Survival Analysis Using R - Online Course
A 4-Day Livestream Seminar Taught by
Paul AllisonFor event-time data, ordinary regression analysis won’t do the job.
If you’ve ever used regression analysis on longitudinal event data, you’ve probably come up against two challenging problems:
- Censoring: Nearly every sample contains some cases that do not experience an event. If the dependent variable is the time of the event, what do you do with these “censored” cases?
- Time-dependent covariates: Many explanatory variables (like income or blood pressure) change in value over time. How do you put such variables in a regression analysis?
Makeshift solutions to these questions can lead to severe biases. Survival methods are explicitly designed to deal with censoring and time-dependent covariates in a statistically correct way. Originally developed by biostatisticians, these methods have become popular in sociology, demography, psychology, economics, political science, marketing, and many other fields.
This seminar covers both the theory and practice of statistical methods for event-time data. Participants receive a thorough introduction to such topics as censoring, Kaplan-Meier estimation, Cox regression, discrete-time methods, competing risks, and repeated events.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions.
More details about the course content
How you will benefit from this seminar
Survival Analysis covers both the theory and practice of survival methodology. Assuming no previous knowledge of survival analysis, this course will turn you into a knowledgeable and skilled user of these indispensable techniques. Here are a few of the skills you will acquire:
- How to organize survival data.
- How to choose the right time axis.
- When to use discrete vs. continuous time methods.
- What to do about nonproportionality.
- How to compute R-squared.
- When and how to correct for unobserved heterogeneity.
- How frequently to measure independent variables.
- What to do if there is more than one kind of event.
- How to test for sensitivity to informative censoring.
This is a hands-on course with ample opportunity for participants to practice survival analysis.
The examples and exercises will focus on R, but slides and code will also be provided for SAS and Stata.
How you will benefit from this seminar
Survival Analysis covers both the theory and practice of survival methodology. Assuming no previous knowledge of survival analysis, this course will turn you into a knowledgeable and skilled user of these indispensable techniques. Here are a few of the skills you will acquire:
- How to organize survival data.
- How to choose the right time axis.
- When to use discrete vs. continuous time methods.
- What to do about nonproportionality.
- How to compute R-squared.
- When and how to correct for unobserved heterogeneity.
- How frequently to measure independent variables.
- What to do if there is more than one kind of event.
- How to test for sensitivity to informative censoring.
This is a hands-on course with ample opportunity for participants to practice survival analysis.
The examples and exercises will focus on R, but slides and code will also be provided for SAS and Stata.
Computing
This is a hands-on course. To optimally benefit, you are strongly encouraged to use a computer with a recent version of R and RStudio installed. Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
For those who prefer SAS or Stata, slides and exercises using these packages will be available.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
This is a hands-on course. To optimally benefit, you are strongly encouraged to use a computer with a recent version of R and RStudio installed. Basic familiarity with R is highly desirable, but even novice R coders should be able to follow the presentation and do the exercises.
For those who prefer SAS or Stata, slides and exercises using these packages will be available.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.
Who should register?
If you need to analyze longitudinal event data and have a basic statistical background, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory.
Previous participants have come from a wide variety of fields: sociology, demography, psychology, economics, management, finance, history, marketing, biology, medicine, veterinary medicine and criminal justice.
If you need to analyze longitudinal event data and have a basic statistical background, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory.
Previous participants have come from a wide variety of fields: sociology, demography, psychology, economics, management, finance, history, marketing, biology, medicine, veterinary medicine and criminal justice.
Seminar outline
- Introduction
- Univariate Survival Analysis
- Cox Regression
- Competing Risks
- Discrete Time Analysis
- Miscellaneous Topics
- Repeated Events
- Introduction
- Univariate Survival Analysis
- Cox Regression
- Competing Risks
- Discrete Time Analysis
- Miscellaneous Topics
- Repeated Events
Payment information
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $895 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.