Design of Experiments (DOE) in Blood Testing / Healthcare

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Mark: Okay. This is one is something that affects us all, and that is blood testing. Blood analysis. And we all go in, sooner or later, to have blood tests done. And you got to wonder if the results that come back are accurate — are they false positives, false negatives — if we go in. And this comes from Abbott Laboratories in (Unclear 55:26), their Japanese affiliate. So, this came, and now you can see here. They are interested. Their experts say,” Well, they have got a response. It is called a signal.” And this is a signal that they are targeting for sixteen hundred and twenty. They have a machine in this blood test that records the target, or the value of the signal. Their target is 1620 and their specs — they have specs. Now, what are the specs? The 1570 to the 1670 on this normal distribution at the bottom of the page. You will see the specs. The specs are right here, Mike, where the red starts on the left and where the red starts on the right.

Michael: Yeah.

Mark: Those are the specs — at 1570 and 1670. So you can see that you got a lot of red. You got a lot of auto spec, possible false positives, false negatives coming out of this test. That would not hack it, okay?

Michael: Yeah, that is not good. Your DPM — your defects per million — is 571,000. Basically half the test they are running are out of specification.

Mark: Yeah, you got 57 percent defect rate. And you can look at the other stats there, but that is the one I concentrate on; is 57 percent of the area under the curve is red. That is not good. They are not going to solve anything if this any of their blood tests. If this is drug testing, they may be vying for the major league baseball contract. Do you think they will be able to compete? Forget it. They may not be able to complete on this drug-testing contract.

So, they get down into the business and are saying, “Okay, what are our factors?” Now, if you were a subject matter expert that is where we need you. We need you to understand what are the factors that could impact that signal. Well, they came up with seven. Substrate Type, PH, range, and concentration. You can see them there on the left. Those are the factors. They wanted to do a screening design. Now, when you do seven factors, unlike the three we did in the sales example or the director of sales did, 23 is eight possible combinations. If you did that for seven, 27 would be 128 possible combinations. Way too expensive. Takes too long, so what we are going to do — a general rule of thumb is, is that if you have six or more factors, you probably want to screen first before you model.

So, screening is the first thing we will do here, and we can do that with a very simple twelve-run design, which is called an L12 Design. It happens to be a Taguchi 12-Run Design, but it does not matter. Let’s see where is our 12-Run Screen Design. I am going to, Mike, eliminate putting in the data. I am going to tell you the data that they already have.

Michael: Okay.

Mark: Here is the 12-Run Design, which is an excellent design for testing up to eleven. You have the ability to test up to eleven factors in a 12-Run Design. There are really eleven columns, but we are only showing seven because they only wanted to test seven factors. So they are doing twelve test cases. Each of those twelve test cases looks like this…

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