• PSI Webinar: Measuring Patient and Physician Benefit–Risk Preferences in Antipsychotic Clinical Trials

    Dates: 26 – 26 Sep, 2017

    Tuesday 26th September 2017
    3:00-4:00pm (UK Time)

    Organised by the Benefit-Risk Special Interest Group

    Recently, we shared an example of adopting benefit risk methodology to schizophrenia studies (click here for more information). Now we are happy to announce that Eva Katz, the epidemiologist behind this nice case study, will give a webinar about it and there will be the possibility to have a discussion around this and how other areas can benefit from it.

    Presenter: Eva Katz
    Eva-Katz-Pic-EFP
    Eva Katz, PhD, MPH, RD is Associate Director of Benefit-Risk and Epidemiology at Janssen Research & Development.  In this role Dr. Katz serves as an internal consultant for benefit‐risk methodology and patient‐focused benefit‐risk assessment, guiding clinical teams across therapeutic areas in medication benefit-risk assessment using both qualitative and quantitative methods.  As part of the efforts to integrate the patient experience in clinical trials, Dr. Katz has helped integrate patient and physician preference surveys within phase 3 clinical trials. Eva also participates in external task forces on benefit‐risk assessment methods and patient focused drug development. Prior to her role in Benefit-Risk, Dr. Katz was part of the patient reported outcomes team at Janssen where she worked cross‐functionally to develop strategy for development, selection and implementation of Patient‐Reported Outcomes (PROs) in phase 2 and phase 3 clinical trials of pharmaceutical products across therapeutic areas. Dr. Katz received her B.S. in Nutritional Science from Rutgers University, her M.P.H. from the University of California, Berkeley and her doctorate in Nutrition Epidemiology from the Gillings School of Global Public Health, University of North Carolina, Chapel Hill.

    Please watch for updates as we are organizing the last pieces. 

    Registration is free. Click here to register!

  • PSI One Day Meeting: Estimands – Examples for Statisticians!

    500 Brook Drive | Dates: 27 – 27 Sep, 2017

    Wednesday 27 September - QuintilesIMS, 500 Brook Drive, Reading, RG2 6UU, UK

    With the recent release of the ICH E9 Addendum, Estimands is moving from a concept to reality with some Regulators already asking “What is your Estimand?”  But where do you start when defining your Estimand?  What is the Estimand of interest and to whom? What inter-current events could determine your Estimand and thus your analysis methodology?  The PSI Scientific Committee have put together this one day meeting to provide Statisticians with real world examples of how Estimands have been defined.  Presenters will give their insight into discussions with colleagues, working groups and regulators and there will be plenty of opportunity to ask your questions on defining an Estimand. 

    Please click here to view the flyer.

    The agenda is outlined below:

    9.30 – 9.55

    Registration

    9.55 – 10.00

    Welcome and Introduction

    10.00 – 10.40

    Estimands and Sensitivity Analyses: What’s in the new ICH E9 Addendum?

     

    Chrissie Fletcher, Amgen

    10.40 – 11.20

    The Estimands Concept – Experiences when Introducing the Concept in a Global Development Organization

     

    Christoph Gerlinger, Bayer

    11.20 – 11.35

    Break

    11.35 – 12.15

    Disentangling Estimands and the Intention-to-Treat Principle

     

    Ann-Kristin Leuchs, BfArM

    12.15 – 12:55

    Use of Clear Estimands – Beyond Hypothesis Testing

     

    Yolanda Barbachano, MHRA

    12.55 – 13.45

    Lunch

    13.45 – 14.25

    A Journey towards Estimand Specification in Pain: Motivation and Challenges

     

    Francesca Callegari, Novartis

    14.25 – 15.05

    Estimands in Chronic symptomatic Diseases

     

    Martin Jenkins, AstraZeneca

    15.05 – 15.25

    Break

    15:25 – 16:05

    Are Estimands Necessary for Time-To-Event Endpoints?

     

    Chris Harbron, Roche

    16:05 – 16:30

    Panel Discussion

     

    All presenters

    16:30 – 16:35

    Closing remarks – Finish



    Estimands and Sensitivity Analyses: What’s in the new ICH E9 Addendum?

    Chrissie Fletcher, Amgen

    The new addendum to ICH E9 on estimands and sensitivity analyses introduces a new framework for clinical trial design, conduct, analysis and interpretation of results. In the new framework the first step is to ensure there is a clearly defined clinical trial objective. The trial objective will lead to defining the estimand, the treatment effect to be estimated, which will influence the choice of trial design. The estimand will lead to defining appropriate statistical analyses to derive estimates of treatment effects, including sensitivity analyses that are aligned to the estimand.

