USMLE STEP 1: KAPLAN-MEIER CURVE w/ Questions

all right guys so in this video we're going to talk about that kaplan meyer curve uh this is something that that i've seen every now and then and you know essentially what i did is i i went to the usmle.org website and you know kind of pulled this the uh u.s assembly content outline and if you go on page 27 where it has the biostatistics epidemiology it literally tells you that the expectation is that you're going to uh you're held accountable for understanding the kaplan-meier curve so in this video we talk about the several topics that are associated with that i think we kind of hit the big stuff but i i will ask for your help on this if you come across any questions that are kind of on this kaplan-meier curve content if you wouldn't mind sending them to me we can kind of use those as a base to kind of build from and we can kind of take them and kind of twist them and kind of make them our own we just want to make sure we can see questions from different perspectives so if you find the video helpful uh don't forget to subscribe you know hit like all that kind of stuff it does it does matter um it's something that we we kind of track to make sure people are actually finding value in kind of in us doing this so um you know keep studying hard and hope you like the video all right guys so here we go question number one says based on the above kaplan-meier curve what can we understand about the median survival time of group one versus group two during the study well you know anytime again anytime you see like a stair-stepping graphs i want you to think this kaplan meyer curve okay now there's a couple principle takeaways from this so anytime you see this you got to think kaplan-meier curve now the purpose of this thing okay is basically it's a way to estimate survival okay it's a way to estimate the survival that's the whole purpose of this um the other thing that you need to understand is that when they made this kaplan-meier deal they needed a way to incorporate what they call and this is a keyword that you're going to see in this stuff is called censored data and what that means is like you know when they when they do a study and all of a sudden say say somebody drops out or someone doesn't reach doesn't have the the occurrence or the event during that time frame then they have to have somehow account for this censored data and that's what this kaplan-meyer thing does it puts it it kind of embeds it in there and i'll show you that just uh in just a second so number one you got to think staircep kaplan meyer curve it estimates survival okay that's the purpose of it it's going to have this stuff called censored data um in there and this sensor data just so you know that can affect okay that can affect uh the curve okay you know obvious right it maybe it won't be as steep so it's the way the curve looks you know we'll get that in just a second as well um okay so back to the question well uh again based on the above kaplan curve what can we understand about the median survival time of group one versus group two or it could be you know a treatment group versus a placebo group now they're gonna they're gonna identify this but long story short you got group one which is going to be this highlighted one right here on top and group two the people who are who are in this one okay and if you look on the y-axis it's survival probability so right here at 100 you know they have it in percent but they could just put it like say even decimals and stuff like that but survivability at 100 that means this when the study began everybody's alive right of course and then all of a sudden group group one went this way group two went this way and so 100 of the people were living here means 80 percent sixty forty and twenty percent and if you're down here that means well everybody's dead all right and then on the x axis it's time okay it's gonna be time and i just have it here in years so 5 10 15 20 25 30 and 35 years so this kaplan-meier curve is for this period of time you know say it was a one group received a treatment second group didn't and they followed them over a period of 35 years we're going to determine in this between the two the survival rate and then we get in this thing takes into consideration censored data all right so now here goes is it a group one has a median survival score half of the patients half of that of the patients treating group two so they're saying group one has a median survival median survival score half of this guy is it the median survival time of group 1 is approximately 15 years okay is it group 1 has overall reduced survival time compared to group 2 is it d the median survival time for group 2 is 8 years or is it e the median survival time for group two is half that of group one all right so a lot of a lot of stuff in here but the long story short it keeps asking about the median survival time remember what we did you know you remember there's media mean median and mode mean is just the average right the median like when you're driving down the road that's just the middle and then of course mode is most common okay well the only one we're dealing here is median so when it talks about the median survival well here's survival so what's the median what's the middle it's going to be well 50 all right it's the that'd be the median so if we were to draw that line all the way across you know it would cross group 2 right here and cross group 1 right there so in group 2 we would go down and so the median survival of group two is going to be you know let's just say looks around eight years in group one it's going to be the median