00:01
Which of the following statements concerning
positive predictive value (PPV) is correct?
Answer choice (A) - positive predictive value is
the proportion of tests that are true positives;
...if disease prevalence is low, then
positive predictive value will be low.
00:20
Answer choice (B) - positive predictive value is
the proportion of tests that are true positives;
...if disease prevalence is high, then
positive predictive value will be low.
00:32
Answer choice (C) - positive predictive value is
the proportion of tests that are true positives;
...disease prevalence has no effect
on positive predictive value.
00:43
Answer choice (D) - positive predictive value is
the proportion of tests that are false positives;
...if disease prevalence is low, then
positive predictive value will be low.
00:54
Answer choice (E) - positive predictive value is
the proportion of tests that are false positives;
...if disease prevalence is high, then positive predictive value will be low.
01:05
Now take a moment to answer this question for yourself.
01:13
Okay. Let's take a look at this question,
dissect it and then walk through it together.
01:18
So this question falls under the category
of epidemiology. We're doing biostatistics.
01:23
Now this question is actually a 2-step question.
01:27
At first glance you may think woah, this is a 1-step
question, they're asking about positive predictive value.
01:33
Well, they're not simply asking
about positive predictive value.
01:37
In fact, what they're really asking here is, 'what is the
relationship between positive predictive value and prevalence?'
So it's a 2-step question and of course the stem is required
because we have to know what they're even asking us.
01:51
So let's walk through this question.
01:53
Now step one, we have to know the
formula for positive predictive value.
01:59
So what is positive predictive value?
It is the proportion of positive tests that are truly positive of
some type of diagnostic test or other statistical measure.
02:10
Now that's the way we think about it, so it's the
proportion of positive test that are truly positive,
and hence the name - positive predictive value.
02:19
The people naming this are pretty smart.
02:21
Now what's the formula for positive predictive value?
Now, it's the number of true positives divided by all of the
positives, which are the true positives plus the false positives.
02:33
So you gotta know that equation.
02:36
Now I just tell you as a clinical pearl.
02:38
FOR USMLE step 1, step 2 and step 3
- you have to know these equations.
02:44
Biostatistics does not go away, it's always high-yield.
02:48
So memorize it now to make it easier for yourself
for this current exam and for future USMLE exams.
02:55
Now, let's look at step 2 of this question.
02:58
We need to determine the influence of
prevalence on positive predictive value.
03:04
Now prevalence is the proportion of a
population found to be affected by a condition.
03:10
That is, the number of people with the
condition divided by the total number at risk.
03:15
Now, let's look at our image here that'll
actually help us understand the relationship
between positive predictive value
and how it changes with prevalence.
03:26
Now we can see on the y-axis we have predictive value,
and on the x-axis we have the population prevalence.
03:33
Now if you look at that nice kind of light pink
color, that's the line for positive predictive value.
03:40
And you can see, as the positive predictive value gets higher, it
clearly is in a same relationship with population prevalence.
03:51
That is, as the positive predictive value
increases, the prevalence also increases.
03:56
But that makes a lot of sense.
03:58
If there is more of a certain condition, say in a
population, the predictive value of that is gonna go up.
04:05
For example, let's look around that
50% mark of population prevalence.
04:11
The predictive value seems to
be a little bit above 80%.
04:15
So if half the population has a certain condition,
we should have a pretty high predictive value.
04:20
And if we look at a hundred percent,
for a 100% population prevalence,
of course, we're gonna have a near-hundred
percent predictive value; Everyone has it.
04:28
So that makes a lot of sense and if
you look back to the answer choices,
when they say disease prevalence being low and positive
predictive value being low, that makes sense as well.
04:39
because if the prevalence is low, the chances
of us predicting it's going to be low.
04:44
And if the prevalence of a condition is high, the chance of
us predicting that prevalence as well is also going to be high.
04:52
So the graph here really does illustrate it well that positive
predictive value will increase as the prevalence increases.
05:00
Reallly well demonstrated here in this image.
05:03
So let's go back to thinking about step 2.
05:05
So what we can see then is that when the prevalence is high, the number of true positives will increase
and the positive predictive value will be high.
05:16
That's more of the equation way of thinking
about what we're seeing in our graph
And the same, in the opposite scenario, when
the prevalence of something is very low,
the number of true positives will decrease and
the positive predictive value will be low.
05:30
Thus, we can see that the correct
answer here is answer choice (A) -
Positive pedictive value is the proportion of tests that are true positives; If disease prevalence is low, then positive predictive value will be low.
05:45
Now let's discuss some high yield
facts concerning this question.
05:50
Now, positive predictive value and negative
predictive value - topics we also need to discuss.
05:56
Now we said, positive predictive value is the proportion of true positives of some type of diagnostic tests or other statistical measure
and it is really a reflection of how likely a positive
test result is going to be a true positive finding.
06:13
Now, what's the equation for positive predictive value?
You have to memorize this.
06:19
It's the true positives divided by all the positives
which is true positives plus false positives.
06:26
Now what about negative predictive value?
We talked about positive predictive value.
06:30
This is something you also have to
know- it's extremely important.
06:33
Now negative predictive value is a proportion
of negative tests that are truly negative
and it is a reflection of how likely a
negative test result is to be a true finding.
06:44
Now the beauty of negative predictive value and positive
predictive value is that they're really quite the opposite.
06:51
Now the equation for negative predictive value is true negative divided
by all your negatives which is true negative plus false negative.
06:59
Really if you look at negative predictive value and
positive predictive value, they're just opposites.
07:05
one is all true positives, one is all
true negatives - very easy to memorize.
07:10
Now let's talk about prevalence.
07:11
Now prevalence is a proportion of a population
found to be affected by a condition
at a specific time point or
during a given time period.
07:21
Now prevalence is calculated by the total number of people with
the condition divided by the total number of people at risk.
07:29
And this is generally expressed as
some type of fraction or percentage.
07:34
Now, it's really really important that you
don't confuse prevalence with incidence
which is the number of NEW cases per
population at risk given a time period.
07:45
So prevalence is, well 'what do we have during a time period' and
incidence is 'what is the number of NEW cases' - so very different.
07:54
Now, both predictive values being positive predictive value
or negative predictive value are influenced by prevalence.
08:03
Now, what we learned from this question is - as positive
predictive value increases as the prevalence increases.
08:12
That means that there are more true
positives, then you'll have a higher prevalence.
08:16
Now let's think about negative predictive value.
08:19
As negative predictive value decreases,
prevalence will then increase.
08:24
So negative predictive value
decreases as prevalence increases,
and that's because there are fewer true
negative tests with a higher prevalence.
08:34
Both really important high-yield
concepts to understand
Now, let's look at the rest of our chart here.
08:42
If you go back to the image, we see that we
have negative predictive value shown in green.
08:47
And that highlights our second point we just discussed that
negative predictive value decreases as prevalence increases.
08:53
You can see, at a zero percent prevalence, our
negative predictive value is at a hundred percent.
08:59
And as we increase in prevalence, our
negative predicitive value will decrease.