Welcome, everyone.
Today, I will be going through custom heat maps
and how they provided some new insights into pet treating.
First of all, I'm Jared Shaw
and I work for Mars.
I've been with Mars for eight years.
Prior to Mars, I worked in semiconductor I ntel
and IM flash technologies for about 13 years.
My background is statistics and education.
I've done a lot of consulting over the years as well as
teaching others in various statistical methods.
I'm married and I have three kids.
They're all adopted.
I play games on the sidelines,
build models and then periodically camping.
Then I also tinker around with construction.
On the bottom right, there is a room over my garage that I finished.
Today, I'll be going through basically the abstract I submitted.
Then C&T stands for care and treat.
I'll give some background on that for Mars.
I'll go through measuring efficacy,
research protocols we have for these types of studies.
Then I'll get into the JMP portion.
This is not a live demonstration of JMP.
I'll just be showing some images from the program
and talking through some approaches that we used in looking at this data
and then ending up with the new approach that I introduced.
First of all,
the abstract here is I have these custom heat maps
and they provide a lot of insights into pet treating.
Overall, our intention is to improve the cleaning of pet's teeth with a new treat.
We do this by changing texture, changing ingredients.
We want to do something that will help impact the teeth,
but also be safe and delicious
and fun for the pet as well as the pet owner.
The results provide a lot of insights,
including patterns that we see across the mouth.
How does the product affect the teeth?
The current graphs and methodologies.
The modeling is pretty good,
but the methodologies and how we showcase this data is not very good.
It doesn't offer very clear insight unless there's a remarkable difference
between the products that are being tested.
I found that there is a great opportunity to utilize JMP mapping
to create some custom maps.
These new images,
they spawned a whole huge investigation,
brought in some new associates to do some great insights and learning.
Just from a simple image, brought some great rewards.
Maybe to help bottom or to ground us to a baseline.
First of all, let's consider what Care & Treats means.
Pet care consists of a dry diet and you also have wet diets
and then the Care & Treat components.
This is split up into two pieces, the treat and then the care.
Treating products,
they have a high palatability, they excite pets.
They may add some extra nutrition
and supplement the pet's diet.
They're used for training.
Positive reward in training, getting the pet to respond to your voice, etc.
In some cases, they're long- lasting to help relieve boredom,
reduce destructive behavior and so forth.
The care products, on the other hand,
these are also have a high probability to encourage consumption.
But these promote a healthy teeth
that we concentrate a lot on oral care, reducing bad breath
and they can also act as a medication
to give appeal to your pet.
You may have heard of like a pill pocket, et cetera.
Now, one of the main drivers of these treats is texture
and that really promotes the consumption benefits.
Does it become fun to chew the product?
For those that have dogs,
you may notice that your dog,
in some cases when they start eating a product,
they seem to inhale it more than they chew it.
The texture piece definitely is something that you want to have them
enjoy the experience of biting into the product.
This is just an image to help us understand
what we're talking about for treating
and trying to clean the teeth for our pets.
These are just a couple of different images that show
the breakdown of the teeth in pets
and how we want to understand
how a product is impacting these different teeth.
How do we measure efficacy?
Efficacy is basically how well the product is running.
Periodontal disease is the most widespread of oral disease in pets.
Companies all over, when they get into the care space,
they're looking for how they can take texture, how they can take shapes,
how they can build a chew that really affects this oral care.
They want to measure the efficacy of the treats.
They use different approaches over the years.
I'm not really going to go too much into these.
This is just more informational...
Years ago, there was a Logan & Boyce method.
This is a visual measure on the teeth.
It was invasive in how they did it against the pets
and you can read more about that on your own.
But there's also GCPI.
This was less invasive to the pets, but still a manual approach.
Then recently, probably within the last maybe 4 or 5 years,
they came up with this QOLF technique
that essentially takes an image of the teeth before and after.
We find that this is just much more informative.
It gives us much clearer results on how things are proceeding when
the pets consume these products.
Efficacy formula for itself.
First of all, there's what's called an ITS
and you'll see this in the data later on.
It stands for Individual Tooth S core.
Basically, it's looking at how much plaque exists on the tooth.
In this case for the data that I'm using is based upon the GCPI approach.
The length of the tooth and this gives us an idea of basically,
how much plaque is on each one of those teeth.
The Chew X, you'll see...
Actually, have them called different names.
But essentially, this is the treat that's being tested.