    The new framework in the ICH E9 addendum will enable sponsors to discuss with regulators prior to the clinical trial commencing what estimand is of primary interest. Choices made in the study design and planned statistical analyses describing how intercurrent events, such as non-adherence, use of rescue medication, and deaths occurring in the study, will be handled can impact what treatment effect is actually being estimated in a clinical trial. Therefore alignment in the choice of estimand and planned statistical analyses, including sensitivity analyses, will improve the interpretation and understanding of trial results.

    This presentation will provide an overview of the new addendum including examples illustrating how to use the new framework in designing clinical trials.

     

    The Estimands Concept – Experiences when Introducing the Concept in a Global Development Organization

    Christoph Gerlinger, Bayer

    The forthcoming addendum to the ICH E9 guidance on statistical principles for clinical trials has major implications on almost all aspects of drug development trials. Despite being a multidisciplinary topic it is often perceived as an exclusively statistical topic in adjacent functions like medical, project management, or regulatory affairs.

    A broad working group of statisticians interested in the estimands framework was founded in our company early 2016 to prepare not only the statisticians but also the whole company for the changes in the way we plan, run, and analyze clinical trials in the future.

    This talk will review the actions taken before the release of the ICH draft addendum: Creation of a white paper, summaries of the key publications, and two pilot workshops all aimed mainly at clinicians in drug development. We will also discuss our plans to roll out the estimands concept both within the statistics department and also to the whole company once the draft addendum is published.

     

    Disentangling Estimands and the Intention-to-Treat Principle

    Ann-Kristin Leuchs, BfArM

    The precise definition of the estimand of primary interest (treatment effect to be estimated) with regard to specification of handling intercurrent events such as recue medication or non-adherence is essential when planning and designing randomized controlled trials (RCT). The analysis should then be aligned to the agreed primary estimand. In this context the question arises how this fits in with the intention-to-treat (ITT) principle. Although the ITT principle has long-since been the gold standard of analyzing RCTs. Despite this there was and still is much ambiguity involved around what is considered to constitute ITT, especially in relation to the problem of missing data and, in recent years, also with regard to intercurrent events and estimands. While some argue it is simply analyzing all patients as randomized, others regard ITT as addressing the treatment policy estimand.

    This talk focuses on the author’s thinking on the ITT principle and its definition, on how it is distinct from the missing data and estimand problem and on how to best move forward. Since ICH E9 is imprecise concerning the ITT principle, discussing an addendum to ICH E9 might be an ideal time point to solve the confusion and ambiguity around defining it. The ITT principle could well remain gold standard of analyzing RCTs even while allowing various different estimands to conform to it.

     

    Use of Clear Estimands – Beyond Hypothesis Testing  

    Yolanda Barbachano, MHRA

    Though the Addendum to ICH E9 was clearly motivated by a need to more precisely define the measure of treatment effect in clinical trials, in the context of efficacy, the same framework is also applicable and helpful when thinking about how to collect and present the safety data. Furthermore, we should ideally think about the estimand of interest for each endpoint or trial objective separately, regardless of whether they are primary or secondary, hypothesis testing or descriptive.  In this talk I will move away from the usual discussion around the choice of estimand for the primary efficacy endpoint, and instead present some examples on safety, tolerability and quality of life, to illustrate the value of the estimand framework in a wider context.  

     

    A Journey towards Estimand Specification in Pain: Motivation and Challenges 

    Francesca Callegari, Novartis

    An estimand clearly defines the treatment effect to be estimated in a clinical trial. An ICH E9 addendum is under preparation, which will introduce the concept of estimand and will provide a structured framework to link trial objectives of a clinical trial and statistical methods in a coherent way.

    In the meantime, regulators are keen to know the definition of estimands for new clinical trials. In this presentation, we focus on a Phase 2 study in chronic pain. The definition of the primary estimand in this context takes into account relevant post-randomization events, which are often informative of the treatment effect of interest, such as intake of concomitant medications and premature discontinuations of study treatment. Other supplementary and secondary estimands are also defined to assess the treatment effect under different handling of the post-randomization events or under different specifications of the variable of interest. Some practical considerations coming from the development of the estimand concept for this trial from its inception till its detailed specification are summarized, outlining the challenges encountered and how these have been overcome.

