Survival Analysis: A Primer

Author: Freedman, David A.¹

Source: The American Statistician, Volume 62, Number 2, May 2008 , pp. 110-119(10)

Publisher: American Statistical Association

Key:

- Free Content

- New Content

- Subscribed Content

- Free Trial Content

Abstract:

In this article, I will discuss life tables and Kaplan-Meier estimators, which are similar to life tables. Then I turn to proportional-hazards models, aka "Cox models." Along the way, I will look at the efficacy of screening for lung cancer, the impact of negative religious feelings on survival, and the efficacy of hormone replacement therapy.

What conclusions should be drawn about statistical practice? Proportional-hazards models are frequently used to analyze data from randomized controlled trials. This is a mistake. Randomization does not justify the models, which are rarely informative. Simpler analytic methods should be used first.

With observational studies, the models would help us disentangle causal relations if the assumptions behind the models could be justified. Justifying those assumptions, however, is fraught with difficulty.

Keywords: COX MODEL; EVENT HISTORY ANALYSIS; KAPLAN-MEIER ESTIMATOR; LIFE TABLES; PROPORTIONAL HAZARDS

Document Type: Research article

DOI: 10.1198/000313008X298439

Affiliations: 1: Professor of Statistics, University of California, Berkeley CA 94720-3860

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$25.00 plus tax