Welcome to Time to Event Data Analysis

My name is Heidi Cyworld, and I will be your instructor for this course. As a statistician at the LMU in Munich, I am excited to share with you the basic methods of analyzing time to event data. Throughout this course, we will keep the mathematics at a minimum and focus on how to do the analysis in our own hands.

By the end of this course, you will be able to compute survival curves and common statistical models for continuous duration times. These methods are all able to deal with right censoring, which means they can handle situations where some data points may not have complete information about the event of interest. Don't worry if you're not familiar with what that means yet; we'll discuss it soon. You will also be able to visualize your results and apply these methods to a variety of events, including death, but also other events such as the time until someone finds a job again after becoming unemployed.

The term "survival analysis" is often used in art, even though our course is actually called Time to Event Data Analysis. We use this term because it's the most commonly used term in the field, and we want to make sure you're familiar with the concept before we dive into the details. In survival analysis, we are interested in duration times, which can be any event or occurrence that has a start time and an end time. For example, let's say we're interested in finding out how long it takes for someone to die from a certain disease. We would look at the time point of death for each individual and visualize the time until death from the start of the study.

But survival analysis isn't just about death; it can be applied to many other events as well. For instance, let's say we're interested in finding out how long it takes for a person to find a new job after they lose their current one. We would look at the time point of reemployment for each individual and visualize the time needed for them to find a new job again. The event of interest can be anything from the delivery of a letter to how long it takes for a cab to pick you up at your house after you call the company.

Throughout this course, we'll explore two data sets: the GB Sg2 data set and another one within the same package. In the first data set, we're interested in finding out how long it takes for breast cancer patients to die from their disease. We have 686 patients with information on treatment, tumor size, age, and other potentially important factors. This data set is available in the th data package and can be loaded using the data function. The unemployed dataset contains information on individuals who have lost their jobs, and we're interested in investigating how long it takes for them to find a full-time position again.

I'd like to share a pro tip with you: if you want to learn more about these datasets, you can use the help function along with the data function. For now, let's move on to our first topic: what survival analysis actually means and how we'll be applying it in this course.

"WEBVTTKind: captionsLanguage: enhi my name is Heidi Cyworld and I will be your instructor for this course I am a statistician at the LMU in Munich in this course you will be learning basic methods to analyze time to event data we will keep the mathematics at a minimum and focus on how to do the analysis in our after finishing this course you will be able to compute survival curves and common statistical models for continuous duration times all methods we discuss are able to deal with right censoring if you don't know what that means yet no worries we will discuss this soon you will also be able to visualize your results we will not only focus on survival times where the event of interest is death but also discuss other events such as the time until someone finds a job again after becoming unemployed fortunately the methods are the same before digging into the statistical methods that can be used for survival analysis let's discuss what survival analysis actually means instead of calling this course survival analysis in our we should actually call it time to event data analysis in our because we will discuss methods where the event does not have to be death the reason why we still call it survival analysis in art is because this is the most commonly used term so what will we actually discuss in the course we will discuss duration times usually we are interested in a certain event for example death and want to know the time until this event happens in the graph here X shows the time point of the event for five individuals oftentimes this event is death and the line visualizes the time until death from the start of a study but let's go through a couple of other examples in one of the data sets we will be working with their starting point 0 is the time a person becomes unemployed the event of interest is reemployment the line shows the time needed for the individual to find a job again the event could also be the delivery of a letter and we are interested in the time needed for a letter to be delivered where how long does it take for a cab to pick you up at your house after having called the cab company as you see the event of interest can be very diverse but we are always interested in the time until this event occurs 2 of the data sets we will be in this course are the GB sg2 data set and the data set in the GB sg2 data set we are interested in the time until death in breast cancer patients we have data of 686 patients including information on the treatment tumor size age and other potentially important factors the data set is available in the th data package and can be loaded with the data function the unempioyed dataset contains information on individuals who have lost their jobs we are interested in investigating how long it takes for them to find a full time position again it is available in the act that package in the upcoming exercises we will take a deeper look into these datasets for this I already have a pro tip for you to see information on the data set exchange the data function with the help function time to look into thehi my name is Heidi Cyworld and I will be your instructor for this course I am a statistician at the LMU in Munich in this course you will be learning basic methods to analyze time to event data we will keep the mathematics at a minimum and focus on how to do the analysis in our after finishing this course you will be able to compute survival curves and common statistical models for continuous duration times all methods we discuss are able to deal with right censoring if you don't know what that means yet no worries we will discuss this soon you will also be able to visualize your results we will not only focus on survival times where the event of interest is death but also discuss other events such as the time until someone finds a job again after becoming unemployed fortunately the methods are the same before digging into the statistical methods that can be used for survival analysis let's discuss what survival analysis actually means instead of calling this course survival analysis in our we should actually call it time to event data analysis in our because we will discuss methods where the event does not have to be death the reason why we still call it survival analysis in art is because this is the most commonly used term so what will we actually discuss in the course we will discuss duration times usually we are interested in a certain event for example death and want to know the time until this event happens in the graph here X shows the time point of the event for five individuals oftentimes this event is death and the line visualizes the time until death from the start of a study but let's go through a couple of other examples in one of the data sets we will be working with their starting point 0 is the time a person becomes unemployed the event of interest is reemployment the line shows the time needed for the individual to find a job again the event could also be the delivery of a letter and we are interested in the time needed for a letter to be delivered where how long does it take for a cab to pick you up at your house after having called the cab company as you see the event of interest can be very diverse but we are always interested in the time until this event occurs 2 of the data sets we will be in this course are the GB sg2 data set and the data set in the GB sg2 data set we are interested in the time until death in breast cancer patients we have data of 686 patients including information on the treatment tumor size age and other potentially important factors the data set is available in the th data package and can be loaded with the data function the unempioyed dataset contains information on individuals who have lost their jobs we are interested in investigating how long it takes for them to find a full time position again it is available in the act that package in the upcoming exercises we will take a deeper look into these datasets for this I already have a pro tip for you to see information on the data set exchange the data function with the help function time to look into the\n"