     

    Estimands in Chronic symptomatic Diseases

    Martin Jenkins, AstraZeneca

    In many chronic, systemic diseases the goal of treatment is to manage patient symptoms and to prevent disease flares. The effect of current therapies is generally reversible and as such it is not necessarily of primary interest to address a treatment policy estimand, but rather to consider the effects attributable to the initially randomised treatment. In addition, defined treatment pathways mean that it is common that estimands in this area must consider the handling of patients who use rescue treatments or escalate therapy. Drawing on examples in rheumatology, dermatology, autoimmune and respiratory disease areas I will compare different scenarios to describe how the precise choice of estimand should take into account the type of endpoint, current treatment paradigm and any retention of treatment effect upon discontinuation.

     

    Are Estimands Necessary for Time-to-Event Endpoints?

    Chris Harbron, Roche

    Observed time-to-event endpoints typically contain many censored observations. As a consequence, many of the standard analysis approaches e.g. Kaplan-Meier and Proportional Hazards are specifically designed to address these partially missing data. This ability to cope with data being missing due to censoring has frequently led to the benefits of estimands for addressing other types of intercurrent events being overlooked.

    In this presentation I will discuss how the estimand framework provides a vehicle for explicitly describing and addressing several of the challenges within time-to-event analyses such as treatment cross-over, informative censoring, lack of blinding and inconsistent definition of endpoints.

     

    Registration

     Registration 
     PSI Member  £160 (plus VAT) 
     Non-Member  £220 (plus VAT) 
     Academic  £90 (plus VAT)

    Please click here to register!

  • EFSPI/PSI Webinar: Spotlight on the Integrated Data Analysis SIG

    Dates: 28 – 28 Sep, 2017

    EFSPI together with PSI will organise a webinar on September 28th on:

    Spotlight on the Integrated Data Analysis SIG

    Date: Thursday 28th September 2017

    Time: 2-3.30pm UK / 3pm CEST / 9am EST

    To download the event flyer please Click Here
  • Introduction To Industry Training Course 2017

    Dates: 01 Oct, 2017 – 31 Jul, 2018

    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!

     PLEASE CLICK HERE TO VIEW THE FLYER

    NEXT COURSE STARTS OCTOBER 2017: £1050+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.

    Limited places available!

    Application forms must be received by 30th June 2017!

    Please discuss your application with your manager
    Final dates to be confirmed.

    CLICK HERE TO DOWNLOAD THE APPLICATION FORM 

    For further information contact:

    Alex Godwood

    MedImmune Ltd, Milstein Building, Granta Park

    Great Abington, Cambridge, CB21 6GH

    Tel: 0203 7496241

    Email: godwooda@MedImmune.com

  • PSI Webinar: Patient Preferences – a webinar with Kevin Marsh presented by the Benefit-Risk SIG

    Dates: 24 – 24 Oct, 2017
    This webinar is free to attend. Please click here to register.

    For more information please click here.
  • Statistics Fundamentals for Clinical Trials for Non-Statisticians (or ‘How to speak stats in a day!’)

    Dates: 14 – 14 Nov, 2017

    Presented by
    Gemma Hodgson
    (Qi Statistics Ltd)


    This basic but wide-ranging course covers techniques for investigating, visualising and performing basic statistical techniques on data sets typical to industry settings. There are many basic concepts that need to be understood before statistics can be used to its full potential to give useful and informative answers. This course ensures that these concepts are understood in a non-technical way and then demonstrated using data examples. 

    Mathematical details are kept to a necessary minimum and we focus on the interpretation of statistical output and illustrate applications with data from dummy clinical trials or published data. The objective of the course is not to teach you how to become a statistician, but to help you work with statisticians and get the maximum value from statistical output. 

    The course will consist of lectures, practical examples and discussions. There will not be any computer exercises. 

    Target Audience: 

    This is a 1-day course, aimed to introduce statistics to people who work on Clinical Trials, but who are not Statisticians. No previous knowledge of Statistics is assumed as we start right at the beginning with the basics. Many practical examples are given and the emphasis is on application and understanding rather than the equations and the technical background. 

    The basics of statistics are discussed to give background and a common base to start from and the applications and use of statistics in drug development is then discussed. The role of the statistician and their ability to help with decision making is also discussed. 

    It also serves as a useful refresher course to those who once studied statistics as part of a college course. 

