|Reference : Expected Design Space: a Bayesian perspective based on modelling, prediction and multi-c...|
|Scientific congresses and symposiums : Unpublished conference|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.
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