Alright guys, so let's try to get started. We've got about 16 of these questions. These are the ones you're most commonly going to see.
So let's get started. And again, when you study this, just kind of go through the questions in your head. Kind of watch this. No sense in working them out over and over and over.
So just kind of watch these. First one says, a new test to diagnose UTIs in women is being assessed. The comparison gold standard is positive urine dipstick plus urine culture.
Results are given. So they're going to give you a chart like this, 2x2 table. They're going to ask you what is the test?
Sensitivity, specificity, positive predictive value, or negative predictive value. Remember how it goes. Now here they're asking for specificity. Let's just go back to what we remember.
Sensitivity, you circle here, go down. Specificity here, go up. Positive predictive value goes this way.
Negative predictive value goes that way. So you should have all the formulas just based on this. Now with specificity, it's here. You've recircled here and went up. So on the top, it goes 180. On the bottom...
180 plus 20. When we connected it, you take both numbers. So the specificity in that case is 180 over 200. You cancel out, you get 9 over 10, and it's 90%. If they said sensitivity, we would have said we went here and go down. So it's 40 over 40 plus 160. 40 over 200, we start canceling, and then we're looking at about 20%. So in this case, specificity.
It was 90, answer choice E. The next one says, A five-year study is planned to assess the incidence and etiology of respiratory disease in 600 individuals greater than 50 years of age. The study consists of two groups.
One cares for a pet dog, and the second does not care for any animals in the household. At the onset of respiratory symptoms, cultures and serological studies will be performed, which the following best describes the study. So when we see a question like this, basically they're going to ask, they're going to try to see if you know the difference between case control and cohort. And remember what we said. Case control has an A and an O.
So an A and an O. Two different things. It's the most meaning like one person has a disease and one does not. The other one is a cohort.
O and an O. So basically both sets of people start out without the disease. So when you look at this problem, they talk about these people that are 50 years old and then they're like, well, we're just looking at the ones who care for a dog and the ones who do not have animals. So at the onset of this study, both... The 50-year-old did not have the disease.
They're actually looking for it to happen in the future, given these circumstances. So in this situation, they're actually looking for a cohort study. Remember, O and O, nobody has the disease initially. Now, since it's like relatives, right? Relatives look the same.
So when you see, again, so just remember, when you see cohort, you say relative risk. When you say case control, you need to say odds ratio. You just got to have that memorized, okay?
They look the same. They're relatives, relative risk. Nobody has disease initially.
It can go ahead and look forward and backwards. Now, when someone has a disease and someone does not, you think odds ratio, case control, and really you only want to look backwards because we don't care about going forward on that one anymore. It says a new study to diagnose prostate cancer is being evaluated.
The sensitivity of the test is 70% and the specificity is 90. In this study, there are 100 patients who truly do not have a disease. truly have UTIs and 200 truly do not? How many false negatives are there in the study? So when you get this, I didn't give you the chart initially, so you've got to create it. So if you ever get stuck, you say, what do I know?
Well, I can always create a chart. And I know up on top, we always say reality goes there. And we'll just say the test goes over here, positive, negative, positive, negative.
Got to be able to label this. Now, positive, if the test is positive and the reality is positive, it's a true positive. Test is negative, reality, it was negative, it's a true negative.
So, and everything is based on the test. So, if the test was negative and in reality it was positive, that's a false negative based on the test. The test was positive, but in reality it was negative. That's a false positive. Now, what does this question say?
It says the sensitivity of the test is 70%. So, we know the sensitivity was this going down. So, we know here this is going to equal 70% somehow.
Specificity is here going up, so we know that's got to equal 90%. Okay. So in the study, there's 100 patients who truly have UTIs. So in reality, 100 patients actually had UTIs.
So this category here is going to equal 100 patients. And 200 who truly do not. So in reality, there's 200 people who do not have UTIs.
So this little category goes here. So it says how many false negatives are in this study. So that'd be that number.
So I know that something over the total of 100, something over the total of 100 equals... 70 percent. Okay, so what would that number be?
70, correct? So if we have 70 over 100, That gives us 70%, but that's not what they asked. They asked how many false negatives.
So 70 plus the 30 equal 100. So my answer on this one, how many false negatives? This right here, answer choice C, number 30. Now, if they would have asked false positives, I would have said, oh, well, I know that 90% of 200 is 180, and 180 plus 20 equals a 200. So if they would have asked me how many false positives, I could have just said 20, okay? But you always go back.
