this video is all about survival time analysis we start with the question what a survival time analysis is then we come to the important point what the censoring of data means and finally we discuss the kaplan-meier curve the log-rank test and the cox regression finally i'll show you how to calculate all three methods online using data tab this is an introductory video and there are separate detailed videos for each of the three topics listed below and with that we start right away with the first question what is survival time analysis survival time analysis is a group of statistical methods in which the variable understudy is the time until an event occurs what does time until the occurrence of an event mean in survival time analysis a variable is used that has a start time and an end time when a certain event occurs the time between the start time and the event is considered in the survival time analysis the period of time can be measured for example in days weeks or months an example would be the time between a drug rehabilitation and the relapse of the respective person the start time would be the end of the rehabilitation and the event under consideration would be the relapse for example you might be interested in whether different types of treatments have an impact on the time until a relapse occurs or as the name survival time analysis suggests the classic example the time until death after a disease here the start time is the recognition of the disease and the end time is death in many cases it is of great interest whether a certain drug for example has an influence on the survival time or not of course the event doesn't have to be a negative one for example you could also look at the time to return to work after a burnout and the object under investigation does not have to be a human being in engineering for example a common question is how long a component lasts in a test bench here one could then select various parameters and see if they have an impact on survival time so therefore time doesn't need to have anything to do with survival time but still we speak of survival time and survival time analysis how exactly does a survival time analysis work let's assume you are a dental technician and you want to study the survival time of a filling in a tooth so your start time is the moment when a person gets a feeling at the dentist and the end time is the moment when this feeling breaks out so now you're interested in the time between these two situations now of course you need test persons so that you have data that you can evaluate for each person you can now note the time that passes until the feeling breaks out now the question will surely come up what if the feeling of a test person does not break out or what if the person moves away and changes the dentist so it is simply not known when the feeling will break out all these cases are summarized under the term censoring what does that mean first of all a study cannot last forever there's always a start and an end date because of limited resources and simply because you want to publish the results at some point if a filling is inserted within this period and then the filling breaks out again within this period you have a valid case the event has occurred however it is also possible that a filling is inserted and then the end of the study is reached before the event occurs or it can happen that a test person decides not to continue with the study in both cases it is not known when or if the event under consideration has occurred or not what else can happen is that another event occurs which is not considered in a study for example the patient under consideration could die or lose the entire tooth due to whatever circumstances in both cases the event under consideration that the filling breaks out can no longer occur another possible situation is that the test person does not realize that the feeling has broken out and this is only discovered during the next routine examination so you can see that there are a lot of cases where the data is not complete this data is then called senzod data you can find out how to handle this data in my video on the kaplan-meier curve now let's first look at the most popular methods of survival time analysis here we have the kaplan-meier survival time curves the log rank test and the cox regression we will now briefly go into all three areas and i will then show you how you can easily calculate these methods online with data tab for each of the three methods there's also a detailed separate video and you can find the links in the video description let's start with the kaplan-meier curve the kaplan-meier curve is used to graphically represent the survival rate or the survival function the time is entered on the x-axis and the survival rate on the y-axis so what is the survival rate let's look at the example of the dental filling again here we have collected data on how long it takes for a filling to break out in the kaplan-meier curve you can now see how likely it is that a filling will last longer than a certain point in time for example you might be interested in how likely it is that your feeling will last longer than let's say five years in order to do this simply go to five years on the x-axis and there you can see what the survival rate is at five years the kaplan-meier curves gives you a value of 0.7 so it is 70 percent likely that a filling will last longer than 5 years of course the data are purely fictitious if you are interested in how the kaplan-meier curve is created from existing data please watch my video about that but now you might be interested in whether this curve differs according to different filling materials for example whether one filling material is better than the other one how do you find that out the log rank test helps you with this task the log rank test compares the distribution of the time until an event occurs of two or more independent samples for example you might be interested in whether there is a difference in survival time between two different materials so in half of the participants you use material a and in the other half you use material b for the filling the look rank test now gives you an answer to the question is there a significant difference between the two curves or in other words does the filling material have an influence on the survival time of the dental filling with this knowledge we can now look at the hypotheses in the log rank test in a log rank test the null hypothesis is there is no difference in terms of the distribution of time until the event occurs and the alternative hypothesis is the groups have different distribution curves so as always with a statistical hypothesis test you get a p-value at the end of the log-rank test the question is whether this p-value is greater than the significance level or not the significance level is set to 0.05 in most cases if the calculated p-value is greater than 0.05 the null hypothesis is retained so based on the available data it is assumed that both groups have the same distribution curve if the p-value is smaller than 0.05 the null hypothesis is rejected now you might ask yourself another question how can i test if there are other parameters that influence the curve not only do you want to know if the material has an effect on survival time but also you want to know if the age of the people has an effect for this you can use a cox regression as i just said at the beginning for each of these methods there is also a detailed separate video now i will show you how you can easily calculate all three methods online with data tab just go to datadeb.net you will find the link in the video description then you copy your own data into this table and click on plus and then on survival analysis here we have a column with the time and then a column that tells us whether the event has occurred or not if not the case is censored here 1 stands for occurred and 0 4 censored then we have the variable material with the two materials a and b and we have the age of our test people depending on what you choose here the appropriate methods are calculated for you if you select only the variable time the kaplan-meier survival curve will be calculated and you will get the corresponding survival timetable if no variable is specified with the status it is assumed that no case is censored if this is not the case you can simply click here at status on the variable that contains the data whether the event has occurred or not if you now select a factor for example the material the log rank test will be calculated here you can read the null and the alternative hypothesis the null hypothesis is there is no difference between groups a and b in terms of the distribution of time until the event occurs and now you get the results here below for example get the p-value if you do not know exactly how to interpret the results then you can simply click on summary in words the summary in words says a log rank test was calculated to see if there was a difference between groups a and b in terms of the distribution of time to the event occurrence for the present data the log rank test showed that there is a difference between the groups in terms of the distribution of time until the event occurs p-value smaller than 0.01 the null hypothesis is thus rejected if you click on the material and the age the cox regression is calculated and here below you can see if the factors have a significant influence or not more about this in my video on cox regression now we continue with the detailed video on the kaplan-meier curve i hope you enjoyed the video and see you next time