9.30 -9.55

Registration

9.55 – 10.00

Welcome and Introduction

10.00 – 11.00

One label with different interpretations…Statistical lead, what does it really mean?

Charlotte Eden and Lucie Tesarova, QuintilesIMS

11.00 – 11.20

Break

11.20 – 11.50

Exploring recent developments in the MAMS methodology for designing an adaptive trial

Julia Abery, University of Reading

11.50 – 12.20

Seamless Study Designs & Real-time Data Capture Using Electronic Devices

Rhian Jacob, Roche

12.20 – 13.20

Lunch

13.20 – 14:20

Training session: Explaining statistics to non-statisticians

James Matcham, AstraZeneca / PSI Training Committee

14.20 – 14.40

Break

14.40 – 15.10

Introduction to Pharmacokinetics

Laura Cope, Quanticate

15.10 – 15.40

AdePro – A new Perspective on Safety Profiles

Nicole Mentenich, Christoph Tasto and Bastian Becker, Bayer AG

15.40 – 16.00

Break

16.00 – 16.30

Marginal and conditional structural mean models for optimising Dynamic Treatment Regimes

Nirav Ratia, GSK

16.30

Close

Charlotte Eden and Lucie Tesarova, QuintilesIMS

Exploring the role of the Statistical lead from different perspectives. Focusing on life in a CRO to experiences in Pharma with audience participation to discuss how we work on some key factors throughout the life of a study.

Julia Abery, University of Reading

Multi-arm adaptive trials allow several new drugs to be assessed simultaneously, potentially giving improved efficiency over conventionally designed trials. In such a trial, data accumulating during the study are utilised to inform decisions about how the remainder of the trial should be conducted. Several different methodological approaches have been developed for multi-arm adaptive trials and a number of studies have compared the performance of different subsets of these proposed methods. The comparisons have not, however, considered the so-called MAMS approach, since methodology for the latter did not incorporate strong control of the family-wise error rate (FWER). Recently, the MAMS approach has been extended such that strong control of the FWER can be guaranteed. Furthermore, an automated process has been developed which can produce efficient designs for trials with any number of stages and treatment arms.Since MAMS is relatively easy to understand and implement, we set out to explore these developments and to compare MAMS to other well established methods.We show how MAMS compares favourably with the more established combination method for some scenarios and explore how use of a novel selection rule may offer a further option within MAMS methodology.

Rhian Jacob, Roche

Developing a new drug in a highly competitive environment necessitates a fast to market approach.  This talk looks at the ongoing '944' trial, a multiple cohort seamless phase II/III study. Key study endpoints are captured using electronic devices, providing high volume of data in real-time. This talk describes the opportunities and challenges for a statistician leading a phase II readout within a phase III framework, with discussion on novel data capture methods; is the industry ready to handle device data?

James Matcham, AstraZeneca / PSI Training Committee

Laura Cope, Quanticate

Pharmacokinetics is the study of the effect of the body on the drug. The pharmacokinetic profile maps the concentration of the drug in the body over time tracking how the drug is absorbed, distributed, metabolised and excreted by individuals. Drug concentration is linked to both efficacy and safety and hence pharmacokinetic studies form the basis of much earlier phase clinical trials. Pharmacokinetic profiles are often described using summary measures such as the area under the curve (AUC) and the maximum concentration (Cmax) but how are these parameters derived? What models are used and what assumptions do they make? This presentation will describe the derivation of pharmacokinetic parameters for both intravenous and extravascular dosing, first looking at a single dose and then extending to multiple dosing schedules.

Nicole Mentenich, Christoph Tasto and Bastian Becker, Bayer AG

The database in a clinical trial contains vast information on adverse events, involving hundreds of different adverse event terms with varying severity grades and different start and end dates. Despite this plethora of information, insight into the adverse events in a clinical study is usually limited to simple summary tables of absolute and relative numbers of adverse event occurrences. AdEPro – an innovation of Bayer’s Biostatistics Innovation Center – is an unparalleled approach to audio-visualize the safety profile of both the individual patient and of the entire study cohort, which enables every study team member to experience the study and emphasize with the patients. The AdEPro app depicts the temporal progress of all adverse events in every study subject and enables the user to give profound answers to complex questions surrounding adverse events such as the frequency, duration and correlation of adverse events of interest.

Nirav Ratia, GSK

Marginal structural models (MSMs) are casual models of dynamic treatment regimes (also known as treatment strategies or policies) designed to adjust for time-dependent confounding. These models are designed to adjust for exposures or treatment that vary over time, and standard approaches for adjustment of confounding can be biased when there exist time-dependent confounding. Dynamic treatment regimes (DTRs) provide the basis for statistical analysis in personalised medicine. A DTR is a decision rule that guides the treatment choices over the course of the therapy. The sequence of treatments a patient receives depends on the patient’s health status, response to prior treatment and other patient characteristics.