    The following key topics will be addressed: 

    1. Types of Data 

    2. Measures of location and variability 

    3. Basic Inference 

    4. Power calculations and Sample Sizing 

    5. Design Issues 

    For more information on specific topics, please contact the presenter direct on gemma@qistatistics.co.uk

    Please click here to view the flyer.

    About the presenter: 

    Gemma Hodgson, Qi Statistics Ltd. http://www.qistatistics.co.uk 

    Gemma Hodgson has worked in the Pharmaceutical industry for 20 years. After receiving her first degree from Imperial College (Maths with Statistics) and then an MSc in Medical Statistics from London School of Tropical Hygiene and Medicine, Gemma began her career at Pfizer in Sandwich working in experienced global teams on major phase 3 projects. After 13 years at Pfizer and working in all phases of development, from phase 1 to phase 4, Gemma then moved to Takeda R &D in London where she worked on later phase projects, focussing on close liaison with other departments within the organisation. In 2012 Gemma left Takeda to work for a statistical training and consultancy firm, Qi Statistics Ltd, where training of non-statisticians and explaining statistical concepts to non-scientific audiences is key. Gemma has a broad interest in the application of statistics and is an experienced trainer to all types of audience, specialising in translating technical concepts into everyday English.

     

    Course runs from:            09:45 – 17:00 (registration from 9:15)

    Registration

    Please register online at www.psiweb.org and click on Events; payment now available online.

    Registration costs (includes lunch and refreshments)

    Registration before 13th October 2017

    £425 plus vat

    Registration on or after 13th October 2017

    £495 plus vat

     Please click here to register

    PSI aims to be fully inclusive and endeavours to accommodate delegates with disabilities wherever possible.  Please help us to help you by letting us know if you require additional facilities or have any special requirements.  Please contact us on +44 (0)1730 715 235 or at PSI@mci-group.com for further information.


  • PSI One Day Meeting: Extrapolation

    Stevenage | Dates: 22 Nov, 2017
    Use of extrapolation techniques is playing an increasingly important role in the development of new medicines particularly with regard to special populations such as paediatrics and rare diseases. This meeting will include speakers from industry, academia and regulatory (including Rob Hemmings from MHRA).

    Speakers include:
    • Peter Milligan - Pfizer
    • Susan Cole - MHRA
    • Rob Hemmings - MHRA
    • Nicky Best - GSK
    • Dawn Edwards - GSK
    • Adrian Mander - MRC Biostatistics Unit, University of Cambridge
    • Franz Koenig - Medical University of Vienna
    • Ian Wadsworth - Lancaster University
    Please click here to view the flyer.

    Abstracts

    Rob Hemmings, MHRA

    Extrapolation; regulatory need, examples and emerging guidance.

    Abstract: Extrapolation is defined as ‘extending information and conclusions available from studies in one or more subgroups of the patient population (source population(s)), or in related conditions or with related medicinal products, to make inferences for another subgroup of the population (target population), or condition or product, thus reducing the amount of, or general need for, additional information (types of studies, design modifications, number of patients required) needed to reach conclusions for the target population, or condition or medicinal product’.  The talk will illustrate the potential need for, and benefits of, this concept in regulatory work with a primary focus on extrapolation from adults to children.  An overview of the EMA Reflection Paper on this topic will be presented and discussed, highlighting areas for further discussion and research.

    Ian Wadsworth, Lisa V. Hampson, Thomas Jaki and Graeme J. Sills

    Using historical data to inform extrapolation decisions in children

    When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. However, significant assumptions about the similarity of adults and children are needed for extrapolations to be biologically plausible. One such assumption is that pharmacokinetic-pharmacodynamic (PK-PD) relationships are similar in these different groups. In this presentation, we consider how ‘source’ data available from historical trials completed in adults and adolescents treated with a test drug, can be used to quantify prior uncertainty about whether PK-PD relationships are similar in adults and younger children. A Bayesian multivariate meta-analytic model is used to synthesise the PK-PD data available from the historical trials which recruited adults and adolescents. The model adjusts for the biases that may arise since these existing data are not perfectly relevant to the comparison of interest, and we propose a strategy for eliciting expert prior opinion on the size of these external biases. From the fitted bias-adjusted meta-analytic model we derive prior distributions which quantify our uncertainty about the similarity of PK-PD relationships in adults and younger children. These prior distributions can then be used to calculate the probability of similar PK-PD relationships in adults and younger children which, in turn, may be used to inform decisions as to whether complete extrapolation of efficacy data from adults to children is currently justified, or whether additional data in children are needed to reduce uncertainty. Properties of the proposed methods are assessed using simulation, and their application to epilepsy drug development is considered.

