Survival Analysis Using R – August 2022

Event Phone: 1-610-715-0115

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A 4-Day Livestream Seminar Taught by Paul Allison, Ph.D.

For 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 intractable problems:

  1. 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?
  2. 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.

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 emphasize R, but slides and code will also be provided for SAS and Stata.

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