We want medical tests to give us a yes or no answer: you have the disease, you're cured. We treat them this way, often. My labs came back saying I'm healthy. I have immunity. I'm sick. The reality is more complicated, and tests do not give you a yes or GlucoLife Blood Sugar no. They give you a likelihood. And most of the time, what the results mean for me, the test taker, is not immediately obvious or intuitive. They can mean something quite the opposite of what they seem. I ran into this recently on a page about celiac disease. The Celiac Disease Foundation has a page about testing for celiac disease. On this page, they give a lot of useful information about what different tests are available, and they point to some other good resources as well. The tTG-IgA test will be positive in about 93% of patients with celiac disease who are on a gluten-containing diet. This refers to the test's sensitivity, which measures how correctly it identifies those with the disease.
The same test will come back negative in about 96% of healthy people without celiac disease. This is the test's specificity. This is great information, and it tells you what you need to start figuring out what your chance of celiac disease is. There is also a slight risk of a false positive test result, especially for people with associated autoimmune disorders like type 1 diabetes, autoimmune liver disease, Hashimoto's thyroiditis, psoriatic or rheumatoid arthritis, and heart failure, who do not have celiac disease. And this is where things are a little misleading. It says that there is a "slight risk" of a false positive test result. What do you think of as a slight risk? For me, it's maybe somewhere around 5%, maybe 10%. The truth is, the risk of a false positive is much higher (under many circumstances). When I take a test, I want to know a couple of things.
If I get a positive test result, how likely is it that I have the disease? If I get a negative test result, how likely is it that I do not have the disease? The rates of positive and negative results listed above, the sensitivity and specificity, do not tell us these directly. However, they let us to calculate this with a little more information. P(A) / P(B). You can read P(A|B) as "the probability of A conditioned on B", or the chance that A happens if we know that B happens. What this formula lets us do is figure out one conditional probability we don't yet know in terms of other ones that we do know. In our case, we would say that A is having celiac disease, and B is getting a positive test result. This leaves P(A|B) as the chance that if you get a positive test result, that you do have celiac disease, which is exactly what we want to know.
To compute this, we need a few more pieces of information. P(A), the probability in general that the person taking the test has celiac disease. This is also called the prior probability, as it's what we would say the probability is if we did not know anything from this computation and test. P(B), the probability that for GlucoLife metabolic support any given test taken, it comes back positive. P(B|A), GlucoLife Daily Support the probability that if one has celiac disease, the test will come back positive. We already know that P(B|A) is 0.93, as we were told this above. And we can find P(A) pretty easily. Let's say P(A) is 0.01, since about 1 in 100 people in the US have celiac disease. Estimates vary from 1 in 200 to 1 in 50, but this will do fine. That leaves us with P(B). We have to compute it from both possibilities. If someone who has celiac disease takes the test, they have a 93% chance of it coming back positive, but they're only 1% of the population.