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Analysis of Multivariate Survival Times with Non-Proportional Hazards Models

  • L. Chen
  • L. J. Wei
Part of the Lecture Notes in Statistics book series (LNS, volume 123)

Abstract

In a clinical trial to evaluate treatments for a chronic disease, a commonly used regression method for analyzing multiple event times is based on a multivariate Cox model (Wei, Lin and Weissfeld, 1989). However, the Cox model may not fit the data well. For univariate survival analysis, a class of linear transformation models (Cheng, Wei and Ying, 1995a) provides many useful semi-parametric alternatives to the Cox model. In this paper, we take a similar approach as Wei et al. (1989) did for the multivariate case by modeling each marginal failure time with a linear transformation model and derive joint inference procedures for the regression parameters. In addition, we show how to check the adequacy of the fitted model graphically. We apply the proposed methods to data from an AIDS clinical trial and a cancer clinical trial for illustration.

Keywords

Failure Time Mycobacterium Avium Complex Recurrence Time Inference Procedure Proportional Odds Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York, Inc. 1997

Authors and Affiliations

  • L. Chen
  • L. J. Wei

There are no affiliations available

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