survival is going to be let's just say roughly it looks like 22 23 somewhere in there okay so back towards crash group one has a median survival score half of the patients in group two so so they're saying group one is half of group two no you know it's not even close group one's median survival scores 22 years so if you were a patient in the group one and you took that medication or you did whatever that one was your median survival was going to be 22 years if you were in this guy and took that medication or whatnot your your median survival score is only eight years that's why you need to understand this whole kaplan meyer how to interpret it because you know this is how they do some uh you know when you read articles and such practice based uh care or evidence-based care i'm sorry uh so group one so it's not gonna be this guy because that's opposite almost um is it the median survival time of group 1 which is highlighted is approximately 15 years no 15 years would be right here and so that's definitely not the median okay is it the group one has overall reduced survival time compared to group two wait a second group one has an increased median survival compared to to group two so it's not reduced it would be actually increase so it's not him is it the median survival time of group two is eight years oh wait here's group two is eight well it kind of looks good i like it the media or the e the median survival of time for group two is half of group one now it's group two is reduced but it wouldn't be half it was happy to be more closer to ten or eleven so you know it's it's it's not correct not bad but it's not correct the best answer the only answer is going to be d the median survival group of group two is eight years so that's just how to interpret this this meyer kaplan-meier curve it looks like stair steps you're going to think kaplan-meier you're going to think it estimates survival so if you're in this group you know your immediate survival was here if you're in this group it means was there and then you have to talk about censored data here in just a second what that means is you know sensor data is essentially if someone you know if someone either just dropped out okay dropped out of the study or um they didn't have the event you know again like if we were trying to uh see if someone had a you know 10 people taking a drug had a myocardial infarction all of a sudden one person in this group had the myocardial infarction and so then then it goes down so now there's 90 percent left um and then another person another person but say somewhere in here somebody dropped out they had these things called these these little uh sensor data uh that would that you would have to incorporate and what that would do is it would make this stair step even longer and that's just a mathematical way so anytime you see a giant stair step compared relatively speaking you know you might consider that that is where sensor data occurred but for you right now just know that sensor data is part of this part of the scale okay so the next one it says so same graph it says based on the above kaplan-meyer curve which group had a better 10-year 10-year survival prognosis okay again group one group two they're asking at 10 years who had the better survival prognosis well let's just say here's 10 years and if we went all the way up we can see that at 10 years group 2 this guy now let's just say for simplicity it was that you had a 40 survival so if you took that drug or in that portion of the study at 10 years you had a 40 chance of surviving if you were in group 1 you're looking at eighty percent roughly okay so of course you'd like heck man i want to be in group one for this so is it group one has a ten year survival prognosis of forty percent uh nope not group one group one was eighty percent okay so we know it's not him uh is it group two has a ten year survival prognosis of sixteen um you know that didn't make sense right it's not 16 percent and and 16 you know that's that's kind of saying like here it has nothing to do with that's that 16 years the survival of group 2 would have been like say 10 but it's it's just to play on numbers so it's not that guy is it c group one has a ten year survival prognosis of eighty percent um group one ten years eighty percent survival looking good right there is it group two greater than group one no now because the survival is much less much less than this guy than it is in the group in this group and you can see one one way i've seen a guy talk about it is pretend if you were like on a on a roller coaster or something like that and the steeper the curve right you're more scared so the steeper the curve you're thinking okay that's more dangerous not it you know for simplicity sake and so if it was a smaller curve like okay this is an easier ride not as dangerous that's one way of looking at it or you just understand that this is time you go up you find out where at what year at five years you know the rate was 60 percent in in group two okay so the correct answer on that one gonna be c uh group one has a ten year survival prognosis eighty percent kaplan meyer okay and now the last one it says based on the above kaplan-meier curve which of the following time spans most likely had a censored patient now you know you to uh to really understand this how the stair-stepping occurs it's almost like this you know if you had n equals 10 in a study okay it's kind of totally separate right now and so right here you started with a hundred percent everybody's in the study everybody's alive and say you start and then here's time so as you start moving time all of a sudden somebody