 Hey guys, it's a room. Hope you found Biostats made easy useful and enjoyable in relation to sensitivity specificity positive predictive value negative predictive value and prevalence. Let's get straight to the next part So then the question comes up as to What is prevalence? All right, so prevalence is Going to be who actually has the disease so here that was an 80 plus 20 right the people that actually have the disease the Reality the positive that people and actually positively we in reality have the disease your prevalence is your true positive or false negative All right, so that's the people that actually have the disease divided by Everything everybody in the population so again your true positive plus false negative who actually have the disease and your false Positive plus true negative who don't have the disease so you put everybody in the denominator And therefore your 20 plus 80 the people that actually have the disease 100 divided by everybody 20 plus 80 hundred and then another hundred here 90 plus 10 is 200 So your prevalence is 0.5. So now we said our prevalence is 0.5 That was that 100 over 200 equation that we just did all right But how does prevalence relate to your positive predictive value and Your negative predictive value. Okay, so that's well. Let's find that out if that will Definitely be asked all right. So let's say that you your prevalence was the amount of people that actually had The disease right your numerator. So let's say your prevalence increases So if your prevalence increases, what does it do to your positive predictive value and if your prevalence? Decreases what does it do to your negative predictive value? Well, how do you increase your prevalence? We just talked about a prevalence was true positive plus false negative It's this column here. So let's say your prevalence goes up by plus 10 and plus 10 here All right, so then your prevalence so as it relates to positive predictive value when we found another positive predictive value here It was 20 divided by 30 which was point six six, but let's say that you add 10 Here right so now that you've added 10 because you're increasing your prevalence. You're adding 10 What does this become well then this becomes instead 20 over 30 this now all of a sudden becomes 30 over 40 Which gives you 0.75 so as your prevalence increases your positive predictive value also increases Okay, but what happens when your prevalence decreases quick summary as We increased prevalence what happened to our positive predictive value it went up, okay But what if we decrease prevalence? What will happen then? Well, how do you decrease prevalence? That means your prevalence is all of your truth all of the reality all the people that actually have it right So let's say last time we did plus 10 plus 10. Let's say this time we do minus 10 and minus 10 All right, so what happens to your negative predictive value for example, for example if you increase prevalence You get an increase in positive predictive value. What happens if you decrease prevalence? What happens to your negative predictive value? Well, you already know that a Negative predictive value is 90 right your true negative always goes on top 90 divided by 90 plus 80, which was 170 So 90 over 170 was 0.53 Right, but what happens if you subtract 10 right so then your true negative remains the same so it's still 90 but now it's 90 over minus 10 which becomes this becomes 70 so it's 90 over 160 and Obviously, that's I think it's about point five six all right, so that's point five six So if you decrease your prevalence you actually Increase your negative predictive value Based off this you know any combination of what they're going to ask you in regards to Prevalence and positive predictive value or a decrease in prevalence and negative predictive value All right, so that gives you the summary of what we discussed in regards to In regards to sensitivity your specificity your positive predictive value your negative predictive value your prevalence Your true positive your false positive, which also you can have your what error you can have your why it's on the top It's above so type one error or your alpha error or Down here, which is your you know you can go ahead and call it your type two error or beta error We'll talk about that more in another video and then there's your true negative and that is your summary So that's your summary so next time when you hear words like sensitivity or specificity again positive or predictive value Negative predictive value, whatever they need to make sense to you They need to have a meaning to you so when you think sensitivity next time You know exactly where it goes on your two by two when you think of specificity You know exactly where it goes as far as calculating the people that actually don't have it When you think of positive predictive value it needs to mean something to you need to know hey positive predictive value I'm trying to see how accurate my test is when it tests positive or negative predictive value How accurate my test is when it tests negative these words have to mean something to you rather than just simply words So that's what we have for you guys if you want personalized tutoring hit us up where you can go over these concepts the actual questions and You know be sure to like and subscribe and comment on the sections If you have any questions at all, we're here to help you in every way and I will put the promo code up for you Thank you so much for watching our content. 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