Skip to main content

Intermediate Clinical Events, Surrogate Markers and Survival

  • Chapter
Lifetime Data: Models in Reliability and Survival Analysis
  • 747 Accesses

Abstract

This paper investigates one- and two-sample problems comparing survival times when an individual may experience an intermediate event prior to death or reaching some well defined endpoint. The intermediate event may be polychotomous. Patients experiencing the intermediate event may have an altered survival distribution after the intermediate event. Score tests are derived for testing if the occurrence of the intermediate event actually alters survival. These models have implications for evaluating therapies without randomization as well as strengthening the log rank test for comparing two survival distributions. The exact distribution of the score tests can be found by conditioning on both the waiting time and occurrence of the intermediate event.

Deceased

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Agresti, A. (1990) Categorical Data Analysis. New York: Wiley.

    MATH  Google Scholar 

  • Aitken M, Laird N, Francis B. (1983) A reanalysis of the Stanford heart transplant data. J Am Stat Assoc 78: 264–274.

    Article  Google Scholar 

  • Andersen PK. (1986) Time dependent covariates and Markov processes. In Moolgavkar S, Prentice RL (eds.) Modern Statistical Methods in Chronic Disease Epidemiology. New York: Wiley, pp. 82–103.

    Google Scholar 

  • Andersen PK, Gill RD. (1982) Cox’s regression model for counting processes: A large sample study. Amer Stat 12: 730–736.

    Google Scholar 

  • Anderson JR, Cain KC, Gelber RD. (1983) Analysis of survival by tumor response. J Clin Oncol 1: 710–719.

    Google Scholar 

  • Crowley J, Hu M. (1977) Covariance analysis of heart transplant survival data. J Am Stat Assoc 72: 27–35.

    Article  Google Scholar 

  • Gray, Robert J. (1994) A kernel method for incorporating information on disease progression in the analysis of survival. Biometrika 81: 527–539.

    Article  MathSciNet  MATH  Google Scholar 

  • Hsieh FY, Crowley J, Tormey DC. (1983) Some test statistics for use in multistate survival analysis. Biometrika 70: 111–119.

    Article  MathSciNet  MATH  Google Scholar 

  • Mantel N, Byar DP. (1974) Evaluation of responsetime data involving transient states: An illustration using hearttransplant data. J Am Stat Assoc 69: 81–86.

    Article  MATH  Google Scholar 

  • Turnbull BW, Brown BW, Hu M. (1974) Survivorship analysis of heart transplant data. J Am Stat Assoc 69: 74–80.

    Article  Google Scholar 

  • Weiss GH, Zelen M. (1963) A stochastic model for the interpretation of clinical trials. Proc Natl Acad Sci 50: 988–994.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Lefkopoulou, M., Zelen, M. (1996). Intermediate Clinical Events, Surrogate Markers and Survival. In: Jewell, N.P., Kimber, A.C., Lee, ML.T., Whitmore, G.A. (eds) Lifetime Data: Models in Reliability and Survival Analysis. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5654-8_26

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-5654-8_26

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4753-6

  • Online ISBN: 978-1-4757-5654-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics