Skip to main content

Proportional Hazards Regression Models

  • Reference work entry
Springer Handbook of Engineering Statistics

Part of the book series: Springer Handbooks ((SHB))

Abstract

The proportional hazards model plays an important role in analyzing data with survival outcomes. This chapter provides a summary of different aspects of this very popular model.

The first part gives the definition of the model and shows how to estimate the regression parameters for survival data with or without ties. Hypothesis testing can be built based on these estimates. Formulas to estimate the cumulative hazard function and the survival function are also provided. Modified models for stratified data and data with time-dependent covariates are also discussed.

The second part of the chapter talks about goodness-of-fit and model checking techniques. These include testing for proportionality assumptions, testing for function forms for a particular covariate and testing for overall fitting.

The third part of the chapter extends the model to accommodate more complicated data structures. Several extended models such as models with random effects, nonproportional models, and models for data with multivariate survival outcomes are introduced.

In the last part a real example is given. This serves as an illustration of the implementation of the methods and procedures discussed in this chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 309.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

EM:

expectation maximization

References

  1. D. R. Cox: Regression models, life-tables (with discussion), J. R. Stat. Soc. B 34, 187–220 (1972)

    MATH  Google Scholar 

  2. J. P. Klein, M. L. Moeschberger: Survival Analysis: Techniques for Censored and Truncated Data (Springer, Berlin Heidelberg New York 1997)

    MATH  Google Scholar 

  3. D. R. Cox: Partial likelihood, Biometrika 62, 269–276 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  4. N. E. Breslow: Covariance analysis of censored survival data, Biometrics 30, 89–99 (1974)

    Article  Google Scholar 

  5. B. Efron: The efficiency of Coxʼs likelihood function for censored data, J. Am. Stat. Assoc. 72, 557–565 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  6. N. E. Breslow: Contribution to the discussion of the paper by D. R. Cox, J. R. Stat. Soc. B 34, 187–220 (1972)

    MathSciNet  Google Scholar 

  7. S. Johansen: An extension of Coxʼs regression model, Int. Stat. Rev. 51, 258–262 (1983)

    Google Scholar 

  8. J. D. Kalbfleisch, R. L. Prentice: The Statistical Analysis of Failure Time Data (Wiley, New York 1980)

    MATH  Google Scholar 

  9. Y. Pawitan, S. Self: Modeling disease marker processes in AIDS, J. Am. Stat. Assoc. 83, 719–726 (1993)

    Article  Google Scholar 

  10. U. G. Dafni, A. A. Tsiatis: Evaluating surrogate markers of clinical outcome when measured with error, Biometrics 54, 1445–1462 (1998)

    Article  MATH  Google Scholar 

  11. A. A. Tsiatis, V. Degruttola, M. S. Wulfsohn: Modeling the relationship of survival to longitudinal data measured with error: applications to survival, CD4 counts in patients with AIDS, J. Am. Stat. Assoc. 90, 27–37 (1995)

    Article  MATH  Google Scholar 

  12. C. J. Faucett, D. C. Thomas: Simultaneously modeling censored survival data, repeatedly measured covariates: a Gibbs sampling approach, Stat. Med. 15, 1663–1685 (1996)

    Article  Google Scholar 

  13. M. S. Wulfsohn, A. A. Tsiatis: A joint model for survival, longitudinal data measured with error, Biometrics 53, 330–339 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  14. R. Henderson, P. Diggle, A. Dobson: Joint modelling of longitudinal measurements, event time data, Biostat. 4, 465–480 (2000)

    Article  Google Scholar 

  15. A. A. Tsiatis, M. Davidian: A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error, Biometrika 88, 447–458 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  16. M. C. Wu, R. J. Carroll: Estimation, comparison of changes in the presence of informative right censoring by modeling the censoring process, Biometrics 44, 175–188 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  17. P. D. Allison: Survival Analysis Using the SAS System: A Practical Guide (SAS Institute, Cary 1995)

    Google Scholar 

  18. E. L. Kaplan, P. Meier: Nonparametric estimation from incomplete observations, J. Am. Stat. Assoc. 53, 457–481 (1958)

    Article  MathSciNet  MATH  Google Scholar 

  19. P. K. Andersen: Testing goodness of fit of Coxʼs regression and life model, Biometrics 38, 67–77 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  20. E. A. Arjas: Graphical method for assessing goodness of fit in Coxʼs proportional hazards model, J. Am. Stat. Assoc. 83, 204–212 (1988)

    Article  Google Scholar 

  21. R. Tibshirani, T. Hastie: Local likelihood estimation, J. Am. Stat. Assoc. 82, 559–567 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  22. L. A. Sleeper, D. P. Harrington: Regression splines in the Cox model with application to covariate effects in liver disease, J. Am. Stat. Assoc. 85, 941–949 (1990)

    Article  Google Scholar 

  23. R. Gentleman, J. Crowley: Local full likelihood estimation for the proportional hazards model, Biometrics 47, 1283–1296 (1991)

    Article  MathSciNet  Google Scholar 

  24. J. Fan, I. Gijbels, M. King: Local likelihood, local partial likelihood in hazard regression, Ann. Stat. 25, 1661–1690 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  25. W. Wang: Proportional hazards regression with unknown link function, time-dependent covariates, Stat. Sin. 14, 885–905 (2004)

    MATH  Google Scholar 

  26. D. Schoenfeld: Partial residuals for the proportional hazards regression model, Biometrika 69, 239–241 (1982)

    Article  Google Scholar 

  27. P. M. Grambsch, T. M. Therneau: Proportional hazards tests, diagnostics based on weighted residuals, Biometrika 81, 515–526 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  28. D. J. Sargent: A general framework for random effects survival analysis in the Cox proportional hazards setting, Biometrics 54, 1486–1497 (1998)

    Article  MATH  Google Scholar 

  29. R. L. Prentice, B. J. Williams, A. V. Peterson: On the regression analysis of multivariate failure time data, Biometrika 68, 373–379 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  30. L. J. Wei, D. Y. Lin, L. Weissfeld: Regression analysis of multivariate incomplete failure time data by modeling marginal distribution, J. Am. Stat. Assoc. 84, 1065–1073 (1989)

    Article  MathSciNet  Google Scholar 

  31. C. F. Spiekerman, D. Y. Lin: Marginal regression models for multivariate failure time data, J. Am. Stat. Assoc. 93, 1164–1175 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  32. D. Y. Lin, L. J. Wei, I. Yang, Z. Ying: Semiparametric regression for the mean, rate functions of recurrent events, J. R. Stat. Soc. B 62, 711–730 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  33. P. K. Andersen, R. D. Gill: Coxʼs regression model counting process: a large sample study, Ann. Stat. 10, 1100–1120 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  34. N. Wayne: Accelerated Testing: Statistical Models, Test Plans, And Data Analysis (Wiley, New York 1990)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Wei Wang or Chengcheng Hu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag

About this entry

Cite this entry

Wang, W., Hu, C. (2006). Proportional Hazards Regression Models. In: Pham, H. (eds) Springer Handbook of Engineering Statistics. Springer Handbooks. Springer, London. https://doi.org/10.1007/978-1-84628-288-1_21

Download citation

  • DOI: https://doi.org/10.1007/978-1-84628-288-1_21

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-806-0

  • Online ISBN: 978-1-84628-288-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics