Reference : Design Space and desirability index. A Bayesian predictive risk-based approach to flexib...
Scientific congresses and symposiums : Unpublished conference
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/2268/93781
Design Space and desirability index. A Bayesian predictive risk-based approach to flexibly achieve multi-criteria decision methods.
English
Lebrun, Pierre mailto [Université de Liège - ULg > Département de pharmacie > Chimie analytique >]
Boulanger, Bruno mailto [ > > ]
Hubert, Philippe mailto [Université de Liège - ULg > Département de pharmacie > Chimie analytique >]
Mbinze Kindenge, Jérémie mailto [Université de Liège - ULg > > > Form. doc. sc. bioméd. & pharma.]
Debrus, Benjamin mailto [Université de Liège - ULg > Département de pharmacie > Chimie analytique >]
2-Mar-2011
Yes
No
International
The Second International Symposium on Biopharmaceutical Statistics
from 1-3-2011 to 3-3-2011
The International Society for Biopharmaceutical Statistics
Berlin
Germany
[en] The Design Space (DS) is defined as the set of factors settings (input conditions) that will provide results at least better than pre-defined acceptance limits. The proposed methodology aims at identifying a region in the space of factors that will likely provide satisfactory results during the future use of an analytical method or process in routine, through an optimization process.

In a Bayesian framework, the responses are modelled using a multivariate multiple regression model allowing deriving their joint predictive posterior distribution.

On the basis of this consequent distribution, a multi-criteria risk-based decision is taken with respect to the pre-defined acceptance limits. This aims to identify the DS. In this context, desirability methodologies are also applied to take the risk-based decision in a more flexible way.

An example based on high-performance liquid chromatography illustrates the applicability of the methodology with highly correlated and constrained responses.
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/93781

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Restricted access
ICH Q8 Bayesian DS2.pdfAuthor preprint7.9 MBRequest copy

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.