Reference : Design Space and desirability index. A Bayesian predictive risk-based approach to flexib...

	Document type :	Scientific congresses and symposiums : Unpublished conference
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Mathematics
	To cite this reference:	http://hdl.handle.net/2268/93781

Title :	Design Space and desirability index. A Bayesian predictive risk-based approach to flexibly achieve multi-criteria decision methods.
Language :	English
Author, co-author :	Lebrun, Pierre [Université de Liège - ULg > Département de pharmacie > Chimie analytique >]
	Boulanger, Bruno [ > > ]
	Hubert, Philippe [Université de Liège - ULg > Département de pharmacie > Chimie analytique >]
	Mbinze Kindenge, Jérémie [Université de Liège - ULg > > > Form. doc. sc. bioméd. & pharma.]
	Debrus, Benjamin [Université de Liège - ULg > Département de pharmacie > Chimie analytique >]
Publication date :	2-Mar-2011
On invitation :	No
Audience :	International
Event name :	The Second International Symposium on Biopharmaceutical Statistics
Event date :	from 1-3-2011 to 3-3-2011
Event organizer :	The International Society for Biopharmaceutical Statistics
Event place (city) :	Berlin
Event country :	Germany
Abstract :	[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.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/2268/93781

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	Fulltext file(s): File Commentary Version Size Access Restricted access ICH Q8 Bayesian DS2.pdf Author preprint 7.9 MB Request copy

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