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Newark, DE, United States

Snee R.D.,Snee Associates LLC | Hoerl R.W.,General Electric
Quality Engineering | Year: 2012

Global competition and information technology are forcing changes in all aspects of how we operate organizations around the world. These forces are also having profound effects on how statistical thinking and methods are used and the roles of statisticians and quality professionals. We have argued that statisticians and quality professionals can deal with the rapid change taking place by using statistical engineering to identify and solve large, unstructured, complex problems. We show that enhanced leadership skills are needed to take advantage of the benefits of statistical engineering because of the required major changes in organizations that are typically associated with such problems. The purpose, benefits, and use of leadership in a statistical engineering context is discussed and illustrated. It is concluded that by becoming stronger leaders, statisticians and quality professionals will increase their impact on the organizations that employ them as well as enhance their personal reputations and that of the profession. Copyright © Taylor & Francis Group, LLC. Source


Wang Y.,Rutgers University | Snee R.D.,Snee Associates LLC | Meng W.,Rutgers University | Muzzio F.J.,Rutgers University
Powder Technology | Year: 2016

Purpose: The purpose of this study is to develop a model for predicting the flow properties of a four-component powder mixture. Method: To build the model, 22 samples were prepared using an extreme vertices mixture design. The flow properties were characterized using rotational shear cell methodology. Two additional blends were tested for external validation to illustrate model applicability. Results: Cohesion was shown to be in a linear relation with unconfined yield strength and a power relation with flow factor. The special cubic model was used to build a mathematical model. Normality test of residuals showed that the regression model was more robust to predict cohesion than to use flow factor. Conclusion: This QbD approach is shown to be useful for predicting flow performance and finding design space during formulation development. © 2016. Source


Snee R.D.,Snee Associates LLC
BioPharm International | Year: 2010

Experience using Quality by Design in upstream processes has identified several things that can improve the application of the method. When the experimental environment is diagnosed and strategies are developed to match the environment, experimentation moves more quickly and the critical process variables are identified with higher probability. This article presents a process for developing experimental strategies. The approach focuses on enhancing process understanding by developing the right data, at the right time, and in the right amount to maximize the time-value of the data collected. The development and operation of robust measurement methods that produce high quality data and streamlining of experimentation work processes also is discussed. Source


Wang Y.,Rutgers University | Snee R.D.,Snee Associates LLC | Keyvan G.,Rutgers University | Muzzio F.J.,Rutgers University
Drug Development and Industrial Pharmacy | Year: 2016

Statistical methods to assess similarity of dissolution profiles are introduced. Sixteen groups of dissolution profiles from a full factorial design were used to demonstrate implementation details. Variables in the design include drug strength, tablet stability time, and dissolution testing condition. The 16 groups were considered similar when compared using the similarity factor f2 (f2450). However, multivariate ANOVA (MANOVA) repeated measures suggested statistical differences. A modified principal component analysis (PCA) was used to describe the dissolution curves in terms of level and shape. The advantage of the modified PCA approach is that the calculated shape principal components will not be confounded by level effect. Effect size test using omega-squared was also used for dissolution comparisons. Effects indicated by omega-squared are independent of sample size and are a necessary supplement to p value reported from the MANOVA table. Methods to compare multiple groups show that product strength and dissolution testing condition had significant effects on both level and shape. For pairwise analysis, a post-hoc analysis using Tukey’s method categorized three similar groups, and was consistent with level-shape analysis. All these methods provide valuable information that is missed using f2 method alone to compare average profiles. The improved statistical analysis approach introduced here enables one to better ascertain both statistical significance and clinical relevance, supporting more objective regulatory decisions. © 2015 Taylor & Francis. Source


Snee R.D.,Snee Associates LLC | Hoerl R.W.,Union College at Schenectady | Bucci G.,Out of Door Academy
Quality Engineering | Year: 2016

Experimenting with both mixture components and process variables, especially when there is likely to be interaction between these two sets of variables, is discussed. We consider both design and analysis questions within the context of addressing an actual mixture/process problem. We focus on a strategy for attacking such problems, as opposed to finding the best possible design or best possible model for a given set of data. In this sense, a statistical engineering framework is used. In particular, when we consider the potential of fitting parsimonious linear additive or nonlinear models as opposed to larger linearized models, we find potential to reduce the size of experimental designs. It is difficult in practice to know what type of model will best fit the resulting data. Therefore, an integrated, sequential design and analysis strategy is recommended. Using two published data sets and one new data set, we find that in some cases nonlinear models, or linear additive models —with no process/mixture interaction terms, enable reduction of experimentation on the order of 50%. In other cases, additive or nonlinear models will not suffice. We therefore provide guidelines as to when such an approach is likely to succeed, and propose an overall strategy for these types of problems. © 2016 Taylor & Francis Source

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