Headshot
    Rob Hemmings (Consilium)
    Biography

    I am a partner at Consilium.  Consilium is my consultancy partnership with Tomas Salmonson, a long-standing member of the EMA’s CHMP and formerly the chair of that committee.  Tomas and I support companies in the development, authorisation and life-cycle management of medicines.

    Previously I worked at AstraZeneca and for 19 years at the Medicines and Healthcare products Regulatory Agency, heading the group of medical statisticians and pharmacokineticists.  I am a statistician by background and whilst working at MHRA I was co-opted as a member of EMA’s CHMP for expertise in medical statistics and epidemiology.  At CHMP I was Rapporteur for multiple products and was widely engaged across both scientific and policy aspects of the committee’s work.  I was fortunate to chair the CHMP’s Scientific Advice Working Party for 8 years and have also chaired their expert groups on Biostatistics, Modelling and Simulation and Extrapolation.  I wrote or co-wrote multiple regulatory guidance documents, including those related to estimands, subgroups, use of conditional marketing authorisation, development of fixed-dose combinations, extrapolation and adaptive designs.  I have a particular interest in when and how to use data generated in clinical practice to support drug development.




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

    Please click here to register

     
  • Maths Meets Medicine 2019

    Dates: 05 – 05 Nov, 2019
    How to find us: University of Reading

    To view the poster for this event, please click here.

    The Careers and Academic Liaison Committee (CALC) will be hosting their fourth 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. 

    If your school is interested in sending students to this event, please contact us at Careers@psiweb.org
  • PSI Scientific Committee Webinar - Longitudinal modelling: Time to take the next step?

    Dates: 18 – 18 Nov, 2019

    Date: Monday 18th November 2019
    Time: 14:00 - 16:00 UK Time
    Speakers: 
     

    • José Pinhero
    • Björn Bornkamp
    • Tobias Mielke 
    • France Mentré
    • Rob Hemmings

    Registration: This webinar is free for PSI members, but has a charge of £20+VAT for non-members. To register, please click here.  
    Please email PSI@mci-group.com if you have any questions.
    ___________________

    Longitudinal data, i.e. data that arises from repeated observations of the variable over a period of time, has long been put forward as one way to improve to the efficiency of drug development. However, even though there is a rich statistical methodology for longitudinal data, there is no full, wholehearted uptake of these methods in pharmaceutical statistics. The purpose of this webinar is to explore the use of longitudinal modelling across drug development, highlighting its opportunities (such as usage as primary analyses, or for improved decision making at interim analyses) and caveats. One important aspect to be discussed is the evaluation of the efficiency of (parametric) longitudinal modelling versus standard cross-sectional approaches, and factors based upon which one approach might be preferable over the other.


    Speakers

    Jose Pinheiro 180x25
    José Pinheiro
    (Janssen Research & Development)


    Abstract: 
    Increasing the efficiency of drug development is imperative to make novel treatments available to patients sooner and at lower costs, ensuring the long-term sustainability of the biopharmaceutical industry. Among the various proposals that have been put forward to improve drug development efficiency, leveraging longitudinal data via (parametric) modeling stands out for its low additional cost and ease of implementation: oftentimes only involving different analysis methods for data already collected in clinical studies. While numerous statistical approaches have been developed over the past several decades for modelling longitudinal data (e.g. nonlinear and generalized linear mixed effects) the uptake of these methods in pharmaceutical statistics as part of mainstream (pre-specified) primary analyses in clinical trials remains quite limited, with main exception being MMRM.

    Partially this may be due to concerns about making assumptions on the shape of longitudinal profiles, leading to the focus on cross-sectional (or landmark) analyses, which do not require such assumptions, but often only utilize a fraction of the information available per patient. By utilizing the full information collected over time, longitudinal modelling, especially of the parametric type, has the potential to lead to substantially more efficient drug development, even when the primary endpoint is cross-sectional in nature (e.g., change from baseline at Week 26). This potential advantage needs to be balanced against standard concerns about model mis-specification and regulatory acceptance.

    Bio: José Pinheiro has a Ph.D. in Statistics from the University of Wisconsin – Madison, having worked at Bell Labs and Novartis Pharmaceuticals, before his current position as Global Head of Statistical Modeling & Methodology in the Statistics and Decision Sciences department at Janssen Research & Development. He has been involved in methodological development in various areas of statistics and drug development, including dose-finding, adaptive designs, and mixed-effects models. He is a Fellow of the American Statistical Association, a past-editor of Statistics in Biopharmaceutical Research, and past-president of ENAR.







