Population Optimum Design of Experiments

PODE 2017

Novartis Pharma AG, Basel, Switzerland

8 September 2017


9.00 - 9.30

Welcome tea/coffee

9.30 - 10.30

Zinnia P Parra-Guillen, Inaki F Troconiz, University of Navarra, Pamplona.

Getting started with Optimal Design for NLME models. (Abstract, Slides)

Maryna Prus, Otto-von-Guericke-Universitat Magdeburg.

Optimal Designs for the Prediction of Individual Parameters in Multiple Group Random Coefficient Regression Models. (Abstract, Slides)

10.30 - 11.00







11.00 - 12.30

Jose Pinheiro, Janssen R&D.

Exposure-response modeling for dose selection under model uncertainty: Extending the MCP-Mod approach. (Abstract, Slides)

Yevgen Ryeznik, Oleksandr Sverdlov, Andrew C. Hooker, Uppsala University

Implementing Optimal Designs for Dose-Response Studies through Adaptive Randomization for a Small Population Group. (Abstract, Slides)

Chrystel Feller, Kirsten Schorning, Holger Dette, Georgina Bermann, Bjorn Bornkamp, Novartis.

Optimal designs for dose response curves with common parameters. (Abstract, Slides)

12.30 - 13.30





13.30 - 14.30

Florence Loingeville, Thu Thuy Nguyen, Marie-Karelle Riviere, France Mentre, INSERM.

Using Hamiltonian Monte-Carlo to design longitudinal count studies accounting for parameter and model uncertainties. (Abstract, Slides)

Jeremy Seurat, France Mentre and Thu Thuy Nguyen, INSERM.

Robust designs for longitudinal trials with binary data taking into account model uncertainty. (Abstract, Slides)

14.30 - 15.00





15.00 - 16.00

Martin Fink, Mark Milton, Phil Lowe, Novartis.

Informative study designs, where modelling and simulation based design features make a trial more informative than a comparable standard design. (Abstract, Slides)

Kayode Ogungbenro, Ivan Nestorov and Srividya Neelakantan, University of Manchester.

Sparse sampling design for characterizing individual PK of recombinant factor VIII fusion protein (rFVIIIFc) in prophylactic treatment of Hemophilia A. (Abstract)

16.00 - 16.30

General Discussion (Slides)

~ PODE ~