Once you write this table out, I don't care what they give you, you just work backwards and you can solve this. Now this one basically says, what type of study is being conducted? And so again, we always look back between cohort and case control for the most part. Everything else is pretty self-explanatory. So it says, a test is being conducted to determine if older students have a lower score on USMLE Step 1. A group of students older...
a group of older students greater than 50 who took step one are compared to a group of younger students who took step one data shown below so here's a two by two table and basically they're saying that you know there should be a better a better example here meaning more of a disease but here they're saying just a lower test score versus a normal test score older older person versus a younger person so basically we already know that we already pretty much know the outcome in this so if we already know the outcome Someone, like say, for example, has disease and someone does not, then that's what we called a case control. Now, if we didn't already know the outcome in this, not in this situation, but if we didn't know the outcome, we can go forward looking or backwards, and that is considered a cohort study. But in this situation, we already know the outcome.
So basically, it's equivalent to saying someone has disease and someone does not. So that one would be a case control. Now, this is the same question, but now the question is, what is the odds ratio? Okay.
And again, we knew that because it was said case control would be, we always think odds ratio. You got to have that. You may see a question that just say odds ratio, and then you got to go back and say, oh, that's case control.
Now, when we say odds ratio, we got to know this. Odds ratio versus relative risk. Think odds. When you're in Vegas, you think odds.
So it's one number over one number. When you say relative risk, you've got to think one number over two. You've got to keep that in mind.
Odds. You're in Vegas. Three to one odds.
Two to one odds. One number over one number. Relative risk.
One number over two. You do that, you get it right. Now, when we read math problems, we read them top to bottom, left to right.
So you've got to read this properly. It says, what is the odds ratio, relative to that of the younger student, of older students having lower test scores? So it's very important to understand they're asking... and older students compared to the younger.
So who's gonna go on top? You know, who's gonna be my thing on top? And that's gonna be the older students. So let's see, older students and it's odds ratio, it's one number over one number. over 200 over the younger student, 40 over 160. Okay, and that's gonna answer choice C.
Now if this thing was different and it basically had a relative risk, okay, if it said relative risk, it'd look something like this. It'd be one number over two, 60 over 60 plus 200 over 40. Over 40 plus 160. Very simple differential between those two. Just remember, odds ratio, one number over one number.
If it said relative risk, one number over two. Okay, you do that, you get it right. Alright, the next one. It says a biomarker is being used for detection of a certain disease.
500 healthy volunteers and 120 patients with a biomarker are used in the figure below. So changing the cutoff value of the biomarker from point B to point A would most likely result So again, they're going to give you something like this, some type of chart. Are you going to move the marker from A to B or B to A?
Or they're just going to not give you this and say, well, I'm moving the cutoff from 200 down to 100. Or they can say moving up from 100 to 200. So the bottom line is are you going to move it left from right to left or left to right? Now you have to label this properly. And we said over to the left, true negative.
Over to the right, true positive. Everybody keeps their last name. So on this side right here, if you keep your last name, that's going to be a false negative.
So we're going to do that. And right here, if you keep your last name, right there it's going to be false positive. Now all we ever care about in these situations are the middle numbers. I don't care about him, and I don't care about him because he will not change.
For our purposes, he will not change. So it says change and then cut off from B to A. So if I'm moving this thing from B to A, and remember, always kind of think you're going to adjust this bottom line.
So if I move from B to A, I'm actually going to smash that false negative. So false negative goes down. And if I move it from left to right, from B to A, technically you think this middle line goes this way and my false positive goes up.
Now if it went from low to high, the opposite occurs. I'm smashing the false positive and increasing the false negative. So they're only looking about, well, let's do this. For lower sensitivity, well, what's my thing for sensitivity?
I'd go back and write my chart of what I know. Reality, test, positive, negative, positive, negative. That's a true.
true positive. Negative negative is a true negative. Everything's based on the test, so that's a false negative.
Everything's based on the test, so that's a false positive. So sensitivity, okay? We said sensitivity, this going down, so that's a true positive over true positive plus false negative, okay?
So it would be lower sensitivity. So here, the only thing that relates to this, that's all I care about in this, would be the false negative. Now, false negative goes down, so if that number goes down... The whole sensitivity actually would go up.
Okay? So it's not looking like it's A. More true negatives?
Well, wait a second. True negatives were out here. Again, we do not care about the true negatives or true positives in this scenario. So those things do not change for us. More false negatives.
Well, we just talked about this. If it goes from right to left, it's... smashes the false negative.
So that's actually gonna be incorrect. Higher negative predictive value. Now again, we went back to that thing and we said sensitivity goes this way, specificity that way, positive predictive value, negative predictive value.