    Bjorn Bornkamp 180x250
    Bj
    örn Bornkamp
    (Novartis)

    Title: When is a longitudinal test better than a cross-sectional one for detecting a treatment effect? 
    (Joint work with Ines Paule)

    Abstract: In this work, we explore the potential benefits of utilizing the full longitudinal profile for testing for a treatment effect and compare this to a cross-sectional test at the last time point.

    While there are a number of papers showing huge gains for longitudinal testing, current practice in clinical development is to focus on a comparison at the last visit for the purpose of trial design and the primary statistical analysis. In this presentation we try to characterize the factors and endpoint properties that determine if and by how much a longitudinal test will be more powerful than a cross-sectional test.

    We consider the setting of a continuous endpoint measured repeatedly over time and utilize a test that uses a weighted average of the treatment differences at the specific time points, which is straightforward to implement with standard mixed effects software. We consider how to weight time-points optimally and which factors play a role in determining the weights. We then assess the potential gain of the longitudinal approach in a set of real case examples.

    Finally, a simulation study is performed that compares this simple longitudinal approach to more strongly parameterized traditional longitudinal mixed effects model.

    Bio: Björn works in the Novartis Statistcal Methodology and Consulting group. He consults for example on dose-finding studies, causal inference and estimands, subgroup analysis, Bayesian statistics and statistical modelling. In 2013 he received the RSS/PSI award for developing innovative statistical dose-finding methodology, in particular the development of the software package DoseFinding in R.

    Tobias Mielke 180x250
    Tobias Mielke
    (Janssen Research & Development)

    Title: Decision-making using longitudinal modelling in presence of model uncertainty

    Abstract: As statisticians, we design studies to answer some research questions and we would like to get answers to these research questions as quickly as possible, using the least amount of resources while still providing enough information to allow for some “significant” conclusions with high probability. It is common that our study designs require multiple assessments of the same variable within the same subjects over some period of time. However, while these correlated longitudinal measurements are collected, our analysis approaches might not take full use of the information contained in the data, leading to inefficiency in some of our analyses and potentially to wrong decisions. Many statistical techniques are available to leverage the information contained in longitudinal data, like summary measures (e.g. AUCs) or the MMRM approach. Knowing the true underlying longitudinal profile and distribution, parametric longitudinal modelling provides an efficient analysis technique. Unfortunately, the true underlying longitudinal profile is known only in rare situations (e.g. mechanistic models in PK/PD). As a result, high uncertainty in longitudinal profiles comes with high concerns in applying parametric longitudinal modelling for decision-making: Using the wrong model for the analysis, the results will be biased such that error probabilities will be inflated. While model uncertainty is a valid concern, mitigation strategies should be evaluated in the design phase to support the selection of an efficient and robust analysis technique.

    Concerns on model uncertainty have been widely discussed for dose-finding studies in the recent scientific literature. The methodology can be generalized to longitudinal modelling, covering model selection approaches, model-based contrast tests and/or (Bayesian) model averaging approaches. The effects of model uncertainty on decision-making will be discussed in this presentation. Different parametric and semi-parametric longitudinal modelling approaches will be evaluated for this purpose. The presented approaches will be compared in their ability of mitigating concerns on the true underlying longitudinal model, while increasing efficiency in decision-making.

    Bio: Tobias works as Scientific Director in Janssen’s internal statistical consulting group. His primary consultancy responsibilities are on adaptive study designs, the handling of multiplicity and statistical modelling in general. Tobias joined Janssen in 2018 from ICON Clinical Research, where he implemented adaptive dose ranging designs, including MCP-Mod, into ADDPLAN DF. In his consultancy roles at ICON and Janssen, he supported many innovative study designs projects, including: inferentially seamless Phase 2/3 designs, adaptive Phase 2 Dose-Finding designs with MCPMod, Phase 1/2 PoC Dose-Finding designs using Bayesian Go/No-Go criteria and designs with adaptive endpoint selection. Tobias holds a PhD degree from Otto-von-Guericke-Universität Magdeburg in Germany. His doctoral dissertation was on the topic of optimum experimental design for nonlinear mixed effects models.

     

    Discussants

    Frances Mentré 180x250
    France Mentré
    (School of Medicine of University of Paris)

    Bio: France Mentré is Professor of Biostatistics in the School of Medicine of University of Paris. She heads an INSERM research team on Biostatistical Modelling and Pharmacometrics in treatment of Infectious Diseases. She has worked on development and application of methods for nonlinear mixed-effects models and pharmacometrics for more than 30 years. She applies these models to understand the variability in the response to anti-infective agents. She is leading the development of the software PFIM for optimal design in pharmacometrics. She has published more than 250 articles in biostatistics, pharmacometrics, clinical pharmacology or medical research.