    Clara Domínguez-Islas1, Adrian Mander1, Rebecca Turner2, Nicky Best3

    A Bayesian framework for extrapolation using mixture priors 

    1 MRC Biostatistics Unit, University of Cambridge, UK.

    2 MRC Clinical Trials Unit, University College London, UK.

    3 GlaxoSmithKline, UK.

    As defined by the European Medicines Agency (EMA), extrapolation refers to the extension of information and conclusions available from studies in a source population to make inferences in a target population, in order to reduce the amount of additional information needed to reach conclusions for the latter.  Bayesian inference seems to provide a natural framework to implement the extrapolation principle, as the information from the source population can be used as the prior beliefs for the target population. However, intrinsic to extrapolation principle, there is also the belief that the source and target populations, although similar enough to allow one of them to inform the other, are not exactly the same and important differences, not known a priori, might exist. Therefore, along with informative priors, we also need to incorporate a certain degree of scepticism. This could be achieved by the use of mixture priors.  Although mixture priors have been already proposed in different extrapolation contexts (bridging studies, historical controls, paediatric extrapolation), we identify some gaps in the research conducted and reported so far. In this presentation, we intend to further explore and better understand the potential of mixture priors to provide a quantitative framework for extrapolation. First we present the mixture prior model with special emphasis on the interpretation and type of inference that it allows, providing a connection with Bayesian model averaging. We then address some of the challenges that arise when constructing a mixture prior, including the choices to be made for each of the components of the model, as well as technical aspects of the estimation and computation. Finally, we discuss the frequentist operating characteristics of this approach and identify the trade-offs that come with the flexibility and robustness of the mixture priors. 

    Lisa V Hampson, Franz Koenig

    Use of frequentist and Bayesian approaches for extrapolating from adult efficacy data to design and interpret confirmatory trials in children

    New medicines for children should be subject to rigorous examination whilst taking steps to avoid unnecessary experimentation. Extrapolating from adult data can reduce uncertainty about a drug’s effects in younger patients meaning smaller trials may suffice.

    We consider how to design a confirmatory trial in children intended to compare the efficacy of a new drug, E, against control. Assuming that conduct of this trial is conditional on having demonstrated a significant beneficial effect in adults, we adopt a Bayesian approach to incorporate these adult data into the design and analysis of the paediatric trial. At each stage, inferences are made using all available data to update a Bayesian mixture model for prior opinion on the degree of similarities between adults and children. Using this framework, we propose designs for the paediatric trial which are specified by calibrating the sample size and final decision rule to: a) achieve a high frequentist power and high minimum (or average) Bayesian positive predictive value of a significant result in children; or b) ensure that a final decision to adopt (abandon) drug E in children is always associated with a minimum positive (negative) predictive value. Operating characteristics of our Bayesian designs are evaluated and compared with those of a recently proposed hybrid approach (Hlavin et al. Statistics in Medicine 2016; 35: 2117) where the sample size and significance level of a frequentist confirmatory trial in children are set to achieve a high frequentist power and high average positive predictive value of a significant result in children.

    Nicky Best, Dawn Edwards

    A case study using Bayesian methods to leverage existing clinical efficacy data in paediatric trials

    Recently there has been increased regulatory interest in partial extrapolation of adult efficacy information to paediatrics populations to reduce data collection requirements in children.  In this talk we will present a case study describing plans to use partial extrapolation of adult efficacy data from a phase III trial of an experimental drug in adolescents with a respiratory disease.  We will demonstrate how adult data on the treatment difference for the endpoint of interest can be included via an informative prior distribution to increase the probability of success of the study in adolescents and the precision of the estimated treatment difference. A method which incorporates dynamic borrowing will be used to define the level of extrapolation using a 2-step approach whereby information from the adult data is first incorporated into a prior distribution before being integrated with the data from the adolescent population. We propose a 3-component weighted robust mixture prior with the informative components based on (1) the adult efficacy data, (2) rescaled adult efficacy data to reflect the expected response for the adolescent population, and (3) a flat component to ensure that, in the event the adolescent and adult data are in clear conflict, the latter will have minimal influence on the posterior distribution of the treatment difference, thus also preventing excessive inflation of type 1 error. We will present results of a simulation study investigating operating characteristics for different choices of success criteria and prior weights.