So the formula for negative predictive value is true negative over true negative plus false negative. So the false negatives in this area went down, so if that goes down, my negative predictive value actually would go up. So that could be definitely an answer choice, so I'm gonna put a little mark next to that, or higher positive predictive value. Positive predictive value was this guy going that way.
So the formula is true positive over true positive plus false positives. That's positive predictive value. Now, the only thing I care about is false positive, and he actually went up.
So if the false positive goes up, the whole number out here actually would go down. So it's not that guy. So the answer in this scenario is higher negative predictive value.
Great question. Remember, all you care about is the middle. You've got to label them correctly. Everybody keeps their last name.
That's how you're going to know which side to put them on. You're either going to slide them to the left or slide them to the right. You're going to smash this guy or smash that guy.
And if you smash... the left, the right goes up. You smash the right, the left goes up.
That's all you care about right there. Okay? The following graph shows distribution values from a healthy group of volunteer and diseased people. Points A through E represent various points for determining distinctions between these people. What cutoff point would determine a sensitivity of 100?
Okay? Well... Well... What was our formula for sensitivity? Again, go back to our chart.
Reality, test, positive, negative, positive, negative. True, positive. True, negative.
Everything's based on the test. That's a false negative. This one's a false positive.
So sensitivity is this guy. So my formula is true positive over true positive plus false negative. Remember, the bottom's two things.
Whatever thing I connect the line, you write both of them down there for the bottom. All right. So to make this 100%, that means I've got to get this guy actually.
he's gonna become basically a zero. So if I notice he's a zero, my sensitivity will be 100%. So where in this chart would I find the false negatives being zero?
Well, let's just label the chart. We know that's a true negative, this is a true positive. And then all I care about is in the center. I gotta label them correctly. That's a false negative and this is a false positive.
So where do I make him zero? Where can I smash this guy and make the false negative a zero? I slid him all the way right here. Point C. Now if they said what is the specificity, well I would say oh that's specificity. True negative over true negative plus false positive.
Where can I make the false positive zero? Where can I make him zero? Oh if I smashed him this way and then so the specificity would be choice E.
Alright so what will happen to the sensitivity and specificity of a test when the markers are moved from the blue curve to the red curve? So from the blue to the red or pink, whatever you see here. And again, you see this scenario, all I care about is the inside.
I don't care anything about this. True negative, true positive. So here, I'm gonna draw my line. So on this side, it was actually the false negative.
And on this side, it was actually the false positive. But going from the blue to the pink, the area under this actually went down. It went this way. So it's going from here smaller.
So the false negatives. Got smaller. It went down and in. The false positives.
Got s... Smaller. So now all I care about, I've got to plug this back into my formulas. Okay? And so all I'm looking at is sensitivity and specificity.
Back to what I know. Positive, negative. Positive, negative. Reality.
Test. True, positive. true negative, everything's based on the test.
So it's a false negative. This one is a false positive. So sensitivity goes this way, specificity goes that way.
So sensitivity, positive plus false negative. and specificity is true negative, right here going up, true negative plus false positive. So, on both these scenarios, in both these situations, the false negative and false positive went down, so the false negative goes down, so if he goes down, this whole number gets...
bigger. So sensitivity goes up. False positive, he went down. So if that number goes down, the whole number goes up. So in this situation, when we went from this, the blue to the pink, the area under the curve got smaller for both of those guys.
Smaller. And if both those get smaller, It's a higher sensitivity and a higher specificity. All go back to our formulas.
You've got to know this, okay? But all we care about in this situation is the center. I don't care about him.
I don't care about him. Checking blood pressure at a health fair would be an example of what type of prevention. Okay, this can come from... up pretty easily.
Just make sure you know these first two. Primary prevention is something like immunizations and then secondary prevention is something like when you're trying to screen for this stuff such as checking blood pressure would be a scenario where you use secondary prevention. So kind of know these. Just kind of read through them if you get a chance.
Primary again, education, immunization before the thing happens. Screening for disease early to reduce the impact. Those are your two main ones. Basically, make sure you know those. Okay?
And let me see here. We talked about the case fatality. Again, when I say case rate, case rate, all I care about is the case they're talking about.
So it says the table shows distribution of spinal cord injuries and death. What is the case fatality for falls? Okay? So case fatality for falls.
So I don't care about anything else except falls. So case. Fatality. Here's the number of fatal ones. Here's the number total.
Case fatality. Falls. How many falls total? 20. How many of those were actually fatal?