Then the overall efficacy,
this is again, that ability
or can we produce the intended result from the product?
This looks at the calculation takes the no chew,
subtract the result of the chew and then divides by that no chew
and we get that efficacy.
The research protocol.
The background image here is actually
one of our feeding center here in Tennessee.
The round sections, there are dog pods.
We have several dogs within each one of those pods
and then the center building has the cat rooms.
What we do is we prepare the pets by cleaning their teeth
so they get a professional cleaning.
We try to get all of plaque off the teeth to give them a score of zero.
We run a crossover design.
Essentially, this means that every Chew is going to be administered to every pet.
Not at the same time.
We break it up into different phases.
In each of these phases, the chews are then rotated
against different dogs,
as I have written here, or cats as well.
Scoring this is essentially done between each phase.
After a phase of the study,
so after those pets, they have their standard diets.
They get maybe a treat product at the end of the day
and then at the end of whatever prescribed time frame,
they measured the amount of plaque that is on the teeth.
Cleaning teeth with a score of zero across all the treatments.
We just removed these from the study.
It's just something that where they're consuming the product.
But for whatever reason, that tooth didn't get impacted by the product.
Typically, we'll see these with the front teeth,
the in cisors, they're used more for cutting.
Generally, the products are more about the chewing behavior.
This whole mouth,
what this is talking about is some cases, we have these individual tooth scores.
We have zero on a No Chew, so basically it's missing,
or we find that the No Chew has results that are less than the tooth.
Basically, No Chew means that
for that phase of the study, the dog or cat,
they did not receive a treat product to consume,
they just had a standard diet.
The Chew X means that they had some care treat at the end of the day.
We summarize the data across the whole mouth.
Sometimes we'll break it up into regions
in order to give us an idea of how it's performing.
The analysis protocol itself, really these are done with linear mixed models.
We have fixed effects with maybe treatments
or the regions depending on the study.
Then we have random effects that's really focused around the pet ID.
Again, the intent here is that we have the effects affect all pets
regardless of the specific pets in the study.
We also then run specific contrast.
This is where we look at different sizes of the mouth,
different regions, etc .
Over here on the right- hand side in this table
is an example of some of those contrast
and depending on the number of contracts we run,
of course, we're going to use the FWER,
to control for inflated error.
Then we communicate these results.
We take the analysis results,
we take images and then we sit down with the stakeholders
and we show them which of these Chews
was better essentially.
Initially, when I started getting involved in these studies,
it was very, this Chew did better versus this other Chew for the whole mouth.
But as we started bringing in different regions of the mouth,
we started seeing some different results
and had much more fruitful discussions.
This will get us into the analysis.
What I'm going to do here is
I'm just going to concentrate really on the data visualization component.
I'm not going to go too much into the statistics on the modeling piece.
This is just about visualization
and in this first part is specifically about
ways that we are trying to visualize the teeth initially.
This is a results, this is from JMP,
from running the mixed effects model
and then at the end here we are running these contrasts.
In these type of results as we look at these,
because I have here marked in the center,
the Chews would show no difference
but areas of the mouth would, particularly the molars.
You can see here on the left, I have just Chew by itself being compared
and then on the right- hand side,
you see I have different areas of the mouth.
Different areas of mouth were showing interesting differences
but the Chew by themselves compared to No Chew,
maybe we're not seeing too much for one of them but some for another.
Then we would group them into different sections used in the variability chart.
This shows my different Chews with the no Chew
and then again the areas of the mouth.
In this case, I would see that the mean of the data is here on the left
and then the standard deviation of the data is on the right.
Definitely, one area of the mouth is operating differently
than another, as I can see here on the right- hand side.
Let me just turn on my pointer.
Over here, we can see that this variability for the lower molars
versus the upper molars is different for different Chew.
Other ways that we tried to portray this is using graph builder,
we used the area of the mouth over here.
I forgot to mention this earlier but the IUL,
this is in sisors and upper lower canine teeth
and then these are the molars.
We can see definitely some pattern going on
when I compare across different phases.
I have phase 1, phase 2 and phase 3.
Phase 3, it looks like I have this linear effect of sorts
that's occurring between the Chews.
It's just how it's showing up visually.
It doesn't mean
in the order in which they are given,
it's just what the data is showing.
Looking further into the variability chart,
bringing in the phases.
We're like "Hey, do we see differences per phase?"
Here we really see for this Chew W,
the variability was much lower than No Chew and Chew P.
