This post is part of our SAS Author Tips series. Today's post is from SAS Author Sanjay Matange. Do you have a complex multi-cell graph created in ODS Graphics Designer that you’d like to reuse with different data? Ideally, you’d like to change the data without having to change the plots in each [...]

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Ronald Snee and Roger Hoerl have written a book called Strategies for Formulations Development. It is intended to help scientists and engineers be successful in creating formulations quickly and efficiently.

The following tip is from this new book, which focuses on providing the essential information needed to successfully conduct formulation studies in the chemical, biotech and pharmaceutical industries:

Although most journal articles present mixture experiments and models that only involve the formulation components, most real applications also involve process variables, such as temperature, pressure, flow rate and so on. How should we modify our experimental and modeling strategies in this case? A key consideration is whether the formulation components and process variables interact. If there is no interaction, then an additive model, fitting the mixture and process effects independently, can be used:

c(x,z) = f(x) + g(z), where 1

f(x) is the mixture model, and g(z) is the process variable model. Independent designs could also be used. However, in our experience, there is typically interaction between mixture and process variables. What should we do in this case? Such interaction is typically modeled by replacing the additive model in Equation 1 with a multiplicative model:

c(x,z) = f(x)*g(z) 2

Note that this multiplicative model is actually non-linear in the parameters. Most authors, including Cornell (2002), therefore suggest multiplying out the individual terms in f(x) and g(z) from Equation 2, creating a linear hybrid model. However, this tends to be a large model, since the number of terms in linearized version of c(x,z) will be the number in f(x) times the number in g(z). In Cornell’s (2002) famous fish patty experiment, there were three mixture variables (7 terms) and three process variables (8 terms), but the linearized c(x,z) had 7*8 = 56 terms, requiring a 56-run hybrid design.

Recent research by Snee et al. (2016) has shown that by considering hybrid models that are non-linear in the parameters, the number of terms required, and therefore the size of designs required, can be significantly reduced, often on the order of 50%. For example, if we fit equation 2 directly as a non-linear model, then the number of terms to estimate is the number in f(x) plus the number in g(z); 7 + 8 = 15 in the fish patty case. Snee et al. (2016) showed using real data that this approach can often provide reasonable models, allowing use of much smaller fractional hybrid designs. We therefore recommended an overall sequential strategy involving initial use of fractional designs and non-linear models, but with the option of moving to linearized models if necessary.

We want to help scientists and engineers be successful at developing formulations quickly and efficiently. Success requires good strategies to get the right data in the right amount at the right time. That's why we published the book Strategies for Formulation Development: A Step-by-Step Approach Using JMP.

We have worked with formulation scientists and engineers for decades and have seen many different types of formulation development programs. This has shown us what formulation scientists really need to know rather than what is nice to know. Because JMP software is used in the examples in the book, readers get valuable guidance on the software for the proposed methodology. That means JMP users can immediately apply what they learn in the book.

Key takeaways from the book include:

• Approach the development process from a strategic viewpoint, with the overall end in mind. Don’t necessarily run the largest design possible. An experimentation plan that implements the strategy provides the right road map for developing a successful formulation.
• Focus on developing understanding how the components blend together. Use designs and models that help find the dominant components, components with large effects, and components with small effects.
• Use screening experiments early on to identify those components that are most important to the performance of the formulation. This strategy creates a broad view and helps ensure that no important components are overlooked. It also saves significant experimental effort.
• Analyze both screening and optimization experiments using graphical and numerical methods, which is easily done with JMP. The right graphics can extract additional information from the data.
• Consider integration of both formulation components and process variables in designs and models, using recently published methods that reduce the required experimentation by up to 50 percent.

This is how you speed up the formulation development process and produce high-quality formulations in a timely manner. Upcoming blog posts will show how to address each of these important issues.

Want more information? You can read a free chapter from the book and learn about authors Ronald D. Snee and Roger W. Hoerl.

The post Formulation success: Getting the right data in the right amount at the right time appeared first on JMP Blog.

Do you want to discover new and useful knowledge in your data using interactive, dynamic graphical displays? Would you like to be able to make sound decisions faster by understanding the patterns of variation in your data and separating it into useful signal and random noise? You can, with the […]

The post Visual Six Sigma: A practical approach to data analysis and process improvement appeared first on JMP Blog.

Everyone’s favorite mash-up of JMP and SAS software will be at PharmaSUG in the Mile-High City, May 8-11. Stop by our booth in the exhibition hall to see demos of JMP and JMP Clinical, as well as of JMP Genomics, another JMP and SAS combination. You can be among the […]

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Continuing your education can be daunting. Just thinking about all of that time that could be spent relaxing and you have to carve out two hours to study…really! Trust me when I say, I feel your pain. BUT, you will reap the rewards ten-fold, I promise. Check out these top […]

The post Get dedicated to your learning with these top-selling SAS Press books of 2015 appeared first on SAS Learning Post.

Ask any user how they first learned JMP, and there’s a good chance that they’ll cite JMP Essentials – An Illustrated Step-by-Step Guide for New Users as a resource they relied on. Authors Curt Hinrichs and Chuck Boiler have written a second edition of this very popular book that promises […]

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This guest blog post comes from Dr. David Dickey, one of our original SAS Press authors. Hope you enjoy! In the late 1970s, shortly after SAS was founded, I was approached by Herbert Kirk and John  Brocklebank from SAS to put together a course on time series.  This was reasonably […]

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SAS is here to help. Whether you are teaching students how to use SAS or are using the power of SAS® to apply analytical intelligence to any data in any discipline, we have a variety of resources available to you including our Academic Evaluation Copy Program. SAS Press will provide […]

The post Teaching a SAS® Class and need assistance? appeared first on Generation SAS.

This tip is from Heath Rushing, coauthor of Design and Analysis of Experiments by Douglas Montgomery: A Supplement for Using JMP®. After a recent design of experiments (DOE) course, a student asked about experiments with dependent factors. Throughout the two days of training, we spent considerable time designing experiments to […]

The post Tips on mixing Grandpa McCafferty’s cough syrup: A statistically designed experiment appeared first on The SAS Bookshelf.