Incorporating a treatment into a classification scheme I have about 400 pieces of silver of different geometric dimensions. They were assigned to six groups and each group went through a series of stress tests, such as bending, pulling, putting in fire for a period of time, etc. The treatments that were given to the six groups were not the same, but fairly similar. The sizes of the six groups were not the same. The pieces either broke at some stage and that was recorded as a success or didn't, which was recorded as a failure. The time of each success was also recorded. The number of successes was about 80. 
My goal is build a predictive model to determine if a piece of silver breaks based on its physical dimensions and the treatment it goes through. 
I have been somewhat successful in building a model using the physical dimensions, but adding various aspects of the treatment (eg. total time spent in fire) didn't improve the performance at all. I have even tried to build features (eg.total stress on the metal in various directions, total strain on the metal, etc.)  based on the physical dimensions and the treatment, for each individual piece, but even these didn't add any predictive performance.   
How can I incorporate the treatment information in a way that adds to my predictive power? It is clear that the treatment is a factor in whether a piece breaks or not, and it should somehow show up somewhere. 
N.B. I didn't have any control over the design of the treatment, and testing more samples with other treatments is not an option for me. 
I'd very much appreciate any suggestions or comments.
Many thanks!
 A: You might try some tree based models, such as randomForest or GBM in R.  Both models are good at picking up non-linear effects and interactions, and both also produce variable importance measures that will probably be useful in your analysis.
GBM in particular might be useful, as it fits each successive tree to the residuals of the model.  In this way, after the model captures the effects of geometric dimensions, it will explore how the various treatments might be used to explain the "leftover" (or residual) variance. On the other hand, random forests require very little tuning and are harder to screw up than GBM models.
I would make sure each treatment is its set of variables, e.g. total time in fire, min/mean/median/max/cumulative bending and pulling pressure, etc.  Particularly in GBM models, more variables are better, so be thorough!
How are you measuring how "good" your models are?  Are you cross-validating them?
A: The functional form of the model is going to be very important here. In fact there might be interaction effects between the treatments (sensitivity of breaking to bending might depend on whether it has been put through fire before) and hence you need to use a non-linear functional form
So, instead of a form like: $$y=\beta_{fire}x_{fire}+ \beta_{bending}x_{bending} + .. $$ you might want to use a form: $$y=\beta_{bending-fire}x_{bending}x_{fire} + ..+\beta_{fire}x_{fire}+ \beta_{bending}x_{bending} + .. $$
You should start with this simple linear model and then move on to random forests since they will automatically create these interactions if they are important