It really starts questioning, well, what's going on here?
Why is this specifically happening for this Chew?
What could we do to understand that better?
Another visual that we generated for this study
is we again, summarized it by the area of the mouth
and then the Chew efficacy for each of the Chews themselves.
We can definitely see some differences between the Chews,
but overall, they might seem like that they're similar
even though we're seeing differences in the areas of the mouth.
One of the things that I started asking is like
why do we see these differences between areas of the mouth
but we don't see across the Chews as much what's going on?
Here I generated a plot where down here on the x- axis,
I have the different dogs
and then the Chew efficacy for the W and P Chews
and then areas of the mouth.
Definitely, what's interesting here is that
particular animals are showing the difference
and other animals are not now.
We would expect this,
given randomness of the study that the Chews are going to behave differently
and how the pet is consuming the product.
We really started making me think it,
much more deeply about the data
and say really what's going on here in this data?
Do the pets chew the product differently?
That led me into this data visualization for the second part
because it made me really start thinking about the data,
what can I do or how can I look at this differently
to bring out this individual component of the pets.
I was working with a research scientist
and we were going through one of the studies
and they happened to have this card.
As you can see here on the right hand side, this is just a picture of the card.
They had this sitting on their desk and I was sitting there staring at it.
I had the idea, "What if I created a tooth map in JMP?"
I could then color each of these individual teeth
and maybe get some clarity,
further clarity in these studies than what we were looking at.
I went and contacted our Waltham scientist.
Waltham is a site within pet care in the UK
and they concentrate on doing research on the pet nutrition.
I went and talked to them and one of their scientists drew me up some teeth.
Here for this first slide, we have the dog teeth.
The map is here on the left that they drew up.
On the right is just a visual to give you an idea
of the different types of teeth that show up in the dog's mouth.
Then we see something similar for cat teeth.
Again, on the left is the one that was drawn up for this study.
What I did then is this is the map creator.
This is a script that's available on the JMP community.
This is an older script.
It's been a while since it's been updated,
but I found that it was very helpful for this scenario
and so downloaded the add in
and it creates the add- in pull- down menu.
You go to the add- in pull- down as you see up here on the upper left.
You can click on Map Shapes and then you can do the Custom Map Creator.
When this pops up, you get this screen here, again without the teeth.
Then you get a couple of empty tables.
I dragged the image file onto the map.
I gave it a name.
Then I go over here to this next section, and basically, I start tracing the teeth.
For every single tooth, I would trace it and then I would give it a name.
This is an example of after doing all of that work.
As you can see, these are all of the individual plot points,
is me just clicking around that tooth to try to get the entire shape
accurately, so it would show up as accurate as possible
on the screen when we look at the plots.
Then we have our different files here.
You have this XY.
This gives you the coordinates.
Down here on the graph on the lower left, you see this is a zero, zero.
This is essentially like an X, Y coordinate system.
It's just telling me where on the graph that particular point is
for that particular shape ID.
Then I have a name file that gives me the shape ID
and then the name of the tooth.
In this case, I created a separate file for dog teeth
and of course, for cat teeth,
since they are different shapes of teeth and different shapes of the jaw.
D just stands for dog and then the ID number for that tooth.
One thing that I found interesting is when
I first created this program, this was a few years ago,
I was able to just create the maps, and I created a custom script.
People would run the script,
it would save the maps to their C drive and everything would work fine.
But soon after a couple of years, it no longer worked.
It was because Mars entered in some security protocols that
basically wouldn't allow us to save maps to the C drive.
It basically locked it up.
I had to go out and figure out, well, how can I still do this?
I want to see the maps, we want to create these maps.
Then I found another community forum that talked about putting it out
onto the app data for your username roaming, etc .
You see the path here and so you put your maps there
and it works just as if I put them on the C drive.
Here, once you have those files saved on to their proper location,
then you go into JMP Graph Builder.
If you've never used it down here on the lower left- hand corner of the screen,
it doesn't show it, it just says Ma.
But that is the map feature.
Since I gave these tooth IDs as map component or the name component,
then I take that tooth ID and drag it down to that section.
When you do, you can see here in the background, I see those teeth showing up.
Not all of the teeth show up because for this particular study,
I did not look at every single tooth.
You can see the incisors are missing.
You can right- click on the image and go to Map Shapes
and show the missing shapes.
When you do that, you get all of the teeth showing up.