has an event say it's a myocardial infarction or something i don't know it doesn't matter someone has an event so now okay one person out of 10 had the event so how many how much is left okay ninety percent so i gotta take ten percent out right so this guy would go to ninety um and then there should be you know basically nine sections left so now i'm left with how many people in the study nine okay and then i start moving with those nine people and then all of a sudden something happens right there so two out of nine now that's a that's a percent now that's uh all right i forget how much the how much that is but then it goes down whatever two divided by nine is and then i should have nine sections okay and then all of a sudden say i have two p a couple people drop out okay let's just say they flat out go away they go away they put in these things called censored data so right here at the next event of someone who's still in the study how many people were actually left let's think about that right how many people were left one guy got out here because he had an event another person got out here so now i'm left with eight but then two people dropped out so now i'm left with six people so when this event happened it happened to one person there were six left and then all of a sudden that's like um sixteen percent so look this drop here was ten percent okay and then two out of nine somewhere here but now this is a pretty big you know instead of it being one out of eight because two people dropped out it's one out of six and so that's a lot bigger than it should be right so that drop is going to be slightly bigger per se okay it's going to be slightly bigger it's a little in general terms just think of it like that and that means that chances are somebody dropped out of the study right there and then you go on and and and then you know either either everybody dies or everybody has the event or or the study ends you know it ends at you know 10 years or something like that and then there's still some people that never had the event okay that's the difference that's the difference between something ending when it stair steps and ends like this or the study that ends like you know that that means everybody had the event this means some people didn't have the event and it ends just like that okay anyways the moral of me telling this is you see once i had it one out of six and not one out of eight there was a bigger drop right here so based on the above kaplan-meier curve which of the following time spans most likely had a censored patient okay is it a right here b c or d and you're thinking all right well uh let's just go ahead and eliminate something these look equal right like it went down by roughly the same even though we know it's going to be a little bit different there's no drastic changes on that so i'm eliminating him uh this you know just because a time is this long do i know somebody left because the drop is not that much right it's a little bit more don't get me wrong it's a little bit more than these guys but it's not overwhelming uh you know it's not a giant drop but when i look out at b or a i see okay there's a the drops are pretty pretty significant like look at the difference here to here okay and then here to here well we don't know how many people were in this study but it looks like it could just be a proportional one and nothing to compare to so the place where i think that perhaps there were some censored patients or patients who just dropped out of the study and then no longer in the denominator like they should have i would say it's going to be right to me it's going to be right here okay because let's just say from here to here it might have been out of you know again i'm just going to say one two three four you know five it could have been you know one person out of 15 but then all of a sudden we had two or three people drop out so the next one is going to be one out instead of being one out of 14 it's going to be say 1 out of 12 or 1 out of 10 and it's a bigger drop okay so that's kind of it's just a a way of doing it does it work every time i you know i don't know this stuff's you know if we can just understand that a censored patient is one part of the kaplan-meier because they wanted to create a curve to where they could incorporate when people dropped out can we still have it to where it we can still do some type of um chart to compare treatments of a treatment group a placebo group or two treatment groups to compare these even when patients drop out or or if they never have the event okay if they never have the event that's called a uh like a right uh a right censored or a censored patient okay so the correct answer i'm putting there is b is in boy so you know where i'm getting this stuff guys is you know if you look again usm emily outline yeah i'm just going to try to go through here you know biostats epidemiology population health is kind of my thing uh the survival analysis kaplan-meier curve is right there they're basically telling you they're telling you you better be familiar with this and so if you really just do it that way and um you know if if you if you really just understand that it's a way to estimate survival and you're comparing the two know the median piece and know how to interpret time going up on these i think you'll be just fine okay hope you like the video guys [Music] you

Channel: Randy Neil, MD

Views: 14,816

Rating: 4.9907298 out of 5

Keywords: #usmle, #step1, usmle, kaplan-meier, #kaplan-meier, kaplanmeier, prepare for step 1, biostatistics, biostats

Id: gBUcw1Jmyw0

Length: 19min 19sec (1159 seconds)

Published: Sun Feb 14 2021