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Methods for Developing a Process Design Space Using Retrospective Data

Prospectively planned designs of experiments (DoEs) offer a valuable approach to preventing collinearity issues that can result in statistical confusion, leading to misinterpretation and reducing the predictability of statistical models. However, it is also possible to develop models using historica...

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Detalles Bibliográficos
Autores principales: Romero-Obon, Miquel, Pérez-Lozano, Pilar, Rouaz-El-Hajoui, Khadija, Suñé-Pou, Marc, Nardi-Ricart, Anna, Suñé-Negre, Josep M., García-Montoya, Encarna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675834/
https://www.ncbi.nlm.nih.gov/pubmed/38004608
http://dx.doi.org/10.3390/pharmaceutics15112629
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author Romero-Obon, Miquel
Pérez-Lozano, Pilar
Rouaz-El-Hajoui, Khadija
Suñé-Pou, Marc
Nardi-Ricart, Anna
Suñé-Negre, Josep M.
García-Montoya, Encarna
author_facet Romero-Obon, Miquel
Pérez-Lozano, Pilar
Rouaz-El-Hajoui, Khadija
Suñé-Pou, Marc
Nardi-Ricart, Anna
Suñé-Negre, Josep M.
García-Montoya, Encarna
author_sort Romero-Obon, Miquel
collection PubMed
description Prospectively planned designs of experiments (DoEs) offer a valuable approach to preventing collinearity issues that can result in statistical confusion, leading to misinterpretation and reducing the predictability of statistical models. However, it is also possible to develop models using historical data, provided that certain guidelines are followed to enhance and ensure proper statistical modeling. This article presents a methodology for constructing a design space using process data, while avoiding the common pitfalls associated with retrospective data analysis. For this study, data from a real wet granulation process were collected to pragmatically illustrate all the concepts and methods developed in this article.
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spelling pubmed-106758342023-11-16 Methods for Developing a Process Design Space Using Retrospective Data Romero-Obon, Miquel Pérez-Lozano, Pilar Rouaz-El-Hajoui, Khadija Suñé-Pou, Marc Nardi-Ricart, Anna Suñé-Negre, Josep M. García-Montoya, Encarna Pharmaceutics Article Prospectively planned designs of experiments (DoEs) offer a valuable approach to preventing collinearity issues that can result in statistical confusion, leading to misinterpretation and reducing the predictability of statistical models. However, it is also possible to develop models using historical data, provided that certain guidelines are followed to enhance and ensure proper statistical modeling. This article presents a methodology for constructing a design space using process data, while avoiding the common pitfalls associated with retrospective data analysis. For this study, data from a real wet granulation process were collected to pragmatically illustrate all the concepts and methods developed in this article. MDPI 2023-11-16 /pmc/articles/PMC10675834/ /pubmed/38004608 http://dx.doi.org/10.3390/pharmaceutics15112629 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Romero-Obon, Miquel
Pérez-Lozano, Pilar
Rouaz-El-Hajoui, Khadija
Suñé-Pou, Marc
Nardi-Ricart, Anna
Suñé-Negre, Josep M.
García-Montoya, Encarna
Methods for Developing a Process Design Space Using Retrospective Data
title Methods for Developing a Process Design Space Using Retrospective Data
title_full Methods for Developing a Process Design Space Using Retrospective Data
title_fullStr Methods for Developing a Process Design Space Using Retrospective Data
title_full_unstemmed Methods for Developing a Process Design Space Using Retrospective Data
title_short Methods for Developing a Process Design Space Using Retrospective Data
title_sort methods for developing a process design space using retrospective data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675834/
https://www.ncbi.nlm.nih.gov/pubmed/38004608
http://dx.doi.org/10.3390/pharmaceutics15112629
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