In addition, I took the different Chews that were investigated
and I dragged that up here to the Group X up here at the top.
We see these three different maps for each of the Chews and the No Chew.
I then take the ITS and pulled it over here to color.
Again, ITS is the individual tooth score, about how much plaque is on the tooth.
At this point, this has given me the average amount of plaque
that is on all of these dog's teeth that were in this particular study.
I can start to see where that plaque is showing up on the No Chew .
Definitely, it's on these molars especially down here on the bottom molars,
on the right- hand side especially.
Then I can also see for the different Chews ,
I could see how maybe some of the canine teeth
on average was showing that some of this plaque remained on the teeth.
But definitely, I'm seeing some cleaning of the molars.
Clicking on done and giving me the bigger image
so I can see it in more detail.
This leads me into data discovery.
I created these maps and they're looking great.
People like, "Hey, this is a really interesting
way of looking at the data."
But I wasn't done there.
We started discovering something when we looked at the maps differently.
In this case, this is just back to where we were, that same map.
What I did is I started,
I put a local data filter on and by the dog names.
Here I have dog names on the left, turn on a local data filter
and I can now filter on each one of these dogs
and look at them individually.
Now, we don't have time to go through all of them,
but I wanted to show just a few of them.
Here for Aura.
What was interesting for Aura is that
we noticed that for this Chew P,
that these lower molars on the right- hand side
weren't really getting cleaned very well.
According to the score, they weren't getting cleaned at all.
This started telling me as I looked at this, "M an, this particular pet,
Aura would only chew the product on the left side."
For this Chew W, we actually saw a different signal.
She actually chewed the product that seemed like
more on both sides of the mouth.
Very interesting results.
A gain, these are just two different types of textures
that are being looked at for this product.
Going down to another dog here, Gretchen.
She showed something different.
For Chew P,
she did very well with that Chew,
but for Chew W,
she preferred to chew it more on the right side of her mouth than the left side.
Again, these are completely two different animals
and they chew the product differently depending on the texture
and their preference to the texture.
Not all dogs, we are starting to see here like the same texture.
They're very individual.
When I was doing this, I started asking friends of mine,
"Do you chew with one side of your mouth for particular products?"
Sure enough, as we started collecting that data, we found, like for myself,
I like to chew nuts, but only on the left side of my mouth.
Others, when they chewed nuts, it didn't matter which side, etc .
As we started talking about it and taking record of it,
we started to see,
"Hey, these pets are consuming product more like
an individual human does when they have preferences in how they look at product."
And just a couple more dogs to look at here.
Bagel this one, the top teeth clean better than the bottom teeth.
Just fascinating results. How is that possible?
Because the product is when they're chewing it,
they're biting down into the product.
Your teeth, your top teeth and your bottom teeth are sinking into that material.
Why would we get certain components showing up here?
Basically, what it means for this particular dog, for Bagel,
it's just the rear molars that Bagel was using to chew into the product.
The front molars, who wasn't using at all.
Then Muck, this is a great example of the dog.
Either they're not consuming the treat at all, or they're just inhaling the treat.
Those are some of the customer calls that we sometimes get is,
"Hey, my dog is not even chewing this product.
They're just like taking it and swallowing it whole."
Very interesting results.
A s we started looking into this data more and more,
it really led us to believe that, "Hey, we need a new product.
We need to create something that will really bring in a whole mouth clean.
A chew experience where the animal likes to chew,
likes to really get into the product and consume it
and to have that efficacy result
where the product is helping to clean the teeth."
In summary,
what I've learned from this experience is
that historical studies for this particular experience were basically
giving an average across the whole mouth
and it wasn't sufficient in really giving us a good idea
of what was happening with the product.
Viewing these tooth maps by individual pets
started showing some very interesting results
that really we couldn't even look at it
by reaching into the mouth across all of the pets.
We actually need to start looking at it by individual and start classifying it by,
"Hey, this particular treat impacted these teeth only,
and this particular treat impacted these teeth only."
Start classifying it in that way
so that we can start learning a lot more about the texture of the product
and how it was consumed.
Now these findings,
we started applying this across all studies, all historical studies.
We pulled this into a large analysis
that started really digging into it to learn more
from the history of what we've done
and how it affects things moving forward.
Of course, this led into some new product development.
Here's an example of what that is.
Unfortunately, I can't show it to you.
The image is protected.
It is not yet released, but it is something that we're investigating.
Thank you.