    Registration

    Registration on or before 22nd October
    PSI Member  £120 (plus VAT)
    Non-Member  £160 (plus VAT)
    Academic   £60 (plus VAT) 
    Registration after 22nd October
    PSI Member  £160 (plus VAT)
    Non-Member  £220 (plus VAT)
    Academic  £90 (plus VAT)


    Please click here to register.
  • European Statistical Meeting: Latest Trends in Health Technology Assessments

    Two Pancras Square | Dates: 28 Nov, 2017

    This 1-day scientific meeting will provide an update on latest trends in HTA, including: the Real-World Evidence Navigator tool created by the IMI GetReal project; the EUnetHTA Joint Action 3 initiative and methodology being researched; introduction to value-based frameworks and estimands in HTA.  Patient perspectives in HTA will be discussed including how to involve patients in HTA and latest methods in patient reported outcomes.  HTA related methodological considerations will be highlighted including approaches to handle treatment switching in HTA.  Industry HTA case studies will also be presented. 

    Speakers include well known representatives from academia, European regulatory bodies and industry. The day will end with a panel discussion.

    To download the flyer please click here.

    Outline of the Agenda

     09:30 Welcome
    Chrissie Fletcher (Amgen, Chair HTA SIG), William Malbecq (MSD)
     
     09:50 Session: Trends in HTA(1)

    Introducing the RWE Navigator - Heather Stegenga (NICE)

    EUnetHTA Joint Action 3 activities - to be confirmed

     11:10 Coffee Break
     11:30 Session: HTA research and methods

    Adjusting for treatment switching in randomised controlled trials - Nick Latimer (University of Sheffield)

    Accumulated Industry experience in bridging regulatory and HTA research methodologies - William Malbecq, Kristel Vandormael (MSD)
     12:50  Lunch
     13:30 Session: Patient perspectives in HTA

    Involving the patient in HTA - Karen Facey (Evidence Based Health Policy Consultant)

    Benefit-Risk assessments in HTA - Shahrul Mt-Isa (MSD), Susan Talbot (Amgen)
     14:50 Coffee Break
     15:10 Session: Trends in HTA(2)

    Value-based frameworks - Jan McKendrick (PRMA Consulting)

    Estimands in HTA - Jason Wang (Celgene), Chrissie Fletcher (Amgen)

     16:30 Panel Discussion
     17:00 Summary and meeting close

    Registration

    Fee includes lunch & refreshments.

    Registration on or before 14th October
    PSI Member  £100 (plus VAT)
    Non-Member  £140 (plus VAT)
    Academic  £70 (plus VAT)
    Registration after 14th October
    PSI Member  £120 (plus VAT)
    Non-Member  £160 (plus VAT)
    Academic  £90 (plus VAT)

    Please click here to register.
  • A PSI Training Course on Missing Data

    Heathrow | Dates: 06 – 07 Mar, 2018
    The aim of this course is to provide participants with an understanding of missing data, its link with what is to be estimated in a study (the “estimand”), and statistical modelling approaches. The 2 day course includes workshops: participants will undertake a number of practical exercises on missing data in SAS. The course will provide participants the opportunity to gain insight into some of the more useful new methodologies for missing data, with a view to being at the service of the real scientific question of interest. Multiple imputation (MI) will be emphasised – due to this method’s flexibility.

    Attendees will require a laptop with access to SAS.

    The following topics will be covered:
     
    - History of research into missing data
    - Prevention of missing data and impact on study power
    - Missing Data and its relation to the estimand
    - Estimands and their models
    - Multiple imputation I: models for missing data
    - Weighting I: weighting for missing data
    - Multiple imputation II: methods for non-continuous endpoints
    - Weighting II: augmenting weighed data with model estimates
    - Composite endpoints
    - Case studies

    Course runs from:
    10:00 - 17:00 (registration from 09:00) on Day 1
    09:00 - 16:00 on Day 2

    ​​Registration

    Registration costs include lunch and refreshments. PSI are holding a limited number of hotel rooms until the 31st January 2018 which will be allocated on a first come first served basis.

    Registration BEFORE 31st January 2018 
     PSI Member  £495 plus VAT
     Non-Member  £590 plus VAT
     Registration AFTER 31st January 2018
     PSI Member  £595 plus VAT
     Non-Member  £690 plus VAT

    Please click here to register.
        
    Please click here to view the flyer.