Survival analysis isn’t just a single model.
It’s a whole set of tests, graphs, and models that are all used in slightly different data and study design situations. Choosing the most appropriate model can be challenging.
In this article I will describe the most common types of tests and models in survival analysis, how they differ, and some challenges to learning them.
by Steve Simon, PhD
There are two features of survival models.
First is the process of measuring the time in a sample of people, animals, or machines until a specific event occurs. In fact, many people use the term “time to event analysis” or “event history analysis” instead of “survival analysis” to emphasize the broad range of areas where you can apply these techniques.
Second is the recognition that not everyone/everything in your sample will experience the event. Those not experiencing the event, either because the study ended before they had the event or because they were lost to follow-up, are classified as censored observations.
Every statistical model and hypothesis test has assumptions.
And yes, if you’re going to use a statistical test, you need to check whether those assumptions are reasonable to whatever extent you can.
Some assumptions are easier to check than others. Some are so obviously reasonable that you don’t need to do much to check them most of the time. And some have no good way of being checked directly, so you have to use situational clues.
Even with a few years of experience, interpreting the coefficients of interactions in a regression table can take some time to figure out. Trying to explain these coefficients to a group of non-statistically inclined people is a daunting task.
For example, say you are going to speak to a group of dieticians. They are interested (more…)
Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure.
There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.
We’ve looked at the interaction effect between two categorical variables. Now let’s make things a little more interesting, shall we?
What if our predictors of interest, say, are a categorical and a continuous variable? How do we interpret the interaction between the two? (more…)