10. Further Time-to-Event Models

  1. Bee Choo Tai1 and
  2. David Machin2

Published Online: 11 OCT 2013

DOI: 10.1002/9781118721957.ch10

Regression Methods for Medical Research

Regression Methods for Medical Research

How to Cite

Tai, B. C. and Machin, D. (2013) Further Time-to-Event Models, in Regression Methods for Medical Research, John Wiley & Sons Ltd, Oxford. doi: 10.1002/9781118721957.ch10

Author Information

  1. 1

    Saw Swee Hock School of Public Health, National University of Singapore and National University Health System; Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore

  2. 2

    Medical Statistics Unit, School of Health and Related Sciences, University of Sheffield; Cancer Studies, Faculty of Medicine, University of Leicester, Leicester, UK

Publication History

  1. Published Online: 11 OCT 2013
  2. Published Print: 29 NOV 2013

ISBN Information

Print ISBN: 9781444331448

Online ISBN: 9781118721957

SEARCH

Keywords:

  • competing risks;
  • parametric models;
  • proportional hazards (PH);
  • time-to-event models;
  • time-varying covariates

Summary

This chapter considers the situation where, rather than having a single outcome which defines the event of interest in a time-to-event study, there are several competing event types defined. Competing in the sense that once any one of these events has occurred within a study participant it may prevent, or at least influence, the times at which the remaining events can occur. The chapter discusses parametric models, which utilize the assumed distribution of the survival times under study, as alternatives to the semi-parametric Cox regression model. In addition there are situations in which covariates, usually first determined at the commencement of a survival-time study, may themselves vary with time. The authors describe how such covariates may be incorporated into the modeling process and how they can help overcome difficulties associated with situations when the assumption of proportional hazards (PH) is violated.