    She received in 2013 the USCF/ISoP Lewis B. Sheiner Lecturer Award and in 2018 the ASCPT Sheiner-Beal Pharmacometrics award. She is the co-chair and one of the founder of the Special Interest Group on Statistics and Pharmacometrics of ASA and ISOP. She is editor in chief since October 2018 of CPT: Pharmacometrics and System Pharmacology.

    Rob Hemmings 180x250
    Rob Hemmings
    (Consilium)

    Bio: Rob Hemmings is a partner at Consilium. Consilium is his consultancy partnership with Tomas Salmonson, a long-standing member of the EMA’s CHMP and formerly the chair of that committee. Tomas and Rob support companies in the development, authorisation and life-cycle management of medicines.

    Previously Rob worked at AstraZeneca and for 19 years at the Medicines and Healthcare products Regulatory Agency, heading the group of medical statisticians and pharmacokineticists. He is a statistician by background and whilst working at MHRA he was co-opted as a member of EMA’s CHMP for expertise in medical statistics and epidemiology. At CHMP he was Rapporteur for multiple products and was widely engaged across both scientific and policy aspects of the committee’s work. He was fortunate to chair the CHMP’s Scientific Advice Working Party for 8 years and also chaired their expert groups on Biostatistics, Modelling and Simulation and Extrapolation. He wrote or co-wrote multiple regulatory guidance documents, including those related to estimands, subgroups, use of conditional marketing authorisation, development of fixed-dose combinations, extrapolation and adaptive designs. He has a particular interest in when and how to use data generated in clinical practice to support drug development.




  • 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 (IQVIA), 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.

    Please click here for the course flyer.

    Registration:

      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. 

  • PSI Webinar - Overview and awareness about quantitative decision-making in drug development

    Dates: 03 – 03 Dec, 2019

    Date: 3 December 2019 
    Time: 14:00 - 15:30
    Speakers: PSI/EFSPI SIG Quantitative Decision-Making members 
    speaker details to be confirmed in due course.

    We performed a survey among pharmaceutical companies, targeting people with different profiles (statisticians, non-statisticians and decision-makers) working at different stages of the clinical development (study level, development level or portfolio level). This survey allowed us to analyze which quantitative methods are known, which quantitative methods are used (or not), what benefit is expected from this kind of methods, and what are the needs for a larger use of quantitative methods to support decision-making. It permitted to understand the gaps and some of the issues associated to the use of such methods in drug development. The webinars are intended to share the learnings from the survey and to promote different quantitative methods for decision-making.

    Registration: to register for this free event please click here. 

    Please email PSI@mci-group.com if you have any questions.
  • PSI Webinar - Main statistical approaches for quantitative decision-making in drug development

    Dates: 10 – 10 Dec, 2019

    Date: 10th December 2019
    Time: 14:00 - 15:30
    Speakers: PSI/EFSPI SIG Quantitative Decision-Making members -
    speaker details to be confirmed in due course.

    Quantitative methods to support decision-making in clinical drug development already exist, but may be unknown or unused by pharmaceutical companies. We performed a survey among pharmaceutical companies, targeting people with different profiles (statisticians, non-statisticians and decision-makers) working at different stages of the clinical development (study level, development level or portfolio level). This survey allowed us to analyze which quantitative methods are known, which quantitative methods are used (or not), what benefit is expected from this kind of methods, and what are the needs for a larger use of quantitative methods to support decision-making. It permitted to understand the gaps and some of the issues associated to the use of such methods in drug development. The webinars are intended to share the learnings from the survey and to promote different quantitative methods for decision-making.

    Registration: To register for this free webinar, please click here.

    Please email PSI@mci-group.com if you have any questions.

  • Medical Statistics Careers Event 2020

    Dates: 04 – 05 Mar, 2020

    Our annual PSI Medical Statistics Careers Event, will be held at the De Montfort University in Leicester on Wednesday 4th March 2020. 

    the half-day event will include a series of talks, panel discussion and exhabition stands. For further details or to register online, click here.  

    We look forward to seeing you there!

    PSI Careers and Academic Liaison Committee (CALC)

  • Introduction To Industry Training Course 2020

    Dates: 01 Oct, 2020

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

    THE INTRODUCTION TO INDUSTRY TRAINING COURSE NEEDS YOU!


    NEXT COURSE STARTS OCTOBER 2020,
    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, health technology assessment and marketing.

     PLEASE CLICK HERE TO VIEW THE FLYER

    CLICK HERE TO DOWNLOAD THE APPLICATION FORM

    For further information contact:

    Alex Godwood & Zelie Bailes

    Sosei Heptares 
    Steinmetz Building
    Granta Park
    Cambridge 
    CB21 6DG
    United Kingdom

     

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