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Response to discussants of “survival models and health sequences”

Author

Listed:
  • Walter Dempsey

    (Harvard University)

  • Peter McCullagh

    (University of Chicago)

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.

Suggested Citation

  • Walter Dempsey & Peter McCullagh, 2018. "Response to discussants of “survival models and health sequences”," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 605-611, October.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:4:d:10.1007_s10985-018-9447-2
    DOI: 10.1007/s10985-018-9447-2
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    Cited by:

    1. Kwun Chuen Gary Chan, 2018. "Commentary: Alignment of time scales and joint models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 601-604, October.

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