Inquiry about Experimental Design with JMP

PolygonGiraffe5

Hello,
I have a concern. I am a beginner in experimental design, and I am conducting formulation trials for medicines.

To start, I screened a number of factors and variables to narrow down the factors for my experimental design.

I would now like to optimize. The parameters are as follows:

• 3 responses: Y1, Y2, Y3
• 4 factors, of which 3 are continuous (each with 3 levels), and 1 is categorical with 2 levels.

I am a beginner with JMP and am wondering whether I should use an optimal DOE plan, a classical plan, or a definitive screening design.

Additionally, I am struggling to set the number of levels for the continuous factors, which are at 3 levels.

My question is, in my case, how many experiments would be the minimum required to be representative?
What is the ideal model for my experimental design? How many replicates would you recommend?

I sincerely thank you in advance for your help.

Victor_G

Hi @PolygonGiraffe5,

 

Welcome in the Community !

 

As the name suggest, a Definitive Screening Design is a powerful screening design used at the beginning of a project, with a high number of potential factors, in order to screen active and important factors and optimize response(s), if the number of active factors is sufficiently low. Classical designs like Response Surface Design can be used for optimization, but they won't take advantage of the previous experiments you already have done, and may be less flexible in terms of necessary experiments number. Another last option could be the use of Space-Filling Designs, that are more flexible as they don't rely on a pre-specified model, but can be more costly to run, as the points are generated in order to cover the experimental space homogeneously.

 

If you have already done a screening phase with the factors identified for the optimization phase, you could try to directly use these preliminary runs from your screening design and Augment your Design on your active factors with various strategies, like model-based augmentation or model-agnostic (space-filling) augmentation.

In case of model-based augmentation, the number of levels of your continuous factors will depend on the complexity of the assumed model : if you specify quadratic (=2nd order) terms for factor A, then 3 levels will be needed for this factor. Selecting this augmentation and specifying a Response Surface model (with main effects, 2-factors interactions and quadratic effects) could be a good first start to create your design.

In case of space-filling augmentation, the number of levels for your continuous factors will depend on the type of space-filling and more importantly on the total number of runs allowed : the more runs in total, the more levels each factor will have.

 

About the question of "representativeness" and replicates (more generally about design creation options), this is a question you have to answer with domain expertise and statistical tools like Compare Designs platform : What is your objective (explainability, predictivity, both ?) and precision required (maximum and average prediction variance for example) ? What is your maximum experimental budget ?
These questions can help you find the best compromise when evaluating and comparing your designs with the Compare Designs platform.

 

Hope these answers will help you,