Reporting DOE results for publication

[#1]

Hi, 

I am writing up a manuscript that includes Design of Experiments (Plackett-Burman DOE and Full Factorial DOE). 

However, I am unsure what is important to report in the publications to be as transparent as possible. 

So far, I have included the model metrics (R2, R2 adjusted, PRESS, model p-value, lack of fit p-value) as well as the significant terms and interactions and their p-value. I believe reporting parameter estimates for the factors/interactions does not make sense when you have a regression model that includes significant interaction terms (since these parameter estimates change due to interactions). In that case, it is just best to use the prediction profiler. However, the prediction profiler is very difficult to include for publication. So how can I report the results of the factors and their interactions in a meaningful way? 

I would appreciate any advice. 

Thank you

Sara 

[#2]

No, I think there is a misunderstanding between the slope at a specific location depending on the levels of a factor, and the main effect of this factor. The slope can change depending where you are in the response surface, but the main effect estimate doesn't change.

[#3]

@SaraA you can always put the Parameter Estimates into the supplementary information for those who may be interested in - if you're publishing to a journal for something like biotechnology development (like I did) the target audience wouldn't gain much use for their inclusion in the main text (in my opinion). In terms of what they are - the coefficients are the amount of change in the response for every one-unit change in the predictor (if the others are held constant) - you will see these exact same values appear in the prediction formula.

Thanks,

Ben

[#4]

@Ben_BarrIngh 

In terms of what they are - the coefficients are the amount of change in the response for every one-unit change in the predictor (if the others are held constant)

This is exactly my point - when there is an interaction, you cannot change one term one unit while keeping the other terms constants due to the interaction. If you change term X with one unit, term Y will change as well (if there is an interaction between X and Y). This is why I was taught that it is not useful to report parameter estimates of main effects when there are significant interaction terms. 

[#5]

Reporting the coefficients is not the issue,  Interpreting the results is.  If an interaction is significant, then you must be careful interpreting the main effects involved in the interaction.

[#6]

Hi @SaraA ,

Yes, the scale of the change that X1 can cause to your response can be altered when there are interaction terms with X2 (i.e. holding X2 at a high value may diminish X1 completely) but the parameter estimates can be used to show a general relationships. If you have a sufficient enough description of your system in your discussion and how those interactions work, the parameter estimates could serve to provide more detail

Thanks,

Ben

[#7]

The other thing to keep in mind wrt to interactions in general is if there is any confounding of effects brought about by the inherent design itself...in other words, any fractionation in the design and are the interaction effects truly estimable? One other failure mode is, for example, if you lost some treatment combinations, some effects may be inestimable. But if the Experimental Execution Gods smiled on the execution of the experiment and all went as planned, then one other way to show interaction effects is to include the Fit Model Report, Interaction Plot report. Described here: Interaction Plots. Not nearly as impressive as the Prediction Profiler...but does the job.