R Tutorial : Why do we need special methods for time-to-event data?

Key Takeaways

in this lesson we will discuss why we need special methods for survival analysis why can't they just compute a linear model the first important point of why we need particular methods for survival analysis is the fact that duration times are always positive so we need to work with distributions that can handle positive outcomes the linear model for example assumes a normal distribution which is not very appropriate for positive outcomes a common distribution to model duration times is the viable distribution and the corresponding model is called the viable model which we will discuss later in this course historically in survival analysis the survival function has been a measure of interest we will learn about the survival function in the next lesson there are some other measures that are more interest in survival analysis than in other areas like for example the hazard function the last point why we need special methods for survival analysis is probably the most important censoring in the example shown here we know that for individual 1 the event happened at time point 5 of individual 2 we only know that the event did not happen until the time point 6 but we have no knowledge about what happened after that let's think about the cab example again each day you call a cab and want to analyze how long it takes them to arrive at your house the cab on day 1 arrives at your house after 5 minutes cap 2 doesn't arrive until time point 6 and you get annoyed and decide to walk instead that leads to you never observing when the cab actually arrives on day 3 the cab does not arrive in the first two minutes but then you fall asleep and never observe what happened the caps on days four and five arrive after four minutes type of censoring shown here is called right censoring and the most common type of censoring and survival analysis they exist two other types of censoring left and interval censoring which we will not cover in this course when working with right sensor time to event data we need to specify this appropriately NR in our example we have times five six two four and for the event indicator is one if the event happened and zero otherwise this means the two censored individuals two and three have a value of zero with the R package survival we can specify that the variables time and event belong together we do this using a serve object created with the serve function we will also call this a survival object in one of the upcoming exercises we will take a look at the serve object more deeply and see what it looks like in the GBS g2 dataset but speaking of our packages I haven't told you about the air packages we will be using in this course yet aside from the packages which store the datasets we will focus on two packages during this course most importantly we will use the survival package it provides all functionality for basic survival analysis and is a very why widely used R package the survival package allows the user not only to do survival analysis but also to visualize the results additional to the plotting features in the survival package we will be using the serve minor package for more advanced visualizations we will focus on interpreting visualizations in this course since we will skip the mathematically more advanced interpretation of the model effect estimates now let's