Case fatality falls four out of 20. A new instrument is purchased by the hospital to check serum levels of some type of X. The published value for the standard is 40. The technologist runs a test on patients and gets readings of 70, 68, 70, 70, 75, respectively. What can be concluded about this instrument?
All right, so the average, they're asking about two things. They're asking about accuracy and precision. Now, you know, if this was a bullseye, You know, accuracy, if they're going to give you something that says to determine accuracy, they've got to give you a gold standard. So with accuracy, you've got to have some type of marker or gold standard, some type of reference to know if you're going to hit the bullseye.
a bullseye, the gold standard is going to be the center of the target. So if you hit around this, you know, obviously, if you're getting close to the mark, you're accurate. Okay?
As long as you're close to the mark, you're accurate. Now with precision, you know, you're just in the same area. Okay, that's all. You just want to make sure you're pretty tight with that.
Okay, so with accuracy, you've got to have some type of gold standard to measure yourself by. With precision, you know, you're just going to be in the same area. So in a situation like this where the standard is 40, yet this guy's hitting these readings of 72, 75, so he's pretty tight in this region. So this guy's pretty precise, but when you reference him next to what the gold standard is, he's not very... accurate.
So in this situation, he's precise but not accurate. Answer choice B. All right, new studies showed that the mean HDL level of a non-diabetic patient was 42 and that the mean HDL level in a diabetic was 35. Probably this was due to chance. was.05. There's also a 15% probability that there is no difference in the HDL measurement when the reality was 1. So first thing, what is the p-value of the study?
What's p-value mean? Basically, you've got to think of p-value as what is the chance of it happening by chance. And in this situation, they actually gave it to you. The probability that this was due to chance was.05 or change to 1,000. 5% and remember for a study to be actually a good study it's got to be either point or less.
Okay so 0.04 is that a good study? Yes because it's less than 0.05. Is 0.06 a good study? We're going to say no because the rule says it's got to be 0.05 or less. Okay or less not higher.
What is the power of the study? Well this gets back to the whole null hypothesis stuff. So when we draw out our null hypothesis, you know, we still stick with our reality over here. I always like to do just the test, stick with test, but it's whatever situation it is. Okay.
Now, we've got to label it correctly. One and an O. One.
and an O. And it's O is the null. Okay, that's the null hypothesis. So null meaning there is no association. Okay.
So let's fill out our chart of what we know. If the test says there is an association, but in reality there is no association, that's called an alpha error. Okay?
Alpha error type 1. Again, the test says there is an association, but in reality there was not one. That's an alpha error. Now, if the test or situation says there is no association, but in reality there was one, That's called a beta, okay?
Beta, or type 2 error, okay? And it's all based on words. You've got to know how to first write this out, 1 and 0, 1 and 0, reality, and the test.
And then you've got to put this into words. Fancy on, you've got to know this. words.
So again, if the test says there was no association, but in reality there was one, beta error. Okay, beta error. If there is an association, reality there was not, alpha error.
Now, how do I find this one right here where it says, well, yeah, there is an association, reality there is one. one? How do I find that?
It's one minus beta, also known as power. If you can write out this, if you can draw out this, you can answer any question they're going to ask you pretty much on step one when it comes to the null hypothesis, but you got to know what it means in words. Okay. So it says, what is the power of the study?
So we want to know what this box is right here. So how do I find that? Well, Let's see what they gave me. They said that there is also a 15% probability of concluding that there is no difference in the H-T-E-L measurement when in reality there is one. Again, 50% probability of concluding that there is no difference.
We're going to try to conclude... In the issue of measurement, when there is one in reality, so they're saying there is no difference when in reality there was one, there's a 15% probability of that happening. Well, what do I got to do? I take one, that was beta, so the 0.15. So one minus the 0.15 is going to be 0.85, and that's actually going to be my...
Power, okay? Answer choice C. But you've got to be able to write this box out, okay? And then this is the most common question, just like we did it right here.
This is the most common question they're going to ask that you can understand that. This in words actually means the beta box. Okay? And you say 1 minus that, that's your answer.
Okay? Now, the prevalence of prostate cancer compared in two groups of men and the following data was obtained. Based on this data, what is the relative risk?
Okay, bottom line, relative risk. And when we read math problems, we read them top to bottom, left to right. I did a lot of prostate cancer in men who had no children compared to men who had children. So we've got to compare men who had no children to the ones who did.
