Skip to main content
Log in

Response to discussants of “survival models and health sequences”

  • Published:
Lifetime Data Analysis Aims and scope Submit manuscript

Abstract

Survival studies often generate not only a survival time for each patient but also a sequence of health measurements at annual or semi-annual check-ups while the patient remains alive. Such a sequence of random length accompanied by a survival time is called a survival process. Robust health is ordinarily associated with longer survival, so the two parts of a survival process cannot be assumed independent. This paper is concerned with a general technique—reverse alignment—for constructing statistical models for survival processes, here termed revival models. A revival model is a regression model in the sense that it incorporates covariate and treatment effects into both the distribution of survival times and the joint distribution of health outcomes. The revival model also determines a conditional survival distribution given the observed history, which describes how the subsequent survival distribution is determined by the observed progression of health outcomes.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Dawid AP (2000) Causal inference without counterfactuals. J Am Stat Assoc 95:407–424

    Article  MathSciNet  MATH  Google Scholar 

  • Liang K-Y, Zeger SL (2000) Longitudinal data analysis of continuous and discrete responses for pre-post designs. Sankhya 62:134–148

    MathSciNet  MATH  Google Scholar 

  • Nelder JA (1965a) The analysis of randomized experiments with orthogonal block structure. I. Block structure and the null analysis of variance. Proc R Soc. A 283:147–162

    Article  MathSciNet  MATH  Google Scholar 

  • Nelder JA (1965b) The analysis of randomized experiments with orthogonal block structure. II. Treatment structure and the general analysis of variance. Proc R Soc A 283:163–178

    Article  MathSciNet  MATH  Google Scholar 

  • Samuels ML (1986) Use of analysis of covariance in clinical trials: a clarification. Control Clin Trials 7:325–329

    Article  Google Scholar 

  • Senn S (2006) Change from baseline and analysis of covariance revisited. Stat Med 25:4334–4344

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

We thank the discussants for their wide-ranging and thoughtful comments. The topics covered are many and varied, so it is not feasible to respond in detail to every point. We focus on the most controversial points and the points raised most frequently. Our responses are arranged by topic.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Walter Dempsey.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dempsey, W., McCullagh, P. Response to discussants of “survival models and health sequences”. Lifetime Data Anal 24, 605–611 (2018). https://doi.org/10.1007/s10985-018-9447-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10985-018-9447-2

Keywords

Navigation