|Reference : Expected Design Space: a Bayesian perspective based on modelling, prediction and mult...|
|Scientific congresses and symposiums : Unpublished conference|
|Physical, chemical, mathematical & earth Sciences : Mathematics|
|Expected Design Space: a Bayesian perspective based on modelling, prediction and multi-criteria decision method|
|Lebrun, Pierre [Université de Liège - ULg > Département de pharmacie > Chimie analytique >]|
|Boulanger, Bruno [Université de Liège - ULg > Département de pharmacie > Analyse des médicaments >]|
|Non-clinical biostatistics conference (NCB09)|
|[en] The Design Space (DS) is defined as the set of factors settings (input conditions) that provides 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.
First, in a Bayesian framework, DS is derived from the joint predictive posterior distribution of the responses in a multivariate multiple regression problem using non-informative as well as informative prior distribution of the parameters. The case of DS in presence of highly correlated responses will be covered.
Second, a multi-criteria decision is taken with respect to the pre-defined acceptance limits, aiming to identify the DS of any analytical methods or other processes. A strong link is made between acceptance limits and objective functions to optimize, using desirability functions and index.
Examples based on high-performance liquid chromatography (HPLC) methods will be given, illustrating the applicability of the methodology with highly correlated and constrained responses.
There is no file associated with this reference.
All documents in ORBi are protected by a user license.