So who's going to go on top? The men who had no children. Now, it's relative risk. Now, when we said odds ratio, we go one number over one number. If I said relative risk, it's one number over.
two. Okay, so relative risk of no children compared to men who had it. So no children, so that was going to be 80 over 80 plus 920 because it's one number over two because it's relative risk compared to the men who had children.
220 over 220 plus 1280. Okay. Now, again, if I said odds ratio, I would have just went 80. And odds ratio in this scenario, I would have went 80 over. 920 over 220 over 1280. Okay? But since it's relative risk, one number over two. Over one number over two.
Gotta know it. Alright? So then if we kind of whittled that down...
220 over 1500. I mean we can kind of do the math here. Hopefully they'll give you a lot easier scenario. 220. You know you can cancel out a lot of stuff.
So if we did it by 500 that's 3. 2. Again if I did it by 10. 24, 44, we're almost looking, you know, we're looking close to about 50, roughly 55%. Alright, very good. Scatter diagram shows a correlation between alcohol consumption and test scores. Alright, so you're going to get a scenario like this, and either the scenario is going to be going like this way, and you just got to look at the dots and draw whatever line you think would most match where this thing's going.
Okay, so in this situation, it's that way. If it was like this, it would be that way. Now, if it's going in this way, it's like as you drink more alcohol, Okay, if you drink more alcohol your test score is going to go down Okay, so that's called.
That's so this is going to be a negative association Okay, I'm just using negative one because it's basically it's basically saying that if I go over one I can also go up one. It's a one-to-one ratio the line looks like it's one to one I go over one up one. It's equal to negative one in a situation like this the more I study I said the more I study the higher my test score again over 1, up 1, or up 1, over 1. It's a 1 to 1 ratio. And then you can kind of go from there. Now, in this situation, for this answer, it would have been negative 1. But you've got to be able to understand if it was negative 0.2, the slope would be a little bit bigger.
If it was a positive 0.2, the slope would be a little bit flatter. So for right now, just understand negative association, positive association. and then understand the slope's going to be a little bit less if it's one of these numbers.
All right, last one. It says a study of 200 patients and hospitalized patients, blah, blah, blah, with complications related to pneumonia showed their serum cholesterol level is normally distributed variable with a mean of 210, standard deviation of 15. Based on the study, how many patients would you expect to have cholesterol greater than 240? All right, so it's just standard deviation stuff.
So when in doubt, just go ahead. to kind of write out what you know. So there's a standard deviation curve. It says there's 200 people in the study.
Pneumonia is a cerebral cholesterol disorder. They mean a 210. So we know the mean is 210. Standard deviation is 15. Now, we know one standard deviation is 68%. We know two standard deviations is 95%.
What, three standard deviations, what is it, 99.7%. Alright, so you basically have to have this memorized. Now they love this one that's 95%.
Just because it's a nice number. They know you can use it, the 95 a lot easier than you can use 68. But you've got to know this, 1 is 68, 2 is 95. Now, an extended deviation of 15. So basically, what they're saying is, if they come out 15 points, 15 points going down is going to be 195, and that 15 points going up is going to be 225. And basically that's saying between 195 and 225, there should be, that's one standard deviation, there's 68% of the people should be in this, in between here. Now the question on this one says, based on the study, how many patients would you expect to have cholesterol greater than 240? Okay, so somehow out here I've got to get to 240. Okay, well I can see what they're doing here.
So if one standard deviation was adding 15, so another standard deviation, another deviation out, would be adding 15. So they're basically looking at two standard deviations. So I'm adding 15, and that takes me to 240. I could minus 15, and that's going to take me to 180. So what that's saying is, now in here, between all this, I should have 95% of the people should be within this range. So again, back to the question, it says, how many patients would you expect to have cholesterol greater than 240? So really, all we're looking at in this whole problem is the number of people that are out here going this way. So let's look at what we know.
We know 95, there's 200 people in here. 95% are contained in here. So if I had 200, and if I did 95, okay.
So I know 90% is 180, 200, so that should be about 100. 190. So, 190 people are in the center here. So, that leaves how many? That leaves 10 that aren't.
So, 10 should make up the outer edges. But again, they would try to trick you on this, and they want everybody to know. bite on this 10 but that's not the answer because the question said how many patients would you expect to have cholesterol greater than 240 so if there's 10 left I got to put 5 on this side 5 on that side so the answer was greater than 240 would be 5 not the 10 so again when you study this you know obviously you just want to go through these just real fast and know the concept this is pretty much the the meat potatoes of what you'll see on step 1 just kind of go through these and I think you'll do well well on the test So hope this helped, guys.
We'll see you later.