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Nov 10 2014 9:00AM - Nov 11 2014 4:00PM | Tryp Barcelona Apolo
Avinguda del Parallel, 57-59
Lifecycle management is a widely accepted concept for the development and marketing of medicines. At each step of this process, pre- and post-licensing, statisticians work to develop methods that can improve efficiency and can enhance decision-making through optimal study design, analysis and inference. The discussions at this workshop will consider not only the methods themselves, but also how to best implement them in a regulatory context.
We will focus on:
Professionals with an interest in the application of, and research into, statistics in the drug development process from the pharmaceutical industry, academia, regulatory and governmental agencies, as well as contract research organisations.
DIA has blocked a limited number of rooms at the following hotel:
TRYP Barcelona Apolo HotelAvinguda del Paral·lel, 57-59,08004, Barcelona, Spain Tel: (34) 93 343 30 00Fax: (34) 93 443 0059E-mail: firstname.lastname@example.org at the rate of:EUR 85.00 single use incl. of breakfast, excl. of VAT and city tax
To make your reservation, please click here: http://meetings.melia.com/en/DIAEUROPE.html
Important: The room rate is available until 04 October 2014 or until the group block is sold-out, whichever comes first.In case of no-shows the hotel is authorised to charge the full amount corresponding to the duration of your stay.
Registration QuestionsPhone.: +41 61 225 51 51 Fax: +41 61 225 51 52 Monday-Friday 8:00-17:00 CETdiaeurope@diaeurope.org
Agenda and Event LogisticsMagdalena Lewandowska, Event ManagerPhone: +41 61 225 51 65Fax: +41 61 225 51 email@example.com
Global Head Full Development Biostatistics OncologyNovartis Pharma AG, Switzerland
Expert StatisticianAlmirall, Spain
Scientific Director, Biostatistics and DataClinic Hospital of Barcelona, Spain
Head of Biostatistics and Special PharmacokineticsFederal Institute For Drugs and Medical Devices (BfArM), Germany
Senior Director of BiostatisticsF. Hoffmann-La Roche Ltd., Switzerland
Chair of Department of Medical StatisticsGeorg-August-University Goettingen, Germany
Executive Director BiostatisticsMSD Europe, Inc., Belgium
Professor of BiostatisticsCatholic University of Leuven, Belgium
In follow-up studies different types of outcomes are typically collected for each subject. These may include several longitudinally measured responses (e.g., biomarkers, blood values), and the time until an event of particular interest occurs (e.g., death, dropout from the study). Often these outcomes are separately analysed, but on many occasions it is of scientific interest to study their association. This type of research question has given rise class of joint models for longitudinal and time-to-event data. These models constitute an attractive paradigm for the analysis of follow-up data that is mainly applicable in two settings: First, when focus is on a survival outcome and we wish to account for the effect of endogenous time-dependents covariates measured with error (e.g., biomarkers), and second, when focus is on the longitudinal outcome and we wish to correct for non-random dropout. This tutorial will provide a short introduction into this modelling framework. In particular, we will explain when these models should be used in practice, which are the key assumptions behind them, and how they can be utilised to extract relevant information from the data. Emphasis will be given on applications and on how to define appropriate joint models to answer their questions of interest.
Recently, an ICH concept paper on choosing appropriate estimands and defining sensitivity analyses in confirmatory clinical trials was drafted. In settings where pronounced lack of adherence to the study protocol is expected, the difference between the ideal treatment effect, if the medication is taken as directed, and the treatment effect if the medication is taken as observed is crucial in the assessment of the drug’s benefit. The handling of missing data and the use data observed after treatment discontinuation or change is strongly related to the targeted estimand. An estimand is what is being estimated and precisely defines a treatment effect regarding population, outcome measure, and the parameter defined by the underlying probabilistic model either under the assumption of perfect treatment adherence or incorporating the actual adherence. The session will discuss the use and definition of different estimands and the consequences with respect to study design and analysis in the context of the drug approval process.
A Biosimilar medicine is a growing field. The number of regulatory applications has increased in recent years. This session will highlight the features of the study design options for biosimilar efficacy trials. The choice of margins, their reliability and the different statistical and regulatory considerations associated with them will be some of the topics that will be discussed in this session from both a regulatory and industry point of view.
Precision medicine becomes more and more important in drug development. Among the many topics possible we picked out two, biomarker identification and seamless enrichment designs. There will be a short introduction into the topic, an overview and different methods, then two examples from industry and a regulatory evaluation. Finally the two sessions will be rounded up by a panel discussion.
This session will pick up two relevant topics which are important for regulatory bodies as well as for industry. For each of the topics we will have one presentation from a more regulatory view and a panel discussion. The first topic will deal with issues in developing new therapies in small populations, rare diseases and paediatric indications. The second issue will discuss issues around censoring for time to event analyses and what the right primary analysis should be.